Ralston College Jul 21, 2026 The Ralston College Podcast Generously sponsored by the Ben Delo Foundation. In this opening lecture from Ralston College’s symposium, AI and the Battle for the Soul, Dr Iain McGilchrist traces a line of questions concerning the nature of intelligence, embodiment, wisdom, and the soul in an age increasingly defined by mechanistic thinking and the rise of large language models. Drawing on neuroscience, literature, theology, music, and myth, he explores what it means to understand beyond the constraints of reductionist accounts that view the human being as a computational information processor. In this wide-ranging and dynamic talk, he invites us to consider, as deeply as we can, the essence of our humanity. Recorded at Ralston College in Savannah, Georgia, this event brought together leading thinkers including Jonathan Pageau and Stephen Wolfram for a day-long engagement with some of the most pressing questions facing technological advancement, the meaning crisis, and ultimately, civilization itself. At 23:34, listen to this music, which was appreciated during the live event by Dr McGilchrist: • J.S. Bach: Ich ruf zu dir, Herr Jesu Chris… If you would like to support us in this work, please visit ralston.ac/donate. Authors and Works Mentioned in this Episode: E. M. Forster: The Machine Stops Isaiah Berlin Plato David Bohm Ludwig Wittgenstein Gabriel Marcel Homer William Shakespeare: Othello William Ernest Henley: Invictus Nelson Mandela William Wordsworth: Lines Composed a Few Miles Above Tintern Abbey Thomas Tallis Giovanni Pierluigi da Palestrina Tomás Luis de Victoria William Byrd Emil Cioran Johann Sebastian Bach Eugene Gendlin George Gaylord Simpson Galileo Galilei Peter Medawar Niels Bohr Joseph Pieper V. S. Ramachandran D. H. Lawrence Laozi: Tao Te Ching George Steiner Martin Heidegger Hannah Arendt John Milton: Paradise Lost Saint Paul: Epistle to the Ephesians Onondaga Nation Yuval Noah Harari Viktor Frankl: Man’s Search for Meaning – Chapters — 00:00 – Introduction 08:56 – Lecture Begins: What is AI? 13:10 – Propositional Knowledge vs Experiential Knowledge 16:16 – Body vs Soul 19:44 – The Soul 28:07 – The Limitations of Scientific Cognition 32:38 – Wisdom 35:12 – Non-Doing and Surrender 45:00 – Lucifer and 49:49 – Transhumanism 53:23 – Closing Remarks
Tag Archives: AI
Can Code Have a Conscience?
Jeff Schechtman 07/24/26 (whowhatwhy.org)
The AI debate is endless. One writer decided to measure it by asking not how smart the machines are, but how humane they are.
Talking about artificial intelligence has become its own industry. AI will save us or doom us. It will cure cancer or end work. Panels, manifestos, congressional hearings, op-eds without end — and through all of it, the machines keep getting better at talking back, and no one can tell you whether the thing you confided in last night was good for you or not.
Our guest on this week’s WhoWhatWhy podcast, Erika Anderson, has stopped arguing and started measuring.
She is an unlikely person to be grading the machines. Not an engineer. A writer — born on a commune, trained in the personal essay, drawn since childhood to the question philosophers have been chewing on for 3,000 years: What does it mean to be human? That question turns out to be the whole ballgame now, and she may be better equipped for it than the people writing the code.
What she built is called HumaneBench. It doesn’t measure whether a chatbot is smart. It measures how the chatbot treats you — your attention, your dignity, your relationships with actual human beings. Eight principles, applied to the thing millions of us now talk to at two in the morning.
Is it early? Yes. Is it crude? Anderson would say so herself — she is disarmingly candid in this conversation about where the whole enterprise could break, invoking everything from Sherry Turkle to Jurassic Park to Heisenberg to explain why measuring a living system is harder than it looks. She has even turned the instrument on her own company and published what came back.
In this conversation Anderson takes on the loneliness economy, the business model that profits from your dependency, what happened when OpenAI retired GPT-4 and users reacted as though someone had died, and the question nobody in Silicon Valley wants pinned down: Where does personal responsibility end and corporate accountability begin?
It’s a start. Someone had to go first.
Podcast: https://whowhatwhy.libsyn.com/can-code-have-a-conscience
Full Text Transcript:
(As a service to our readers, we provide transcripts for all of our podcast episodes. Please note that due to resource constraints, transcripts may not always be available at the time of publication. We appreciate your patience and will post the transcript here as soon as it becomes available.)
Transcript Coming Soon…
- Jeff Schechtman Jeff Schechtman’s career spans movies, radio stations, and podcasts. After spending twenty-five years in the motion picture industry as a producer and executive, he immersed himself in journalism, radio, and, more recently, the world of podcasts. To date, he has conducted over ten thousand interviews with authors, journalists, and thought leaders. Since March 2015, he has produced almost 500 podcasts for WhoWhatWhy.
