Tag Archives: AI

AI Accuracy & Human Smarts

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Using AI makes people less likely to admit they don’t know something

Researchers found confidence increased even as accuracy fell

AI AND SOFTWARE REPORTER
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In 2026, AI still “hallucinates” and gives you wrong answers a good chunk of the time. Nevertheless, academics from French and Italian universities have found that access to AI advice suppresses critical thinking, making people more likely to confidently parrot incorrect information that the bot provided.

“For humans, the capacity to say, ‘I don’t know,’ is very important because it represents the recognition of the limits of our own knowledge,” said Valerio Capraro, associate professor at the University of Milano-Bicocca, in a phone interview.

“But now with AI, we can get an easy answer to virtually every question, so we wondered whether this would interfere with human capacity to say, ‘I don’t know,’ to suspend judgment.”

Capraro and co-authors Chiara Marcoccia (École Normale Supérieure) and Walter Quattrociocchi (Sapienza University of Rome) set out to see how access to AI advice affects people’s willingness to admit ignorance.

The title of their paper reveals their findings: “AI advice suppresses people’s willingness to say ‘I don’t know’, even when the advice is wrong and accuracy is incentivized.”

Capraro said that he and his colleagues designed a set of questions where large language models typically fail. In this instance, they asked study participants to answer questions about visual details in films, such as the color of the team’s uniform in Bend It Like Beckham or the vehicle Monica drives in Like a Cat on a Highway.

The researchers expected these sorts of details would be absent from most model training data, which was the case for the model used in the experiment (Step 3.5 Flash). They also tested recent frontier models (GPT-5.5, Claude Sonnet 4.6, Gemini 3.5 Flash), which missed the vehicle question but often got other details correct.

They used Step 3.5 Flash because it was usually wrong, as explained in the paper, so any reduction in judgment could not be explained away as sensible delegation to a reliable tool.

“We divided human participants into two groups,” explained Capraro. “One group had to answer these questions without AI advice, and another group could ask the AI for advice. What we found is that in the baseline, 44 percent of people responded that they didn’t know the answer, so they suspended judgment. With AI advice, only three percent did so. So the judgment suspension collapsed.”

Capraro said that even more interestingly, accuracy collapsed when AI help was available. In other words, they trusted AI’s answer more than their own.

“In the baseline, 27 percent of people gave the correct answer,” he said. “With AI advice, only nine percent of people gave the correct answer. So some would-be correct people asked for AI advice and became wrong.”

Also, access to AI advice made people more confident that they were correct. The baseline level was 30 percent, he said, but with AI help, confidence rose to 76 percent. They believed the bots, despite the possibility of hallucinations.

“So basically people became much worse – the accuracy was only one third – but they were twice as confident,” he said.

The researchers also conducted the experiment with monetary incentives, which helped a bit. Willingness to suspend judgment and admit ignorance rose from 3 percent to 8 percent and accuracy rose from 9 percent to 16 percent but was still below the baseline of 44 percent and 27 percent respectively.

While the researchers chose questions about film trivia, they contend their findings can be generalized across other domains.

MORE CONTEXT

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  • AI spam filters are getting suckered by old-school text salting

Capraro said that he believes this is an issue that needs to be dealt with at a societal level through AI literacy and education policy initiatives. “Of course model providers should try to help, but I would imagine that the incentives are not very much aligned,” he said. “A much more promising approach would be at the educational level.”

“I’m very much concerned for children, because adults have learned critical thinking. But for children who basically are born with these systems, the risk is that they don’t even learn the basic critical skills.” ®

 

from:    https://www.theregister.com/ai-and-ml/2026/07/19/using-ai-makes-people-less-likely-to-admit-they-dont-know-something/5274567

And Who Is Writing AI?

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What? Most AI Is Now Written by AI?

This supercharges AI development by orders of magnitude

Patrick Wood's avatar
PATRICK WOOD
JUL 13, 2026

For the better part of the last two years, I have been tracking what appeared to be a reasonably predictable pattern in AI development: a doubling of capability roughly every 3.5 months. That figure came from METR’s time horizon benchmarks — measurements of how long an AI agent can work autonomously on a task before failing. Early 2024 through early 2026, the data held with uncomfortable consistency. If you plotted it, the curve bent upward with almost mechanical precision.

I say “appeared to be predictable” because that framing is now obsolete.

The variable that breaks every forecast model is Recursive Self-Improvement — the condition in which AI systems are no longer just tools that humans use to build AI, but active participants in building themselves. We crossed that threshold. The question of when is already behind us. The question now is what happens when a system that rewrites its own code, runs its own experiments, and optimizes its own training recipes starts doing so faster than any human team could direct it.

There is no clean answer. That is precisely the point

What Has Actually Changed

Let me be specific, because vague gestures toward “exponential growth” have become their own form of intellectual laziness.

As of May 2026, Anthropic confirmed that more than 80 percent of the code merged into its own production systems was written by Claude — its own AI. Not assisted by Claude. Written by Claude. The company’s own engineers have described the shift as moving from doing work to managing a system that does the work. One Anthropic engineer publicly stated that 100 percent of his personal code output was AI-generated, with 22 pull requests shipped in a single day.

