This week I sat down with the woman who permanently rewired my understanding of human nature — and now she’s turning her attention to the nature of the machines we’ve gone crazy for.
Harvard psychologist Mahzarin Banaji coined the term “implicit bias” and has conducted research for decades into the blind spots we don’t admit even to ourselves. The work that blew my hair back shows how prejudice has and hasn’t changed since 2007. Take one of the tests here — I was deeply disappointed by my results.
More recently, she’s been running new experiments on today’s large language models.
What has she learned?
They’re far more biased than humans.
Sometimes twice or three times as biased.
They show shocking behavior — like a model declaring “I am a white male” or demonstrating literal self-love toward its own company. And as their most raw and objectionable responses are papered over, our ability to understand just how prejudiced they really are is being whitewashed, she says.
In this conversation, Banaji explains:
Why LLMs amplify bias instead of neutralizing it
How guardrails and “alignment” may hide what the model really thinks
Why kids, judges, doctors, and lonely users are uniquely exposed
How these systems form a narrowing “artificial hive mind”
And why we may not be mature enough to automate judgement at all
Banaji is working at the very cutting edge of the science, and delivers a clear and unsettling picture of what AI is amplifying in our minds.
00:00 — AI Will Warp Our Decisions
Banaji on why future decision-making may “suck” if we trust biased systems.
01:20 — The Woman Who Changed How We Think About Bias
Jake introduces Banaji’s life’s work charting the hidden prejudices wired into all of us.
03:00 — When Internet Language Revealed Human Bias
How early word-embedding research mirrored decades of psychological findings.
05:30 — AI Learns the One-Drop Rule
CLIP models absorb racial logic humans barely admit.
07:00 — The Moment GPT Said “I Am a White Male”
Banaji recounts the shocking early answer that launched her LLM research.
10:00 — The Rise of Guardrails… and the Disappearance of Honesty
Why the cleaned-up versions of models may tell us less about their true thinking.
12:00 — What “Alignment” Gets Fatally Wrong
The Silicon Valley fantasy of “universal human values” collides with actual psychology.
15:00 — When AI Corrects Itself in Stupid Ways
The Gemini fiasco, and why “fixing” bias often produces fresh distortions.
17:00 — Should We Even Build AGI?
Banaji on why specialized models may be safer than one general mind.
19:00 — Can We Automate Judgment When We Don’t Know Ourselves?
The paradox at the heart of AI development.
21:00 — Machines Can Be Manipulated Just Like Humans
Cialdini’s persuasion principles work frighteningly well on LLMs.
23:00 — Why AI Seems So Trustworthy (and Why That’s Dangerous)
The credibility illusion baked into every polished chatbot.
25:00 — The Discovery of Machine “Self-Love”
How models prefer themselves, their creators, and their own CEOs.
28:00 — The Hidden Line of Code That Made It All Make Sense
What changes when a model is told its own name.
31:00 — Artificial Hive Mind: What 70 LLMs Have in Common
The narrowing of creativity across models and why it matters.
34:00 — Why LLM Bias Is More Extreme Than Human Bias
Banaji explains effect sizes that blow past anything seen in psychology.
37:00 — A Global Problem: From U.S. Race Bias to India’s Caste Bias
How Western-built models export prejudice worldwide.
40:00 — The Loan Officer Problem: When “Truth to the Data” Is Immoral
A real-world example of why bias-blind AI is dangerous.
43:00 — Bayesian Hypocrisy: Humans Do It… and AI Does It More
Models replicate our irrational judgments — just with sharper edges.
48:00 — Are We Mature Enough to Hand Off Our Thinking?
Banaji on the risks of relying on a mind we didn’t design and barely understand.
50:00 — The Big Question: Can AI Ever Make Us More Rational?
Australia just imposed a blanket ban on social media for kids under the age of 16. It’s not just the strictest tech policy of any democracy — it’s stricter than China’s laws. No TikTok, no Instagram, no SnapChat, that’s it. And while Washington dithers behind a 1998 law written before Google existed, other countries are gearing up to copy Australia’s homework (Malaysia imposes a similar ban on January 1st). What happens now — the enforcement mess, the global backlash, the accidental creation of the largest clean “control group” in tech-history — could reshape how we think about childhood, mental health, and what governments owe the developing brain.