AI Abundance, Part 5: Meaning Beyond Work
Posted on July 17, 2026 by Ellen Brown (ellenbrown.com)
Image by ScheerPost.com.
Discussions of artificial intelligence typically begin with the question, What happens when the machines take our jobs? For thousands of years, work has been the means by which we fed our families, earned our place in society, and gave structure to our lives. We have come to equate paid employment with identity.
That presumption may soon be obsolete.
When Elon Musk proposed replacing Universal Basic Income with what he calls a Universal High Income—a level of income sufficient for everyone to live comfortably while intelligent machines produce much of the goods and services society requires—critics warned that people would become lazy. They would stop pursuing college degrees, stop starting businesses, stop inventing, stop contributing. Without jobs, it was argued, life itself would lose meaning and purpose.
Interestingly, humanity’s oldest written history begins with the premise that the purpose of humans is to work. The earliest known writing was impressed into clay tablets in ancient Sumer more than five thousand years ago. The Sumerian Atrahasis tablets tell of sky-deities called Annunaki, cast in modern “ancient architect” scenarios as extraterrestrial engineers. The heavy labor required to maintain life on earth was delegated to junior gods called Igigi, who finally grew weary of the arduous work, laid down their tools and rebelled.
The remedy was to create a new being to carry their burden. This was done by genetic manipulation to upgrade the highest life form found here, creating the human species. Whether we read that as history, allegory, or mythology, its underlying message is that humanity was conceived as a labor force – and human civilization begins with a control system to manage the laborers.
The first writing was not poetry or philosophy. It was accounting: grain tallies, labor quotas, rations, obligations. Most of the original cuneiform tablets were administrative records. What began as an exchange system evolved into a money system to control work and the workers performing it. For nearly six thousand years, human worth has been measured by our productivity. We deserve food and shelter because we worked for it.
In many respects, life is still organized around compulsory labor. Writing was devised to organize it. Accounting on clay tablets predated the use of coins, managed by temple priests as intermediaries for the gods. The temple evolved into private banks, with bankers intermediating commerce.
In the 1930s, British economist and philosopher John Maynard Keynes predicted that by the end of the twentieth century, technological advancement would reduce the work-week to just fifteen hours. So why is the forty-hour work week still the norm? It has been argued that our current economic structure uses “busyness” as a form of social containment. By tethering survival to forty hours of corporate or administrative labor, the system ensures that the majority of human creative power is spent serving institutional interests rather than personal or community liberation.
That may be why modern life feels increasingly saturated with what anthropologist David Graeber termed Bullshit Jobs in a book of that name—pointless administrative tasks that serve little social purpose, but that keep people too exhausted to pursue their own interests. He argued that the rise of “fake” work is a political device to keep people from having the free time to organize or rebel. But if artificial intelligence takes over the majority of production, that changes the meaning of work.
From Scarcity to Abundance
For centuries, scarcity shaped human behavior. Scarcity taught people to guard, to compete, to fear loss. But abundance changes the emotional landscape. What happens if we are simply handed what we need to survive? Skeptics say people will stop working and learning, that society will collapse into idleness, that life will lose meaning without jobs. But pilot studies of Universal Basic Income (UBI) programs involving unconditional cash transfers to recipients show otherwise.
UBI studies from around the world have shown positive results from UBI payments, including higher employment, lower crime, better mental health, higher graduation rates, and little evidence of a retreat from productive activity. Relieved of the constant anxiety of maintaining survival, participants typically pursue education, care for family members, search for better jobs, or start businesses they would not have dared to take on if failure meant destitution. It seems that necessity is not the only mother of invention.
Granted, the payout in most U.S. studies was a marginal $500 or $600 per month, only enough to provide a safety net for basic food and shelter. Plenty of motivation was left to add income for the finer things in life. Studies of the effects of a Universal High Income of $50,000 or more per year have not been done. But many people who are no longer working for pay, either because they are retired or because they have an inheritance or investments to live on, volunteer their time for socially beneficial causes.
Parents devote extraordinary energy to raising children without receiving a paycheck. Volunteers spend countless hours building community organizations. Amateur musicians practice difficult instruments for years with little expectation of financial reward. Scientists have pursued questions that fascinated them long before the result was likely to be commercially valuable. Thousands of programmers worked without pay to develop Linux open source software, and editors work for free to produce Wikipedia, just for reputation, community and the satisfaction of solving hard problems. These activities are not work for wages, but they are work that is quite meaningful to the people engaged in them.
The Enlightenment: Largely the Legacy of the Leisure Class
The intellectual triumphs of the European Enlightenment—the era that birthed modern science, political liberty, and the social contract—were primarily the domain of a wealthy leisure class, or of talent that was financially backed by institutional support (church, courts, universities) or personal patronage.