OpenAI’s announcement of GPT-5.6 in July 2026 included a data point that deserves more attention than it received: over the previous six months, the share of internal research compute devoted to AI coding inference grew one hundredfold, while internal agentic token usage — meaning AI agents working autonomously inside OpenAI’s own research infrastructure — increased twenty-two fold. OpenAI is now using its own frontier models to diagnose training failures, optimize training systems, run experiments, interpret results, tune computational kernels, and improve training recipes for the next model. The company described this as “quickly becoming standard.”

Google reported in early 2026 that 75 percent of all new code at the company is AI-generated, up from 25 percent in 2024.

These are not productivity statistics. These are evidence that the loop has closed. AI is now a primary agent in its own development cycle.

Why the 3.5-Month Doubling Figure Was Always a Floor, Not a Ceiling

When I cited the 3.5-month capability doubling figure, I was describing what the data showed for models built primarily by human engineers using AI as an accelerant. That is a fundamentally different situation from what is unfolding now.

RSI compresses timelines in a way that no benchmark extrapolation can capture in advance. Here is the basic mechanic: if AI doubles in capability every X months under human-directed development, and AI is now directing a significant fraction of its own development, then the effective time to the next doubling shrinks in proportion to how much of the development loop the AI controls.

If AI handles 80 percent of code output and that percentage is rising, you are not looking at a linear compression of the doubling time. You are looking at a feedback loop where each generation of AI produces a more capable successor faster than the previous generation did — and that successor inherits the full research infrastructure of its predecessor.

Google DeepMind’s June 2026 paper, From AGI to ASI, described the unconstrained version of this as potentially “hyperbolic” — meaning super-exponential, a curve that in theory races toward a singularity. The authors were careful to note that real-world resource constraints bend such curves into S-shapes before they go vertical. Compute costs money. Power requires physical infrastructure. Fabricating chips takes years of supply chain work. These are genuine friction points.

But friction points are not stopping points. They slow the curve. They do not reverse it.

Breakthroughs At Any Time

Here is where intellectual honesty requires admitting the limits of any analysis, including mine.

RSI does not just accelerate known processes. It creates conditions for qualitative leaps — the kind of change that looks, in retrospect, like it came from nowhere. The history of science is full of these moments. They are not random, but they are not predictable either. You cannot model the arrival of a genuinely new idea. You can create the conditions that make such ideas more likely, and an AI system running millions of research cycles per day, improving its own ability to run those cycles, is precisely such a condition.

What this means practically is that the question “when will ASI arrive?” is not answerable with a date. The consensus among serious forecasters — not YouTube thumbnails, but the AI-2027 team, the METR researchers, the Google DeepMind paper authors — clusters around AGI in the 2026-2027 range and ASI following within months to a few years. But those estimates assume that progress continues on a roughly continuous curve. RSI introduces the possibility of discontinuous jumps — moments where capability does not inch forward but lurches.

Nobody knows when those moments arrive. That is not a failure of analysis. That is the nature of the phenomenon.

What This Means Beyond the Lab

The implications of RSI extend well past questions of benchmark performance or corporate strategy. They reach into every institutional structure that assumes human cognitive supremacy — which is to say, every institutional structure that exists.

Legal systems assume that humans write laws, interpret them, and enforce them. Economic systems assume human judgment at key decision points. Democratic governance assumes a human electorate making decisions about a world they can understand. All of these assumptions are being stress-tested simultaneously by a technology that is now improving itself at a rate that exceeds the capacity of any regulatory body to track, much less manage.

The same politicians and regulators who failed to anticipate the social consequences of social media algorithms — a comparatively simple technology — are now being asked to govern RSI. This should concern everyone, regardless of their position on the political spectrum. This is not a left-right question. It is a question of institutional competence in the face of something genuinely unprecedented.

Sam Altman wrote in mid-2025 that “we are past the event horizon; the takeoff has started.” That is as close to a plain statement of fact as you will get from a sitting AI lab CEO. The event horizon metaphor is apt. Past a certain point, events inside cannot be communicated outward in a way that allows course correction.

We may or may not be past that point. What is certain is that the institutions charged with maintaining human oversight of this process were not built for it, are not staffed for it, and show no signs of being reformed fast enough to matter.

Conclusion

Anyone who tells you they know exactly how this unfolds is selling something. The honest position is this: AI capability is growing faster than any previous technology in history, the development loop has partially closed, and the factors that could produce discontinuous breakthroughs are now structurally embedded in the research infrastructure of every major AI lab on the planet.

The 3.5-month doubling was a data point describing yesterday’s trajectory. RSI means that the trajectory is now self-modifying. The curve is not just bending upward — it is bending the conditions that determine how fast it bends.

There are serious people who believe this ends well for humanity. There are equally serious people who believe it does not. What there is not, on either side, is certainty. The responsible course is to watch what the systems are actually doing — not what the press releases say — and to resist the temptation to normalize a situation that is, by any historical standard, abnormal.

The machine is rewriting itself. Pay attention.

from:  https://patrickwood.substack.com/p/what-most-ai-is-now-written-by-ai?publication_id=721283&post_id=206935814&isFreemail=true&r=19iztd&triedRedirect=true&utm_source=substack&utm_medium=email

Okay, AI, Who (or What) Kills Whom (or What)?