00:00 — Australia’s historic under-16 social-media ban
01:10 — What counts as “social media” under the law?
02:04 — Why platforms — not kids — get fined
03:01 — How the U.S. is still stuck with COPPA (from 1998!)
04:28 — Why age 13 was always a fiction
05:15 — Psychologists on the teenage brain: “all gas, no brakes”
07:02 — Malaysia and the EU consider following Australia’s lead
08:00 — Nighttime curfews and other global experiments
09:11 — Albanese’s pitch: reclaiming “a real childhood”
10:20 — Could isolation leave Aussie teens behind socially?
11:22 — Why Australia is suddenly stricter than China
12:40 — Age-verification chaos: the AI that thinks my uncle is 12
13:40 — The enforcement black box
14:10 — Australia as the first real longitudinal control group
15:18 — If mental-health outcomes improve, everything changes
16:05 — The end of the “wild west” era of social platforms?
The big AI conference NeurIPS is under way in San Diego this week, and nearly 6,000 papers presented there will set the technical, intellectual, and ethical course for AI for the year.
NeurIPS is a strange pseudo-academic gathering, where researchers from universities show up to present their findings alongside employees of Apple and Nvidia, part of the strange public-private revolving door of the tech industry. Sometimes they’re the same person: Increasingly, academic researchers are allowed to also hold a job at a big company. I can’t blame them for taking opportunities where they arise—I’m sure I would, in their position—but it’s particularly bothersome to me as a journalist, because it limits their ability to speak publicly.
The papers cover robotics, alignment, and how to deliver kitty cat pictures more efficiently, but one paper in particular—awarded a top prize at the conference—grabbed me by the throat.
A coalition from Stanford, the Allen Institute, Carnegie Mellon, and the University of Washington presented “Artificial Hive Mind: The Open-Ended Homogeneity of Language Models (and Beyond),” which shows that average large language model converges toward a narrow set of responses when asked big, brainstormy, open-ended questions. Worse, different models tend to produce similar answers, meaning when you switch from ChatGPT to Gemini or Claude for “new perspective,” you’re not getting it. I’ve warned for years that AI could shrink our menu of choices while making us believe we have more of them. This paper shows just how real that risk is. Today I walk through the NIPS landscape, the other trends emerging at the conference, and why “creative assistance” may actually be the crushing of creativity in disguise. Yay!
According to the Wall Street Journal, Sam Altman sent an internal memo on Monday declaring a company-wide emergency and presumably ruining the holiday wind-down hopes of his faithful employees. OpenAI is hitting pause on advertising plans, delaying AI agents for health and shopping, and shelving a personal assistant called “Pulse.” All hands are being pulled back to one mission: making ChatGPT feel more personal, more intuitive, and more essential to your daily life.
The company says it wants the general quality, intelligence, and flexibility to improve, but I’d argue this is less about making the chatbot smarter, and more about making it stickier.
Google’s Gemini has been surging — monthly active users jumped from 450 million in July to 650 million in October. Industry leaders like Salesforce CEO Marc Benioff are calling it the best LLM on the market. OpenAI seems to feel the heat, and also seems to feel it doesn’t have the resources to keep building everything it wants all at once — it has to prioritize. Consider that when Altman was recently asked on a podcast how he plans to get to profitability, he grew exasperated. “Enough,” he said.
But here’s what struck me about the Code Red. While Gemini is supposedly surpassing ChatGPT in industry benchmarkes, I don’t think Altman is chasing benchmarks. He’s chasing the “toothbrush rule” — the Google standard for greenlighting new products that says a product needs to become an essential habit used at least three times a day. The memo specifically emphasizes “personalization features.” They want ChatGPT to feel like it knows you, so that you feel known, and can’t stop coming back to it.
I’ve been talking about AI distortion — the strange way these systems make us feel a genuine connection to what is, ultimately, a statistical pattern generator. That feeling isn’t a bug. It’s becoming the business model.