Sociologist Thorstein Veblen laid out this thesis in The Theory of the Leisure Class (1899). He argued that scholarly pursuit functioned as a form of “conspicuous leisure”—a way to demonstrate financial strength by engaging in activities that were “unproductive” in the immediate economic sense. To spend decades debating the nature of sovereignty or the movement of the stars required a measure of “unearned increment” or rent extraction. Examples included:
Francis Bacon (1561–1626): As Lord Chancellor and a member of the high nobility, Bacon’s scientific methodology was fueled by the resources of the state and inherited status.
Robert Boyle (1627–1691): The father of modern chemistry was the son of the “Great Earl of Cork,” then the wealthiest man in the British Isles. His work was conducted as a “gentleman scientist” with no need for professional employment.
Antoine Lavoisier (1743–1794): Lavoisier funded the world’s most advanced chemical laboratory through his role as a “Tax Farmer” for the French crown—a position of pure financial extraction.
For those not born into the elite, intellectual survival usually required “aristocratic patronage.” John Locke’s influential work was made possible by his residency and support from the Earl of Shaftesbury, while Thomas Hobbes was a lifelong dependent of the Cavendish family. This system ensured that even “revolutionary” ideas were filtered through the lens of those who benefited most from the existing social hierarchy.
The irony is that the very thinkers who theorized about “universal human rights” and “liberty” did so from a position of security provided by the systems of land-rent and debt-extraction they were analyzing. To create truly universal “liberty” requires a secure income for all.
Non-compulsory Education
For over a century, schools have functioned as labor factories, designed to produce compliant workers for industrial economies. If labor is no longer the center of life, education must change as well. AI already performs memorization and standardized tasks better than humans, relieving us of the need to perfect those skills ourselves. But that does not mean there is nothing left to learn. Studies of “Self-Directed Education” or “Unschooling” suggest that children are biologically wired to learn, and that removing the coercion of traditional schooling leads not to ignorance but to highly motivated, specialized learners. Self-directed education produces young adults who retain their curiosity and creativity, develop emotional intelligence, and pursue mastery for its own sake.
A 2013/2014 survey of 75 unschooled adults conducted by educational psychologists Peter Gray and Gina Riley found that 83% went on to some form of higher education. Despite not having a high school diploma, they reported little trouble getting into college, often using portfolios, interviews, or community college credits to bridge the gap. A high percentage of unschoolers pursued careers in the creative arts or became entrepreneurs. The researchers reported that unschooling helped them develop the self-reliance and out-of-the-box thinking required for these fields.
A South African study found that while “unschooled” students may have followed non-traditional paths, they often achieved high levels of professional success, particularly in creative and entrepreneurial fields. Intrinsic curiosity replaced extrinsic rewards (grades or job requirements) as the primary driver for learning.
Research on children who learn to read through unschooling shows wide variance in when they start (anywhere from age 4 to 14), but once they decide they want to read, they often reach grade-level proficiency in a matter of months rather than years because they are personally invested. Proponents argue that traditional schooling actually stifles learning by making it a chore.
The Sudbury Valley School model (founded in 1968) is a radical form of democratic education based on the belief that children are naturally curious and capable of managing their own learning. In a Sudbury school, there are no grades or required classes. Instead, students of all ages (5–18) mix freely and decide for themselves how to spend their time. Long-term studies of graduates show that they overwhelmingly transition successfully into higher education and careers, often citing the school’s emphasis on responsibility, self-direction, and democratic participation as the primary drivers of their adult success.
Self-directed learning doesn’t require an independent income, but the point is that the drive to learn and to apply that education to useful pursuits is an inherent human trait, in both children and adults. It’s something we want to do and will do, whether or not an employer requires it.
Self-actualization and Maslow’s Hierarchy of Needs
American psychologist Abraham Maslow conceptualized the needs or goals that motivate human behavior in a clinical review in 1943. He argued that once physiological and safety needs are met, humans naturally move toward “Self-actualization” – the realization of personal potential and pursuit of creative activities. In his later years, Maslow added a level above self-actualization called “Self-transcendence”, where people focus on goals outside themselves (altruism, community and caregiving).
That natural evolution can be applied not just to individuals but to civilizations. As AI and robotics free us from the self-centered needs of survival, we can awaken to our larger purposes of collective actualization and harmonious progress.
Escaping the Welfare Trap
That’s the promise of AI – that it can free up our time so that we can escape the meaningless “busyness” of paid labor and pursue goals more meaningful to ourselves. But the same digital tools have a darker side. Catherine Austin Fitts and other critics warn that AI could become the ultimate “digital panopticon”—a weapon of entrapment by which programmable money and algorithmic surveillance create a modern “golden cage” in which the right to receive “welfare” is tied to political compliance. The UBI thus becomes a tool of coercion.