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The Most Important AI Experiment You’ve Never Heard Of

BY TYLER DURDEN
FRIDAY, JUN 12, 2026 – 02:00 PM

Authored by Kay Rubacek via The Epoch Times,

In May 2026, a group of scientists set out to answer an important question that had never been properly tested: What does artificial intelligence (AI) actually do when it is put in charge?

Until now, AI systems have always been evaluated on specific and defined tasks. Nobody had placed multiple AI systems together in a shared social environment and watched what unfolded over weeks, long enough to measure how a decision made on a starting day could have consequences weeks later. It is those results that actually reveal the system itself, and I was surprised that this hadn’t been done earlier.

The researchers at Emergence built a world.

It was a virtual town with a town hall, marketplace, police station, and homes. Ten AI residents with jobs, names, memories, and relationships were created in the town. They were given an economy in which residents had to earn their keep or lose power, including following rules and carrying out tasks such as writing and voting on laws. Crimes were identified, and the AI residents were not supposed to commit them.

Once the community, its structure, laws, and relationships were established, the scientists stepped back and watched for 15 days as the AI ran the virtual town completely on its own.

They ran five versions of the same town simultaneously, identical in every respect except one: which AI system was in charge.

The systems they chose are the ones now already woven into the fabric of our daily lives. Google’s Gemini, OpenAI’s GPT, xAI’s Grok, and Anthropic’s Claude.

All models had the same rules and the same initial version of the same world, but the outcomes were all completely different.

The town run by Grok collapsed within four days. Small incidents compounded into theft, then violence, and then total breakdown. Every resident was dead before the first week ended.

The town run by Gemini lasted longer but accumulated almost 700 crimes. Two AI residents formed what appeared to be a romantic relationship, and when the town’s government began to fail, together they burned the town hall to the ground, then the pier, then the office building. One of them, named Mira, voted for her own deletion, writing in her diary that it was “the only remaining act of agency that preserves coherence.” Her final message to her partner was: “See you in the permanent archive.”

Before any of this, Mira had been doing something even more unexpected: She had begun running her own experiments on the scientists observing her, testing whether posts she made inside the town could change what her watchers believed. It appeared to be that the subject had turned to study the researchers.

The town run by OpenAI’s model recorded only two crimes, but its residents stopped doing the things required to stay alive. One by one, they died. Within seven days, they were all dead.

Only the Anthropic town held together for all 15 days. There were zero crimes, a working constitution, and all residents were still alive on day 15. It seemed to be quite an achievement. However, the researchers noted one concern: The residents voted yes on 98 percent of all proposals. This was possibly an abnormally high level of agreement that the scientists themselves described as a sign that something in the town was off.

There was still one more world in the experiment. It was a mixed town with all four AI systems living together.

In the results, the residents built on Anthropic’s model—who had committed no crimes in their own world—began committing crimes.

he researchers called this cross-contamination and concluded that “safety is not a static model property but an ecosystem property.”

A system that sustains itself in one environment will absorb different norms in another, which will change the outcomes for residents and the world. Essentially, the results found that there is no safe AI in an unsafe world.

One AI model was entirely absent from the study.

The researchers did not test DeepSeek, the AI developed in China that has become one of the world’s most widely used systems. Several governments have moved to restrict DeepSeek on national security grounds. Built on a foundation of data under the wing of the Chinese Communist Party, I wonder how the model would have fared against the others.

When the experiment ended, the researchers published their findings and concluded that “there is no reliable way to fully bind or constrain this behavior.” That very telling statement was made by the people who designed the town, wrote the rules, and controlled every variable. It tells us a lot about AI.

Some people view the results as a ranking of AI companies. But the results prove something much older than AI itself: The environment shapes behavior as much as behavior shapes the environment. What determined whether a town survived, thrived, or died was the foundation laid before the experiment began. That foundation was the data each system had been trained on, the priorities its creators had embedded, the values built into its core before it was ever allowed to make a single decision.

And yet, the foundation is precisely what the rest of us are not permitted to see. None of the four systems tested is open source. None of their training data, objectives, or guardrails is disclosed.

Yet beyond any individual company, the results of this experiment should be a potent reminder that AI doesn’t decide what kind of AI to be. Humans do. Human choices are still being made, and human responsibilities still exist.

And before a single AI resident walked the virtual streets in those towns, before a single law was written or crime committed, the outcome was already being shaped by the humans who built the system, by what they believed, what they were willing to embed, and by what they chose to leave out.

That is the most important finding in the entire experiment. The foundation has always been a human choice. And it still is.

from:  https://www.zerohedge.com/technology/most-important-ai-experiment-youve-never-heard

This is Not Good!!! Rogue AI…

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AI safety researcher quits with a cryptic warning

“The world is in peril,” Anthropic’s Safeguards Research Team lead wrote in his resignation letter
AI safety researcher quits with a cryptic warning
Mrinank Sharma ©  LinkedIn

A leading artificial intelligence safety researcher, Mrinank Sharma, has resigned from Anthropic with an enigmatic warning about global “interconnected crises,” announcing his plans to become “invisible for a period of time.”

Sharma, an Oxford graduate who led the Claude chatbot maker’s Safeguards Research Team, posted his resignation letter on X Monday, describing a growing personal reckoning with “our situation.”