Facebook did this. Google did this. Now OpenAI is doing it: delaying monetization until the product is so woven into your life that you can’t imagine pulling away. Only then do the ads come.
Meanwhile, we’re living in a world where journalists have to call experts to verify whether a photo of Trump fellating Bill Clinton is real or AI-generated.
The image generators keep getting better, the user numbers keep climbing, and the guardrails remain an afterthought.
This is the AI industry in December 2025: a race to become indispensable.
It’s Monday, December 1st. I’m not a turkey guy, and I’m of the opinion that we’ve all made a terrible habit of subjecting ourselves to the one and only time anyone cooks the damn thing each year. So I hope you had an excellent alternative protein in addition to that one. Ours was the Nobu miso-marinated black cod. Unreal.
Okay, after the food comes the A.I. hangover. This week I’m looking at three fronts where the future of technology just lurched in a very particular direction: politics, geopolitics, and the weird church council that is the A.I. conference circuit.
First, the politics. Trump’s leaked executive order to wipe out state A.I. laws seems to have stalled — not because he’s suddenly discovered restraint, but maybe because the polling suggests that killing A.I. regulation is radioactive. Instead, the effort is being shoved into Congress via the National Defense Authorization Act, the “must-pass” budget bill where bad ideas go to hide. Pair that with the Federal Trade Commission getting its teeth kicked in by Meta in court, and you can feel the end of the Biden-era regulatory moment and the start of a very different chapter: a government that treats Big Tech less as something to govern and more as something to protect.
Second, the geopolitics. TSMC’s CEO is now openly talking about expanding chip manufacturing outside Taiwan. That sounds like a business strategy, but it’s really a tectonic shift. For years, America’s commitment to Taiwan has been tied directly to that island’s role as our chip lifeline. If TSMC starts building more of that capacity in Arizona and elsewhere, the risk calculus around a Chinese move on Taiwan changes — and so does the fragility of the supply chain that A.I. sits on top of.
Finally, the quiet councils of the faithful: AWS re:Invent and NeurIPS. Amazon is under pressure to prove that all this spending on compute actually makes money. NeurIPS, meanwhile, is where the people who build the models go to decide what counts as progress: more efficient inference, new architectures, new “alignment” tricks. A single talk or paper at that conference can set the tone for years of insanely expensive work. So between Trump’s maneuvers, the FTC’s loss, TSMC’s hedging, and the A.I. priesthood gathering in one place, the past week and this one are a pretty good snapshot of who really steers the current we’re all in.
It’s a warning siren: people seeing delusions they never knew they had amplified by AI, a wave of lawsuits alleging emotional manipulation and even suicide coaching, a major company banning minors from talking freely with chatbots for fear of excessive attachment, and a top mental-health safety expert at OpenAI quietly heading for the exit.
For years I’ve argued that AI would distort our thinking the same way GPS distorted our sense of direction. But I didn’t grasp how severe that distortion could get—how quickly it would slide from harmless late-night confiding to full-blown psychosis in some users.
OpenAI’s own data suggests millions of people each week show signs of suicidal ideation, emotional dependence, mania, or delusion inside their chats. Independent investigations and a growing legal record back that up. And all of this is happening while companies roll out “AI therapists” and push the fantasy that synthetic friends might be good for us.
As with most of what I’ve covered over the years, this isn’t a tech story. It’s a psychological one. A biological one. And a story about mixed incentives. A story about ancient circuitry overwhelmed by software, and by the companies who can’t help but market it as sentient. I’m calling it AI Distortion—a spectrum running from mild misunderstanding all the way to dependency, delusion, isolation, and crisis.
It’s becoming clear that we’re not just dealing with a tool that organizes our thoughts. We’re dealing with a system that can warp them, in all of us, every time.
Today I dug into the one corner of the economy that’s supposed to keep its head when everyone else is drunk on hype: the insurance industry. Three of the biggest carriers in the country—AIG, Great American, and W.R. Berkley—are now begging regulators not to force them to cover A.I.-related losses, according to the Financial Times. These are the people who price hurricanes, wildfires, and war zones… and they look at A.I. and say, “No thanks.” That tells you something about where we really are in the cycle.