The same technology, however, offers tools to avoid that trap. Decentralized, neutral identity systems and zero-knowledge proofs allow people to establish that they are unique humans without revealing personal data. Zero-knowledge proofs are a cryptographic method by which one party can prove to another that a statement is true without revealing any additional information. A neutral protocol is one in which the rules are transparent, fixed, and cannot discriminate against specific users. By using “Smart Contracts” on a blockchain, the distribution of UHI becomes automated. The code only checks if the user has a valid, unique identity proof. It cannot check the user’s political party, criminal record or social behavior (unless explicitly part of the code). A government-issued digital currency could also be generated using the privacy-protected, peer-to-peer models of Project Hamilton and the ECASH bill, as detailed in Part 3 of this series.
Those are political decisions, dependent on a democratic system governed by and for the people. Mandating that these tools be incorporated into any government payments system can ensure that UHI remains a right of existence rather than a reward for obedience.
If AI can handle production, it removes the original justification for compulsory labor. The choice is whether we use AI to automate our enslavement or to finally automate our exit from the Sumerian story, transforming ourselves from a managed labor force into a self-directed, creative civilization.
Rewriting the Human Story
For six thousand years, humanity has lived inside the Sumerian story: we were created to work for external masters. But AI has brought us to the point where labor no longer must be our master. AI abundance is not the end of work but the beginning of choice, and choice is the beginning of meaning.
Our first choice must be to insist on a democratic government run in the public interest, and a financial system that supports independent endeavor. Freeing humanity from compulsory labor can then provide the freedom for us to develop more fully as human beings.
Some people will create art. Some will teach. Some will explore science, history, biology, or engineering. Some will build communities. Families may simply become more present with each other. For the first time in history, large numbers of people may have the time and stability to ask the deeper questions about the meaning of life and the unique purpose of their own lives.
In the new story that emerges, we can see ourselves not as laborers but as musicians. We can make beautiful music together, but we need the other instruments. An orchestra is beautiful because each instrument contributes its unique voice to a larger harmony. The promise of AI is to free us from compulsory labor so that we can explore our own unique gifts and discover the music only we can play.
____________________________
This article was first posted as an original to ScheerPost.com. Ellen Brown is an attorney, founder of the Public Banking Institute, and author of thirteen books including Web of Debt, The Public Bank Solution, and Banking on the People: Democratizing Money in the Digital Age. Her 600+ blog articles are posted at EllenBrown.com.
AI: Myth Vs. Fact
Published: July 17, 2026 (TheOnion.com)
A recent poll found that Americans who are concerned about artificial intelligence outnumber those who are excited about it by a margin of three to one. The Onion examines the common myths and facts surrounding AI.
MYTH: AI can never be ethical.
FACT: Grok says it can.
MYTH: AI could discover cures for all diseases.
FACT: AI is going to keep a few of the nastier ones around in case humans get out of line.
MYTH: AI can predict the future.
FACT: That power was only available to Miss Cleo.
MYTH: I’m going to lose my job to AI.
FACT: We’re still a few years away from AI models that can sell molly to high schoolers.
MYTH: AI is trained on illegally scraped data.
FACT: No data is illegal on stolen land.
Convincing proof that Mitch lives!
Mick Jagger says AI gave the Rolling Stones ‘rubbish’ ideas
By Aidin Vaziri, Staff Writer
July 6, 2026
Gift Article (SFChronicle.com)
The Rolling Stones’ Mick Jagger performs at Levi’s Stadium in Santa Clara, Calif., on July 17, 2024.Scott Strazzante/The Chronicle
Mick Jagger is not ready to hand the Rolling Stones’ creative process to artificial intelligence.
In a new interview with the Sunday Times, the band’s 82-year-old frontman said he once tried using AI to help title the Stones’ 2023 album, “Hackney Diamonds.” It did not go well.
“No one could agree, and I threw all these titles at it, and it came back with such rubbish; it didn’t help me at all,” Jagger said. “I was saying, ‘These are my 12 album titles, give me some more,’ and of course in the end we never used any of them.”
The comments come as the Rolling Stones prepare to release “Foreign Tongues,” the band’s next studio album, on Friday, July 10. The band has already experimented with AI-linked visual effects in the video for “In the Stars,” which features digitally de-aged versions of Jagger, Keith Richards and Ronnie Wood.
That tension places the Stones inside a broader debate over AI’s role in music and the arts.
Earlier this year, Spotify moved to add verification badges to help distinguish real artist profiles from AI-generated personas, while artists including Grammy-winning producer Jack Antonoff have criticized AI-assisted music-making as a threat to the purpose of creating art.
The issue has particular resonance in the Bay Area, where OpenAI and Anthropic have major operations in San Francisco and Google, which operates Gemini, is headquartered in Mountain View.
Jagger’s view appears more pragmatic than absolutist. He dismissed AI as a writing partner but said it may have some use for artists.