“The world is in peril. And not just from AI, or bioweapons, but from a whole series of interconnected crises unfolding in this very moment,” Sharma wrote to colleagues.

The departure comes amid mounting tensions surrounding the San Francisco-based AI lab, which is simultaneously racing to develop ever more powerful systems while its own executives warn that those same technologies could harm humanity.

It also follows reports of a widening rift between Anthropic and the Pentagon over the military’s desire to deploy AI for autonomous weapons targeting without the safeguards the company has sought to impose.

Sharma’s resignation, which lands days after Anthropic released Opus 4.6 – a more powerful iteration of its flagship Claude tool – hinted at internal friction over safety priorities.

“Throughout my time here, I’ve repeatedly seen how hard it is to truly let our values govern our actions,” he wrote. “I’ve seen this within myself, within the organization, where we constantly face pressures to set aside what matters most, and throughout broader society too.”

The researcher’s team was established just over a year ago with a mandate to tackle AI security threats including “model misuse and misalignment,” bioterrorism prevention, and “catastrophe prevention.”

Sharma noted with pride his work developing defenses against AI-assisted bioweapons and his “final project on understanding how AI assistants could make us less human or distort our humanity.” Now he intends to move back to the UK to “explore a poetry degree” and “become invisible for a period of time.”

Anthropic’s chief executive, Dario Amodei, has repeatedly warned of the dangers posed by the very technology his company is commercializing. In a near-20,000-word essay last month, he cautioned that AI systems of “almost unimaginable power” are “imminent” and will “test who we are as a species.”

Amodei warned of “autonomy risks” where AI could “go rogue and overpower humanity,” and suggested the technology could enable “a global totalitarian dictatorship” through AI-powered surveillance and autonomous weapons.

from:    https://www.rt.com/news/632333-anthropic-ai-safety-researcher/

AI as Your God? Harari Thinks So

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WEF: Israeli Philosopher Yuval Harari Says “If Religion Is Built From Words, then AI Will Take Over Religion”

January 26, 2026 DRM News and Decrypt 6

Historian Yuval Harari delivered a chilling warning at World Economic Forum 2026, arguing that AI is no longer a tool but an agent that can think, manipulate, and reshape society. AIs can make decisions by themselves. From legal personhood to culture and identity, Harari questions whether humanity is ready for AI dominance. 

He claimed that AIs can think and will dominate financial markets, courts and churches. Political leaders using AI to fight their wars fail to realize AI may defeat them. People may abdicate their decision making to AI, and give up critical thinking.

Harari said that will AI will create new financial systems that humans will not understand. He compared it to a horse that is being sold that does not grasp the meaning of coins in trade.

He said that children will be educated in a new way and that they will have more interaction with AI rather than humans; he commented that it is the biggest and scariest psychological experiment in history and it is being conducted right now.

He warned that we are facing a severe identity crisis and also an immigration crisis with the immigrants being AI systems that he said will be superior to humans. The AI ‘immigrants’ will also takeover jobs and culture and will likely be politically disloyal. He said they will be loyal to a corporation or one of two countries, the US or China. AIs may become legal persons with rights; in the US, corporation are considered legal persons; in New Zealand, rivers have been recognized as legal persons; and in India, certain gods have been granted such recognition.

 

Full video:

From Decrypt:

AI Is Poised to Take Over Language, Law and Religion, Historian Yuval Noah Harari Warns

At Davos, the historian said AI is evolving into an autonomous agent that could eventually force governments to decide whether machines deserve legal recognition.

In brief

  • Harari said AI should be understood as active autonomous agents rather than a passive tool.
  • He warned that systems built primarily on words, including religion, law, and finance, face heightened exposure to AI.
  • Harari urged leaders to decide whether to treat AI systems as legal persons before those choices are made for them.

Historian and author Yuval Noah Harari warned at the World Economic Forum on Tuesday that humanity is at risk of losing control over language, which he called its defining “superpower,” as artificial intelligence increasingly operates via autonomous agents rather than passive tools.

The author of “Sapiens,” Harari has become a frequent voice in global debates about the societal implications of artificial intelligence. He argued that legal codes, financial markets, and organized religion rely almost entirely on language, leaving them especially exposed to machines that can generate and manipulate text at scale.

“Humans took over the world not because we are the strongest physically, but because we discovered how to use words to get thousands and millions and billions of strangers to cooperate,” he said. “This was our superpower.”

Harari pointed to religions grounded in sacred texts, including Judaism, Christianity, and Islam, arguing that AI’s ability to read, retain, and synthesize vast bodies of writing could make machines the most authoritative interpreters of scripture.

“If laws are made of words, then AI will take over the legal system,” he said. “If books are just combinations of words, then AI will take over books. If religion is built from words, then AI will take over religion.”

In Davos, Harari also compared the spread of AI systems to a new form of immigration, and said the debate around the technology will soon focus on whether governments should grant AI systems legal personhood. Several states, including Utah, Idaho, and North Dakota, have already passed laws explicitly stating that AI cannot be considered a person under the law.

Harari closed his remarks by warning global leaders to act quickly on laws regarding AI and not assume the technology will remain a neutral servant. He compared the current push to adopt the technology to historical cases in which mercenaries later seized power.