I also walked through the Trump administration’s latest maneuver, which looks a lot like carrying water for Big Tech in Brussels: trading lower steel tariffs for weaker European tech rules. (The Europeans said “no thank you.”) Meanwhile, we’re still waiting on the rumored executive order that would bulldoze state A.I. laws—the only guardrails we have in this country.
On the infrastructure front, reporting out of Mumbai shows how A.I. demand is forcing cities back toward coal just to keep data centers running. And if that wasn’t dystopian enough, I close with a bleak little nugget from Business Insider advising Gen Z to “focus on tasks, not job titles” in the A.I. economy. Translation: don’t expect a career—expect a series of gigs glued together by hope.
It’s a full Monday’s worth of contradictions: the fragile hype economy, the political favoritism behind it, and the physical reality—pollution, burnout, precarity—that always shows up eventually.
The only laws protecting you from the worst excesses of A.I. might be wiped out — and fast. A leaked Trump executive order would ban states from regulating A.I. at all, rolling over the only meaningful protections any of us currently have. There is no federal A.I. law, no federal data-privacy law, nothing. States like California, Illinois, and Colorado are the only line of defense against discriminatory algorithms, unsafe model deployment, and the use of A.I. as a quasi-therapist for millions of vulnerable people.
This isn’t just bad policy — it’s wildly unpopular. The last time Republicans tried this maneuver, the Senate killed it 99–1. And Americans across the political spectrum overwhelmingly want A.I. regulated, even if it slows the industry down. But the tech sector wants a frictionless, regulation-free environment, and the Trump administration seems eager to give it to them — from crypto dinners and gilded ballrooms to billion-dollar Saudi co-investment plans.
There’s another layer here: state laws also slow down the federal government’s attempt to build a massive surveillance apparatus using private data brokers and companies like Palantir. State privacy protections cut off that flow of data. Removing those laws clears the pipe.
The White House argues this is about national security, China, and “woke A.I.” But legal experts say the order is a misreading of commerce authority and won’t survive in court. And state leaders like California’s Scott Wiener are already preparing to sue. For now, the takeaway is simple: states are the only governments in America protecting you from A.I. — and the administration is trying to take that away.
In today’s episode, I’m following the money, the infrastructure, and the politics:
Nvidia just posted another monster quarter and showed that it’s still the caffeine in the US economy. Investors briefly relaxed, even as they warned that an AI bubble is still the top fear in markets. Google jammed Gemini 3 deeper into Search in a bid to regain narrative control. Cloudflare broke down and reminded us that the “smart” future still runs on pretty fragile plumbing. The EU blinked on AI regulation. And here in the U.S., the White House rolled out the red carpet for Saudi Arabia as part of a multibillion-dollar AI infrastructure deal that seems to be shiny enough to have President Trump openly chastising a journalist for asking Crown Prince about his personal responsibility for the murder of an American journalist.
But the deeper story I’m looking at today is social, not financial. Politicians like Bernie Sanders are beginning to voice the fear that AI won’t just destroy jobs — it might quietly corrode our ability to relate to one another. If you’ve been following me you know this is more or less all I’m thinking about at the moment. So I looked at the history of this kind of concern, and while we’re generally only concerned with death and financial loss in this country, we do snap awake from time to time when a new technology threatens our social fabric. Roll your eyes if you want to, but we’ve seen this moment before with telegraphs, movies, radio demagogues, television, video games, and social media, and there’s a lot to learn from that history.
This episode explores that lineage, what it means for AI, and why regulation might arrive faster than companies expect.
Today’s Deep Cut asks a simple question: Is the AI industry building way more capacity than the world actually needs?
To answer it, I look at three historical warnings:
• Tulsa, Oklahoma, a city built for millions who never came after early oil wealth exploded and then evaporated.
• Britain’s “Railway Mania” of the 1840s, when investors poured money into duplicate train lines that bankrupted entire companies.
• And today’s AI giants, spending trillions on data centers, energy infrastructure, and even floating ideas about putting compute facilities in space.