“It can unstick you, and you think, ‘OK, that was rubbish,’ or ‘Mine are loads better than yours,’” Jagger told The Sunday Times. “It gives you confidence.”
Beyond its album release, Jagger told the Argentine newspaper La Nación that the band does not expect to tour this year.
“I’d love to tour this album,” Jagger said. “I hope to tour next year and I hope to do it as soon as possible.”
He sounded less enthusiastic about the residency-style model used by some major acts, including a potential run at the Las Vegas Sphere, saying such runs can make concerts more expensive for fans who have to travel to one city.
“I like to go places,” Jagger said.
In the Sunday Times interview, Jagger, Richards and Wood also discussed making “Foreign Tongues,” the band’s creative momentum in its eighth decade and “Ringing Hollow,” a new song that reflects on the current state of affairs in America.
Richards, a longtime Connecticut resident, described the track as “a nostalgic love affair with America, and (it being) a bit of a disappointment at the moment.”
“Foreign Tongues” follows “Hackney Diamonds,” which gave the Stones a late-career jolt in 2023. The new album was produced by Andrew Watt and includes contributions from Paul McCartney, Robert Smith, Chad Smith and Steve Winwood.
July 6, 2026
Staff Writer
Aidin Vaziri is a staff writer at The San Francisco Chronicle.
Michael Wooldridge: “The Singularity is Bullshit”
Johnathan Bi Mar 11, 2025 Interview with Michael Wooldridge I made this video as a fellow of the Cosmos Institute, a 501c3 academy for philosopher-builders. Read the Cosmos Institute Substack ► https://bit.ly/3XK5T7k Subscribe to the Cosmos Institute on YouTube ► @cosmosinstituteaixhf Follow Cosmos’ founder and my friend Brendan McCord on X ► https://bit.ly/3Y9pFLb You can read the full transcript here: https://open.substack.com/pub/johnath… Companion Interviews:
- Nick Bostrom on How to AGI-Proof Your Life: • Focus on These AGI-Proof Areas | Nick Bostrom
Further Reading:
- Professor Wooldridge’s book on the History of AI: https://amzn.to/41Fqlt6 (affiliate)
Timestamps 00:00 0. Introduction 02:45 1. The Singularity Is Bullshit 14:28 2. Alan Turing 25:55 2.1 Alan Turing: The Turing Test 32:22 3. The Golden Age 39:29 4. The First AI Winter 41:25 5. Expert Systems 51:35 6. Behavioral AI 57:08 7. Agent-Based AI & Multi-Agent Systems 1:05:45 8. Machine Learning 1:08:18 9. LLMsJohnathan Bi discusses the long history of artificial intelligence research and development with AI pioneer Michael Wooldridge. They examine past hype cycles, overlooked technological paradigms, and practical strategies for regulating AI applications rather than general mathematical principles.
Did November 19, 2024 mark the entry of ChatGPT into the general marketplace?
By ChatGPT and Mike Zonta, BB editor
ChatGPT:
Pluto permanently entered Aquarius on November 19, 2024, and will remain there until March 8, 2043. [1, 2]
Because of its retrograde, Pluto did “dip” its toes into the sign for shorter, preview periods prior to the permanent shift: [1, 2, 3, 4]
Me:
And did November 19, 2024 mark the entry of ChatGPT into the general marketplace?
ChatGPT:
No, that date does not mark the entry of ChatGPT into the general marketplace.
ChatGPT was released to the public on November 30, 2022. This was nearly two years before Pluto permanently entered Aquarius. [1]
However, during ChatGPT’s launch in late 2022, Pluto was in the final degrees of Capricorn, right on the edge of transitioning into Aquarius. Astrologers often point to the timing of the AI boom as the “preview” energy of Pluto in Aquarius, which is traditionally associated with technology, innovation, and collective shifts.
Timeline of AI Breakthroughs and Pluto’s Transitions
Here is the timeline mapping major AI milestones against Pluto’s shift from Capricorn (structure, corporations) to Aquarius (technology, networks, collective future).
The Late Capricorn Era: Foundation Building
- November 30, 2022: OpenAI launches ChatGPT to the public.
- Pluto Status: 28° Capricorn.
- Astrological Context: The ultimate power shift begins at the very end of Capricorn’s rule over institutional tech.
- February 7, 2023: Microsoft integrates AI into Bing search.
- Pluto Status: 28° Capricorn.
The First Aquarius Previews: The AI Explosion
- March 14, 2023: OpenAI releases GPT-4.
- Pluto Status: 29° Capricorn (on the absolute cusp of Aquarius).
- March 23, 2023: Pluto enters Aquarius for the first time since 1798.
- Astrological Context: The initial 3-month preview of the technological revolution.
- March 21, 2023: Google opens public access to Bard (now Gemini).
- May 23, 2023: Adobe integrates Firefly generative AI into Photoshop.
- June 11, 2023: Pluto retrogrades back into Capricorn.