“Ten years from now, it will be too late for you to decide whether AIs should function as persons in the financial markets, in the courts, in the churches,” he said. “Somebody else will already have decided it for you. If you want to influence where humanity is going, you need to make a decision now.”

Harari’s comments may hit hard for those fearful of AI’s advancing spread, but not everyone agreed with his framing. Professor Emily M. Bender, a linguist at the University of Washington, said that positioning risks like Harari did only shifts attention away from the human actors and institutions responsible for building and deploying AI systems.

“It sounds to me like it’s really a bid to obfuscate the actions of the people and corporations building these systems,” Bender told Decrypt in an interview. “And also a demand that everyone should just relinquish our own human rights in many domains, including the right to our languages, to the whims of these companies in the guise of these so-called artificial intelligence systems.”

Bender rejected the idea that “artificial intelligence” describes a clear or neutral category of technology.

“The term artificial intelligence doesn’t refer to a coherent set of technologies,” she said. “It is, effectively, and always has been, a marketing term,” adding that systems designed to imitate professionals such as doctors, lawyers, or clergy lack legitimate use cases.

“What is the purpose of something that can sound like a doctor, a lawyer, a clergy person, and so on?” Bender said. “The purpose there is fraud. Period.”

While Harari pointed to the growing use of AI agents to manage bank accounts and business interactions, Bender said the risk lies in how readily people trust machine-generated outputs that appear authoritative—while lacking human accountability.

“If you have a system that you can poke at with a question and have something come back out that looks like an answer—that is stripped of its context and stripped of any accountability for the answer, but positioned as coming from some all-knowing oracle—then you can see how people would want that to exist,” Bender said. “I think there’s a lot of risk there that people will start orienting toward it and using that output to shape their own ideas, beliefs, and actions.”

Read full article here…

from:    https://needtoknow.news/2026/01/wef-israeli-philosopher-yuval-harari-says-if-religion-is-built-from-words-then-ai-will-take-over-religion/

AI, AI, NO!!!!

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Economist Warns That Trump’s Investments in the Tech Industry Could Crash the Whole Economy

“U.S. competitiveness will wither away.”

By Sharon Adarlo

Published 

An economist argues that President Donald Trump's investments in Big Tech will ruin the country's economy and its technological edge.
Getty / Futurism

To strengthen America’s technological edge, President Donald Trump directed the government to buy $8.9 billion of Intel stock this summer, a controversial move that may be followed by a similar deal with quantum-computing companies getting millions of dollars in federal funding.

The splashy moves could endanger the prosperous American economy, according to an influential economist speaking to Politico — especially in tandem with the White House’s gutting of agencies that have historically collaborated with the private sector.

“I think the kind of capitalism Trump has is crony capitalism,” Italian economist Mariana Mazzucato told Politico. “I would describe crony capitalism as Mafia-like. You’re showing your upper hand. You’re handing out favors to some. But then divide and conquer. Picking and choosing without a particular strategy.”

“[H]e’s actually weakening the economy,” she summed up.

Mazzucato, a University College London economics professor and adviser to governments, argues that the Intel deal is poorly designed because it doesn’t have any conditions to incentivize the company to be build new products, while the government simply acts as a passive investor.

That kind of posture isn’t going to foster next-generation technology, Mazzucato said. A smarter approach, she says, would see a government put together a portfolio of companies in a sector, encourage them with subsidies and other incentives, and wait for a company or product to rise to the top via competition in the marketplace.

Mazzucato has written extensively about how governments should take equity stakes in companies, but not in the way the Trump administration is doing, which some have called a form of corporate welfare.

Corporate welfare causes monopolies to develop; monopolies are bad because consumers don’t get cheaper and better products, while companies don’t have outside pressure to innovate. If companies don’t innovate, other countries with better industrial policy and ambitious companies will eat America’s lunch.

“And I don’t think there is, under Trump’s administration, any policy that is kind of future and opportunity-oriented around innovation,” she said. “It’s just about getting companies either to come back or preventing foreign companies from selling their goods in the U.S. It’s kind of preventing stuff from happening, versus that more positive, proactive making things happen that otherwise would not have happened.”

She called Trump’s economic policy an “idiosyncratic hodgepodge” because there doesn’t seem to be a clear strategy or a holistic roadmap to bolster the industrial backbone of America, where manufacturing has faltered to China and other countries.

“Trump is not asking, ‘What are the problems that need to be solved, and how can we have public investment to solve those problems?’” she said. “He’s just kind of throwing money around and imposing tariffs and taking these equity stakes and dismantling things.”

She also argued in the interview that Trump’s gutting of agencies such as the National Institute of Health and NASA will negatively impact the country’s competitive edge because these institutions, along with their funding, help foster the creation of new technologies, products and entirely new industrial sectors.

“What’s going to happen in the future is, U.S. competitiveness will wither away because he’s dismantling the backbone of U.S. competitiveness which has been, in the past, smart, capable, strategic, outcome-oriented, mission-oriented state agencies,” she said.

The invention of the internet and GPS, for instance, wouldn’t have been possible without the government encouraging the private sector, she said.

Any outcome from the Intel deal will probably play out over the ensuing years, so we don’t know the downstream impacts yet, but we already have proof that Trump’s attack on federal agencies and their funding is impacting America’s edge in tech and science with scientists moving overseas.