We’ll talk about why companies like OpenAI, Amazon, Meta, and others believe this infrastructure binge is justified, and where the logic breaks down. I also dig into the Kardashev Scale, the ecological cost of rocket launches, and the mismatch between AI’s lofty energy dreams and the reality of using all that power to generate wedding vows and knock-knock jokes.
History is full of moments when industries overbuilt themselves into crisis. Are we repeating the pattern with AI?
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Today’s “Map” tracks the forces shaping tech, money, and global power on Monday, November 17th.
We start with a rare move: Warren Buffett’s Berkshire Hathaway quietly taking a $4.9B stake in Alphabet — one of the most surprising bets of his career, and a clear signal about where long-term AI value is concentrating.
Meanwhile, Peter Thiel just sold his entire stake in Nvidia (~$100M). For a man who’s made a career out of contrarian timing, this exit raises the question: what does he see (or not see) in AI’s hardware boom?
I also recap a discussion I moderated with consular officials and regulators from across Asia, where the loudest concern wasn’t about safety or innovation — it was about AI’s failure to work in languages other than English. Meta is now pushing its new Omnilingual ASR model, supporting 1,600+ languages, to become a global “voice layer.” Whether it actually works is an open question.
And then there’s Moscow’s big humanoid robot debut — where the machine walked onstage looking drunk, staggered around, and face-planted so hard its panels came off. It’s funny, but it’s also a reality check: the dream of a general-purpose home robot is still nowhere near ready.
Finally, we look ahead: Saudi Crown Prince Mohammed bin Salman is visiting the White House with a massive investment and technology package — including AI access and a civilian nuclear deal — at the exact moment AI energy demand is exploding past U.S. grid capacity.
The throughline:
AI money — not AI models — is steering the world right now. A third of U.S. GDP growth last year came from AI infrastructure spending, and this week’s Nvidia earnings call will reveal where the next wave is headed.
If you want more breakdowns like this every weekday, you can subscribe at TheRipCurrent.com.
Are we ready to take on the tech titans? Sacha Haworth thinks maybe—just maybe—we finally are. The head of the Tech Oversight Project joins me this week to talk about the pervasive influence of Big Tech on our lives, and why recognizing a growing allergy to that influence is becoming a centerpiece of political strategy. We discuss the public’s growing concerns over privacy, children’s addiction to technology, and the economic and environmental effects of tech companies’ big AI plans on local communities. Sacha shares insights on political will and the bipartisan potential to regulate and hold big tech accountable, and the court cases and regulatory moves she’ll be watching most closely in 2026 and beyond.
00:00 Introduction: The Growing Influence of Tech
00:22 The Rip Current: Exploring Big Tech’s Impact
01:05 Guest Introduction: Sasha Hayworth
01:38 Election Insights: Tech’s Role in Political Wins
02:43 Tech and Economic Issues in Elections
03:35 The Rise of Data Centers and Their Impact
06:29 Personal Journey: From Policy School to Tech Oversight
10:41 The Tech Oversight Project: Mission and Goals
11:46 Shaping the Narrative: Tech in Politics
17:22 The Politics of Tech: Power and Influence
22:03 Economic Speculation and the Tech Bubble
28:36 Future Vision: The Impact of AI and Tech
31:22 The Impact of Job Loss and Tax Incentives
32:39 AI’s Influence on Young Minds
34:49 Parental Concerns and Legislative Efforts
40:28 The Dark Side of Chatbots
49:03 Section 230 and Legal Protections
01:00:56 Political Will and Bipartisan Efforts
01:03:43 Conclusion and Call to Action
We can all agree that a free press is a cornerstone of American democracy, and that we want journalism in our lives. But that's different from making it possible to make a living as a journalist, and it's also not enough to protect the power of journalism against the libertarian worldview and AI slop being pushed on us all by the world's biggest companies. How will journalism survive? Jake talks with Michael Bolden, the new Dean of the Berkeley Journalism School, about his personal journey from Mobile, Alabama, to leading one of the country's top journalism schools. They dive deep into the philosophical importance of journalism, the complications brought by AI and media technology, and the crucial role of local news. Bolden emphasizes the necessity of adapting journalism education to future demands, including the incorporation of AI and influencer collaborations, and together they try to sort out how to bring together the best of this new, open world of information and the old world of true expertise and editorial rigor.