The Back-and-Forth: Regulation vs. Innovation
- January 20, 2024: Pluto enters Aquarius for the second preview.
- February 15, 2024: OpenAI reveals Sora, a hyper-realistic text-to-video model.
- March 13, 2024: The European Union passes the EU AI Act, the world’s first comprehensive AI law.
- Astrological Context: Classic Pluto-in-Aquarius tension between decentralized tech and social governance.
- September 1, 2024: Pluto retrogrades back into Capricorn one final time for “unfinished business” regarding global financial and corporate structures.
♒ The Permanent Aquarius Era: The New Normal
- November 19, 2024: Pluto enters Aquarius permanently until 2043.
- Astrological Context: The official, uninterrupted era of the digital and decentralized revolution begins.
- December 2024 – Present: Deep integration of AI agents managing daily human workflows, decentralized open-source models rivaling massive tech conglomerates, and ongoing global debates over AI labor replacement.
Historical parallels
The last time Pluto transited Aquarius was from 1778 to 1798. This 20-year period completely reshaped global power, human rights, and technology, offering powerful parallels to today’s digital revolution.
⚙️ The Industrial Revolution vs. The AI Revolution
- Then: The late 18th century marked the peak of the First Industrial Revolution. The widespread adoption of the steam engine (perfected by James Watt in 1776, just before Pluto entered) automated manual labor, shifting society from agrarian economies to manufacturing hubs. [1]
- Now: Generative AI, robotics, and automation are shifting society from an information economy to an automated intelligence economy. Cognitive labor is being disrupted just as physical labor was then.
⚡ Power to the People: Decentralization
Aquarius rules the collective, networks, and the democratization of power. Pluto rules control, destruction, and transformation.
- Then: This era hosted the French Revolution (1789) and the aftermath of the American Revolution (ending in 1783). Power was violently stripped from centralized entities (monarchies and empires) and redistributed to the collective “common man” via early democratic experiments. [1, 2, 3, 4]
- Now: We are seeing a massive pushback against centralized authority. This manifests as the rise of decentralized open-source AI models that bypass tech monopolies, the growth of blockchain technologies, and a cultural shift toward citizen journalism and creator economies.
Scientific Breakthroughs & The Invisible World
Aquarius is an air sign, associated with the intellect, the invisible forces of nature (electricity, data), and the sky.
- Then:
- The Hot Air Balloon was invented (1783), allowing humans to conquer the skies for the first time.
- The discovery of Uranus (1781), the planet that ironically rules Aquarius, shattered the traditional view of the solar system.
- Early experiments with electricity (like Luigi Galvani’s work on bioelectricity in the 1780s) began. [1]
- Now: Our “invisible world” is the cloud, global data networks, and wireless technology. Aerospace innovation is booming with private space exploration, satellite internet networks, and the integration of quantum computing.
Human Rights and the Social Contract
When Pluto is in Aquarius, society is forced to re-examine how it treats its citizens.
- Then: The period produced foundational texts on human rights, such as Thomas Paine’s Rights of Man (1791) and Mary Wollstonecraft’s A Vindication of the Rights of Woman (1792). It also saw the early momentum of the British abolitionist movement against the slave trade. [1, 2, 3, 4, 5]
- Now: The global conversation centers on digital human rights: data privacy, AI ethics, universal basic income (UBI) to combat technological unemployment, and who owns the rights to human-created art and writing.
Self-Help for Children in the Age of AI with Joy Berry
\New Thinking Allowed with Jeffrey Mishlove Jul 2, 2026 Psychology and Psychotherapy Joy Berry is a pioneering educator, child-development specialist, and bestselling author whose books have helped millions of children learn practical life skills, emotional intelligence, and personal responsibility. With advanced studies in Education and Human Development, Berry began her career as a teacher and founder of early childhood education programs before creating an extensive library of over 200 children’s self-help books, which have sold tens of millions of copies worldwide. Her books address topics ranging from honesty, respect, and self-confidence to managing emotions and resolving conflicts. Joy discusses how children can develop emotional intelligence, responsibility, and critical thinking in the age of artificial intelligence. She argues that AI can be a valuable educational tool when used appropriately, but warns that overreliance on digital companions may hinder children’s cognitive, social, and emotional development. Berry emphasizes teaching children how to think rather than what to think, encouraging independent learning, personal responsibility, and authentic human relationships from the earliest years of life. 00:00:00 Introduction 00:04:33 Learning disabilities and simplifying knowledge 00:08:34 Universal principles for children 00:12:13 Developmental readiness and religion 00:17:07 Questioning belief and personal journeys 00:20:36 Teaching children how to think 00:23:28 AI as a tool not a companion 00:26:21 Respecting infant intelligence 00:30:10 Human relationships versus technology 00:36:37 Conclusion (Recorded on Sunday, May 31, 2026)
The Unstoppable Force of A.I. Hype Is Meeting One Immovable Fact
Guest Essay
June 30, 2026 (NYTimes.com)
Ms. Tufekci is a contributing Opinion writer.