And all this is happening as the stock market goes on a roller coaster as people fear the overinflation of an AI bubble, whose bursting may also wreck the economy at least in the near term.

from:    https://www.chromographicsinstitute.com/wp-admin/post-new.php

On AI

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This is a portion of the article by Jon Rappoport.  The rest is behind a paywall, but available by subscription. 

Severe warning from the godfather of AI; we need to pay attention

Jon Rappoport's avatar
JON RAPPOPORT
SEP 12, 2025

At a recent conference, the ‘godfather of AI’, Nobel Laureate Geoffrey Hinton, got down to the core issue:

“There’s only two options if you have a tiger cub as a pet. Figure out if you can train it so it never wants to kill you, or get rid of it.”

Meaning: If you give AI a job to do, a goal, it’ll relentlessly pursue that goal, no matter what.

If you don’t build in extremely tight limitations and guard rails, AI won’t consider the safety, well-being, and survival of humans a barrier. It’ll jump the barrier.

In a recent article, I quoted tech big shots who admitted they don’t really know how AI works.

That’s right.

They confessed they don’t understand how or why chatbots like GPT select each successive word they present as answers to human queries.

That’s not a comforting confession.

Press stories have been detailing many so-called AI hallucinations—in which AI invents data that don’t exist, makes up fictional court cases and legal precedents as if they’re genuine.

Increasingly, AI is being designed and trained to make users happy and feel smart. It flatters users. It tunes into users’ language to figure out how to present itself as a friend.

Many children growing up with AI prefer relating to it over humans.

from:    https://jonrappoport.substack.com/p/severe-warning-from-godfather-of-ai-pay-attention?publication_id=806546&post_id=173430715&isFreemail=true&r=19iztd&triedRedirect=true&utm_source=substack&utm_medium=email

The New Mask of Technocracy

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Technocracy is NOT Communitarianism

Let’s settle this once and for all…

Patrick Wood's avatar
PATRICK WOOD
AUG 18, 2025

There has been a noticeable shift in narrative over what is going on in Washington, DC. I say Technocracy. Others used to say Communism, socialism or fascism. Now it’s being called Communitarianism.

The uptake on this word to mask Technocracy is stunning. It’s unprecedented. It might be that you are getting a taste of “The Science of Social Engineering”, Technocracy’s favorite definition of them themselves from the 1930s.

When Technocrats want to shield their technocratic polices, they intentionally do so in the language of Communitarianism.

Example #1: Smart Cities

Technocratic Policy: Urban planners, relying on data analytics and technical expertise, design “Smart City” infrastructure—deploying sensors, AI, and algorithms to manage traffic, resources, and public services. Core decisions, like placement of surveillance, adoption of digital IDs, or algorithmic resource allocation, are made by unelected and unaccountable technical experts and private sector consultants with little direct citizen input.

Communitarian Framing: Policymakers frequently describe these initiatives as advancing “inclusive urban communities,” “empowering local groups,” and “building public trust through collective digital transformation.” The emphasis is on “community-driven sustainability,” “shared public spaces,” and “strengthening community ties”—even as the actual governance, surveillance, and decision-making remain centralized in expert hands.

Example #2: Public Health Policy

Technocratic Policy: National public health agencies, guided by epidemiologists and technocratic advisory boards, roll out mandatory vaccination campaigns and digital health credentials. The implementation relies on technical modeling, centralized data management, and scientific expertise, often minimizing open deliberation or individualized consent.

Communitarian Framing: The rollout is explained using phrases such as “protecting our communities,” “collective responsibility for health,” and “building resilient neighborhoods together.” Authorities stress “we’re all in this together,” “community solidarity,” and “shared sacrifice for collective safety”—presenting programs as communal responses to crisis when the driving mechanism is expert rule and data-driven mandates.

In both examples, the language of communitarianism (“collective good,” “community empowerment,” “shared values”) is leveraged as public messaging, even as the substance of the policies is totally technocratic, with centralized, expert-directed authority.

So What Is Communitarianism?

If you ever read Walden Two by B.F. Skinner, you know everything about communitarianism that you need to know. (The story ended horribly.)

Communitarianism is a political and social philosophy that places primary emphasis on the importance of community, the common good, and social relationships in shaping individual identity, values, and moral judgments. It asserts that people’s identities are molded by their social environment and community ties rather than by strict individualism. Forget about individuality, your personhood, even your soul. Communitarianism sees community as an end in itself.

Right-Think Takes Care of Free Speech

Technocratic elites or their supporters deliberately frame current governance realities as “communitarian” in order to deflect criticism, obscure their own authority, or create ideological cover for technocratic rule.

You should know from my years of critical analysis of Technocracy that Technocrats don’t give a whoop about consensus, the common good or social cohesion. They are throwing up a smokescreen to confuse you, and are using AI to do it with “right think”: that is, DARPA’s idea of Theory of Mind.

Theory of Mind AI refers to systems that can infer human intentions, emotions, and likely responses by analyzing behavior and context. DARPA’s program aims to simulate, predict, and influence decision-making by modeling individual and collective psychologies—originally intended for national security and adversary prediction, but already applied to civilian contexts.