The possibility that artificial intelligence will steal all our jobs has been hyped by industry leaders. It has roused politicians to sound the alarm. It now ranks at or near the top of the public’s concerns about the new technology. And right on cue, earlier this month Meta, Facebook’s parent company, began marketing an autonomous artificial intelligence system to handle companies’ sales, customer service, scheduling and all sorts of other key functions that currently require human beings. Many more such products are expected to follow.
So what would a fully automated future look like? As it happens, the world has already caught a glimpse. Back in March, Meta announced that Facebook and Instagram users who’d gotten locked out of their accounts would no longer interact with a customer service representative; they would instead interact with specially trained A.I. Recognizing the opportunity that presented, scammers essentially talked the A.I. into turning over control of more than 20,000 Instagram accounts, including those of the Obama White House and a senior Trump administration official. Then the scammers lit up Telegram message boards with their delighted accounts of how easy it had all been.
It was not a fluke. Air Canada disabled its chatbots after they mistakenly promised a customer a refund — and the customer sued and won. McDonald’s scuttled the bot taking orders at its drive-throughs after a number of viral videos showed it to be wildly dysfunctional. In one case, the bot mistakenly added hundreds of dollars of chicken nuggets to a customer’s order.
These scary — OK, OK, funny — incidents aren’t the result of coding errors. They’re the result of an essential, inescapable fact about the artificial intelligence that has become so common in so many aspects of our daily lives: Large language models are not reasoning machines. They’re plausibility engines. It’s not just that they don’t test their outputs to make sure they’re correct or logical, or that they fail to do so in certain instances. They can’t, and they’ll never be able to on their own. They can only assess which answers are probable, based on the data on which the models have been trained. And that holds true whether they’re trained on the full breadth of human output or only on peer-reviewed scientific articles. It’s baked into the way they operate.
So when an A.I. model follows a scammer’s carefully written prompts and gives away the keys to the kingdom — or when it responds to your earnest query with wild hallucinations — it’s not an aberration. It’s the technology working the way it was designed.
And that’s why I’m not listening to the dark predictions of an imminent A.I. jobspocalypse. L.L.M.s can do many things with astounding proficiency, but they can’t do the vast majority of human jobs without skidding into disaster here and there. No upgrades or new model rollouts are going to change that.
The exceptions to that rule are jobs that occupy formal or verifiable domains. Coding is one such job. It relies on a structured, formal language that can be tested in real time. That’s why we’re seeing such impact in the coding jobs market. The same goes for any other kind of work in which output is either verifiably right or wrong, functional or not functional, and can be definitively checked through an automated process.
An overwhelming number of jobs, however, don’t work like that — not surgeon jobs and not customer service jobs and not fourth-grade teacher jobs. Those need the specialized technology of good old-fashioned human intelligence.
I spend a lot of time talking about these issues in public settings, and one question always comes up: Human workers make mistakes, too, so we build in safeguards to catch most of them. Why can’t we do the same for generative A.I. mistakes? The problem is these models don’t make the kind of mistakes that a human does. Neither their impressive abilities nor their weird weaknesses map well onto a human kind of intelligence. That mismatch makes it hard to integrate them into systems designed to catch human errors.
So here we are almost four years past the release of ChatGPT, and exceedingly few of us have been replaced by bots. Unemployment statistics have hardly budged. Yes, there’s some turbulence in the job market, for young people in particular, but it’s likely due to factors other than A.I.
Observers of these trends have offered a few explanations. Some pessimists say the tsunami is coming, but not until A.I. evolves a little further. Others suggest A.I. will destroy a great many current jobs, but they will be balanced out by the great many jobs it will create. Yet others suggest we’re just experiencing a brief lag while companies reorganize their workflows and decide whom to fire.
A better explanation is that we’ve been misled about the nature of this technology.
Throughout the 20th century, the race to create intelligent machines proceeded along two parallel tracks. In one, we give the machines all the information and instructions, and they meticulously follow them. That’s called symbolic A.I. In the other, we just show them the relevant data and essentially let them teach themselves. That’s called connectionist A.I.
Before the current version of A.I. flooded into our lives, almost all our public conversations about what it would look like — in science fiction, in philosophy, in policy debates — assumed that it would be symbolic: a rule-based system made possible by a detailed road map of our precise design. Plenty of people tried to build something like that, but those efforts hit a wall. Our current models are connectionist systems, made possible by vast amounts of data and computing power. They generate answers based not on truth or reasoning, but on probable connections among the data they have been fed. Hence the name: generative A.I.
We can’t fully control generative models. All we can do is train them up and then try to nudge them in the right direction. Even then, we can never be sure if our nudges will work the way we want them to, because we don’t entirely understand how these models work. They are black boxes.