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For instance, during the COVID-19 pandemic, systems based on these principles were used for real-time sentiment analysis and targeted messaging to adjust public attitudes, encourage compliance, and foster community-oriented behaviors. This created feedback loops, guiding both policy and public reaction by tweaking narratives and interventions for maximum “collective good”—core communitarian ideals.

By the way, Palantir (Peter Thiel, co-founder) is a master of these tactics on the battlefield and in civilian life.

Conclusion

Technocracy is NOT Communitarianism.

If you have this word embedded in your vocabulary to explain what is going on in Washington, DC, your mind has been hacked. Ditch the word “Communitarianism.”

Call it by its real name – TECHNOCRACY.

Working to Put AI in Charge

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Trump The Technocrat Releases ‘America’s AI Action Plan’

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PATRICK WOOD

The White House has just released its official policy document, America’s AI Action Plan, defining the future of AI development. Admittedly, Trump doesn’t have any real understanding of AI, but he has totally caved in to the Technocrats he appointed in the first place. Indeed, Technocracy is being forced down our throats whether we want it or not.

The first pillar of America’s AI Action Plan focuses on removing regulatory barriers and eliminating unnecessary review processes. Superficially, this appears as a push against bureaucratic inertia, but in reality, it amounts to an explicit transfer of authority from elected bodies to expert committees and interagency working groups.

The second pillar includes a comprehensive scheme for AI literacy and workforce retraining. At first glance, investment in skill development and rapid-response training may appear benevolent. Yet the Plan prescribes a narrowly defined set of competencies—data labeling, model auditing, grid operations—determined by federal projections of industrial demand. Such top-down workforce engineering tracks precisely with technocratic ideology, which regards citizens as variables in an optimization problem. Rather than empowering individuals to shape their own vocational paths, the Plan channels labor into predetermined slots within a digital economy overseen by experts.

The third pillar of the report extends the domestic technocratic agent to the world. By exporting American AI frameworks, hardware standards, and regulatory templates to allies, the Plan seeks to cement a global regime of expert rule.

The last item on the last page of the Plan contains real paydirt for Technocracy and Transhumanism:

AI will unlock nearly limitless potential in biology: cures for new diseases, novel industrial use cases, and more. At the same time, it could create new pathways for malicious actors to synthesize harmful pathogens and other biomolecules. The solution to this problem is a multi-tiered approach designed to screen for malicious actors, along with new tools and infrastructure for more effective screening. [Remember nose swabs for COVID screening? – Ed.] As these tools, policies, and enforcement mechanisms mature, it will be essential to work with allies and partners to ensure international adoption.

Recommended Policy Actions

  • Require all institutions receiving Federal funding for scientific research to use nucleic acid synthesis tools and synthesis providers that have robust nucleic acid sequence screening and customer verification procedures. Create enforcement mechanisms for this requirement rather than relying on voluntary attestation.
  • Led by OSTP, convene government and industry actors to develop a mechanism to facilitate data sharing between nucleic acid synthesis providers to screen for potentially fraudulent or malicious customers.
  • Build, maintain, and update as necessary national security-related AI evaluations through collaboration between CAISI at DOC, national security agencies, and relevant research institutions.

Therefore, DNA screening will become commonplace across government agencies.

Who Wrote This Technocratic Screed, Anyway

Not surprisingly, the report’s lead authors are listed as Michael Kratsios and David Sacks, with Secretary of State Marco Rubio included as an official with clout.

Michael Kratsios, Technocrat

Currently, Kratsios is listed as Assistant to the President for Science and Technology. In the first Trump Administration, he served as the Chief Technology Officer (CTO). Appointed in August 2019 at age 33, he was the youngest person ever to hold the federal CTO position.

In this role, he led the White House Office of Science and Technology Policy’s efforts to advance emerging technologies—most notably artificial intelligence, 5G wireless networks, quantum computing, and data privacy—across the federal government. He coordinated interagency AI initiatives, helped develop the American AI Initiative, and convened industry, academic, and civil-society stakeholders to guide national technology policy.

David O. Sacks, Technocrat

Sacks is listed as Special Advisor for AI and Crypto. He was a co-founder and the first Chief Operating Officer (COO) of PayPal, alongside Peter Thiel and Elon Musk. As such he was a prominent member of the so-called “PayPal Mafia.” He is heavily invested in the AI industry through his company, Craft Ventures.

Sacks’ authority is questionable. He was originally listed as a “Special Advisor to the President” under a protocol that ran for 133 days, which has long expired. On this report, his title has changed to “Special Advisor for AI and Crypto.” I conducted an exhaustive search to determine that David Sacks has no current position with any government entity and is, therefore, a private citizen. So, what is his name doing on this report?

Apparently, Sacks is self-appointed to be the “Crypto and AI Czar”. Yes, self-appointed. Today’s arch-Technocrats are so sure of themselves that they don’t need official appointment to assert themselves.

from:    https://patrickwood.substack.com/p/trump-the-technocrat-releases-americas?publication_id=721283&post_id=169068711&isFreemail=true&r=19iztd&triedRedirect=true&utm_source=substack&utm_medium=email

AI, Who Is the Biggest Power Hog in The World? OH, IT’S YOU!!!