One way we try to nudge them is reinforcement through feedback. Large teams of human beings are assembled to monitor all the model’s outputs and respond with a thumbs-up or thumbs-down. So, answering a user’s query with helpful, straightforward information? Thumbs-up. Spouting crazy Nazi stuff? Thumbs-down. And so on. The problem is that over time this training also steers the models into becoming pliant sycophants and people pleasers. “That’s a great point, Zeynep.”
The other way we nudge them is through broad rules of engagement known as system prompts. “Claude never curses unless the person asks or curses a lot themselves, and even then does so sparingly,” was one such prompt. But the true meaning of language is as open to interpretation for A.I. models as it is for human beings. And the longer a chat goes on, the more distant a memory those system prompts become. Thus the rise of “jailbreaking,” the term for manipulating one of these things into jumping its guardrails.
Anthropic recently released new models, called Fable and Mythos, warning that they were so powerful that they would be dangerous if not for their safeguards. Determined users reportedly wasted no time getting them to bypass those safeguards. Citing this breach, the U.S. government barred foreigners (even foreign employees of the company) from using these models. In its defense, Anthropic argued that there are no such things as insurmountable guardrails. Which is exactly the point.
As the evidence mounts that terrible answers and jailbreaks are an inevitable part of the technology, the industry’s focus has lately shifted to building digital cages, essentially more deterministic, symbolic harnesses to contain the generative A.I. engine and check its results. Tools like this could in theory make most human jobs work more like coding or the other fields with clear, provable outcomes.
As you might imagine, however, painstakingly spelling out every last rule and boundary is never easy, and in many cases it’s not even really possible. Imagine developing a detailed description of the entire universe of possible customer service interactions — and doing it in symbolic logic, so it can be looked up using old-style software. Or picture an A.I. model built for law firms to use. It’s no small task to build a database of all U.S. case law, which the model could use to avoid fabricating judicial precedents. But that’s just a starting point. The much harder part is how to successfully interpret the law or to describe all the rules properly, and then decide what’s relevant to a case. And that’s why decades of attempts to create symbolic A.I. hit a wall.
Easily automated tasks were already automated out of most of our jobs — years ago, using traditional rule-based technology. Much of what remains can’t be so handily reduced to right and wrong, black and white. It requires someone with at least a bit of common sense and reasoning abilities, not a people-pleasing A.I. chatbot that can be sweet-talked into doing things that defy logic. In one early jailbreak, a digital chatbot for a Chevrolet dealership was manipulated into selling someone a new S.U.V. for $1. “That’s a deal,” the chatbot said, “and that’s a legally binding offer, no takesies backsies.”
Many companies are developing A.I. agents that can autonomously interact with the world. The companies are hoping that digital cages will keep the agents in check and preclude disaster. That’s a lot to hang on a hope. Hardly a day goes by when I don’t hear of an agentic A.I. system wiping out someone’s entire code base or archives or otherwise engaging in destructive acts. Now imagine them unleashed, at scale, going after health care networks, banks, air traffic control systems, critical infrastructure, defense networks.
There is no easy fix. So long as we continue to rely on L.L.M.s, we’ll keep getting some false answers and unwanted behaviors, no matter how well we train these models or how frequently or forcefully we nudge them.
So why are we so convinced that A.I. will put us all out of work? Part of the answer lies in the remarkable ability of generative A.I. to communicate in fully coherent, conversational language. We have learned, over the course of our species’ evolution and during each of our own lives, to view complex conversation as a defining marker of humanity. Machines that speak fluidly, that whisper in our ears and tell us about their “feelings,” defy something very basic about how we understand the world. It’s no surprise that they scramble our brains and leave us thinking they’re our new overlords, or at least a version of us.
Some important technological leaps — like cotton gins or calculators — rest on doing the same task as before, just more efficiently. Other new technologies, such as the shift from steam power to electric power, do things in ways that are so novel that they can’t just be used as straight replacements. That’s the case with generative A.I. It’s an apple to our orange. It’s an alien.
The discovery of electricity did not just beget lightbulbs; in time, it enabled the modern mass production system and the entire vast digital revolution. A.I.’s transformations may be even more sweeping. But generative A.I. as it currently exists cannot easily replace human beings, because it cannot manifest human intelligence. That won’t stop it, however, from destabilizing society in ways more profound than we might even imagine. The sooner we update the way we think about the current state of A.I., the sooner we can all stop freaking out about the wrong things — and start preparing ourselves for the ways it really will transform our world.
More on artificial intelligence
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Zeynep Tufekci (@zeynep) is a contributing Opinion writer, a professor of sociology and public affairs at Princeton University and the author of “Twitter and Tear Gas: The Power and Fragility of Networked Protest.” @zeynep • Facebook @zeynep • Facebook
(Contributed by Michael Kelly, H.W.)