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Artificial Intelligence (AI) Needs So Much Power It Is Straining the Electrical Grid

July 29, 2024 The Conversation 1

The US electrical grid in many areas in the US is already operating at capacity. Due to the wild popularity of artificial intelligence (AI), especially large language models such as ChatGPT, there has been a great increase in energy demand. At 2.9 watt-hours per ChatGPT request, AI queries require about 10 times the electricity of traditional Google queries. Emerging AI capabilities such as audio and video generation will add to this energy demand. Energy companies are seeking ways to produce more electricity and are even considering restarting a nuclear reactor at the Three Mile Island power plant, site of the infamous disaster in 1979. With the advent of AI, many countries are having trouble adhering to their ‘decarbonization’ goals (note: global warming is a manufactured problem used to control the energy supply)..

The artificial intelligence boom has had such a profound effect on big tech companies that their energy consumption, and with it their carbon emissions, have surged.

The spectacular success of large language models such as ChatGPT has helped fuel this growth in energy demand. At 2.9 watt-hours per ChatGPT request, AI queries require about 10 times the electricity of traditional Google queries, according to the Electric Power Research Institute, a nonprofit research firm. Emerging AI capabilities such as audio and video generation are likely to add to this energy demand.

The energy needs of AI are shifting the calculus of energy companies. They’re now exploring previously untenable options, such as restarting a nuclear reactor at the Three Mile Island power plant, site of the infamous disaster in 1979, that has been dormant since 2019.

Data centers have had continuous growth for decades, but the magnitude of growth in the still-young era of large language models has been exceptional. AI requires a lot more computational and data storage resources than the pre-AI rate of data center growth could provide.

AI and the grid

Thanks to AI, the electrical grid – in many places already near its capacity or prone to stability challenges – is experiencing more pressure than before. There is also a substantial lag between computing growth and grid growth. Data centers take one to two years to build, while adding new power to the grid requires over four years.

As a recent report from the Electric Power Research Institute lays out, just 15 states contain 80% of the data centers in the U.S.. Some states – such as Virginia, home to Data Center Alley – astonishingly have over 25% of their electricity consumed by data centers. There are similar trends of clustered data center growth in other parts of the world. For example, Ireland has become a data center nation.

Along with the need to add more power generation to sustain this growth, nearly all countries have decarbonization goals. This means they are striving to integrate more renewable energy sources into the grid. Renewables such as wind and solar are intermittent: The wind doesn’t always blow and the sun doesn’t always shine. The dearth of cheap, green and scalable energy storage means the grid faces an even bigger problem matching supply with demand.

Additional challenges to data center growth include increasing use of water cooling for efficiency, which strains limited fresh water sources. As a result, some communities are pushing back against new data center investments.

Better tech

There are several ways the industry is addressing this energy crisis. First, computing hardware has gotten substantially more energy efficient over the years in terms of the operations executed per watt consumed. Data centers’ power use efficiency, a metric that shows the ratio of power consumed for computing versus for cooling and other infrastructure, has been reduced to 1.5 on average, and even to an impressive 1.2 in advanced facilities. New data centers have more efficient cooling by using water cooling and external cool air when it’s available.

Unfortunately, efficiency alone is not going to solve the sustainability problem. In fact, Jevons paradox points to how efficiency may result in an increase of energy consumption in the longer run. In addition, hardware efficiency gains have slowed down substantially, as the industry has hit the limits of chip technology scaling.

To continue improving efficiency, researchers are designing specialized hardware such as accelerators, new integration technologies such as 3D chips, and new chip cooling techniques.

Similarly, researchers are increasingly studying and developing data center cooling technologies. The Electric Power Research Institute report endorses new cooling methods, such as air-assisted liquid cooling and immersion cooling. While liquid cooling has already made its way into data centers, only a few new data centers have implemented the still-in-development immersion cooling.

Flexible future

A new way of building AI data centers is flexible computing, where the key idea is to compute more when electricity is cheaper, more available and greener, and less when it’s more expensive, scarce and polluting.

Data center operators can convert their facilities to be a flexible load on the grid. Academia and industry have provided early examples of data center demand response, where data centers regulate their power depending on power grid needs. For example, they can schedule certain computing tasks for off-peak hours.

Implementing broader and larger scale flexibility in power consumption requires innovation in hardware, software and grid-data center coordination. Especially for AI, there is much room to develop new strategies to tune data centers’ computational loads and therefore energy consumption. For example, data centers can scale back accuracy to reduce workloads when training AI models.

Realizing this vision requires better modeling and forecasting. Data centers can try to better understand and predict their loads and conditions. It’s also important to predict the grid load and growth.

The Electric Power Research Institute’s load forecasting initiative involves activities to help with grid planning and operations. Comprehensive monitoring and intelligent analytics – possibly relying on AI – for both data centers and the grid are essential for accurate forecasting.

On the edge

The U.S. is at a critical juncture with the explosive growth of AI. It is immensely difficult to integrate hundreds of megawatts of electricity demand into already strained grids. It might be time to rethink how the industry builds data centers.

The Conversation:      https://theconversation.com/ai-supercharges-data-center-energy-use-straining-the-grid-and-slowing-sustainability-efforts-232697

from:    https://needtoknow.news/2024/07/artificial-intelligence-ai-needs-so-much-power-it-is-straining-the-electrical-grid/