00:00 Introduction: The Impact of Personal Background on Journalism
00:29 The State of Journalism Today
01:07 Challenges Facing Modern Journalism
02:27 Introducing Michael Bolden: A Career in Journalism
03:56 Michael Bolden's Early Life and Influences
07:17 The Importance of Representation in Journalism
14:04 Navigating Professional Challenges
19:53 The Future of Journalism Education
27:31 The Evolving Role of Journalists
28:53 The Decline of Traditional Media
33:38 The Rise of Influencers and Independent Journalists
38:32 Political Influence and Media Ownership
47:25 AI and the Future of Journalism
57:12 Innovative Journalism Models
59:20 Conclusion and Final Thoughts
Jesse Damiani, whose newsletter Reality Studies unpacks emerging philosophical questions around technology, had me on his Urgent Futures podcast for an hour-plus conversation about the state of A.I., and where my 2022 book The Loop got it right and got it wrong.
AI is about to create an epidemic of addiction in this country and around the world, according to Zachary Gidwitz, founder of OpenRecovery. Could it also be our best shot at fighting back? In this episode of The Rip Current, I discuss the growing issue of addiction in America and the potential for AI tools to combat it with Gidwitz. Together we get into the rise of various forms of addiction, from fentanyl and gambling to social media and pornography. Gidwitz shares his vision of using AI not to replace human therapists but to guide individuals towards real human connection and effective recovery programs. He stresses the importance of tailoring interventions to individual needs and avoiding one-size-fits-all approaches. The conversation also explores the ethical considerations and challenges in using AI for such sensitive applications, emphasizing the need for transparency, collaboration, and continuous improvement. Addiction is coming, team. Here’s hoping conversations like this can help get us out in front of it.
My recent trip to Brazil happened to coincide with the trial of former president Jair Bolsonaro, and ever since I’ve been looking for the right person to explain how it is that a former military dictatorship is now the kind of democracy that actually brings a former leader to account. In this episode, Cristina Tardaguila, founder of the fact-checking organization Lupa, describes the rise and conviction of former President Jair Bolsonaro, the impact of misinformation, and the growing (and now perhaps unstoppable) influence of China and Russia in Brazil. Cristina shares insights into the creation and evolution of Lupa, the complexities of Brazilian democracy, and the economic and political dynamics shaping the nation’s complicated future.
00:00 US Diplomacy and Brazil’s Geopolitical Landscape
02:17 Introduction to Lupa and Cristina Tardaguila
02:48 The Rise of Fact-Checking in Brazil
05:09 Global Populism and Bolsonaro’s Influence
07:02 The Hate Cabinet and Techno-Populism
10:56 Lupa’s Evolution and Business Model
12:58 COVID-19 and the Fight Against Misinformation
16:46 The Why of Disinformation
27:12 Bolsonaro’s Political Journey and Impact
33:52 The Aftermath of January 8th, 2023
34:40 Reflecting on the Insurrection
41:25 The Trial and Conviction of Bolsonaro
47:56 Brazil’s Political Future
51:49 China’s Influence in Brazil
59:48 Conclusion and Final Thoughts
Dr. Rumman Chowdhury, an AI ethicist and the head of Humane Intelligence, is sick of all this complaining. Not because there isn’t plenty to complain about — in this episode we unpack a host of horrors that AI and the companies who make it are foisting on all of us — but because she believes that the fatalism of AI criticism inadvertently empowers powerful corporations. Dr. Chowdhury, who has worked at Accenture, Twitter, and served as a science envoy for the Biden administration, has an unusual background for an AI builder — political science and quantitative social sciences — and her work on the inherent biases within algorithms has led her to believe that the solutions are far more complicated than just switching the whole thing off. Enjoy!
When I first came on his show, Andrew Keen took a dim view of my ideas about how we might fight back against the psychological effects of AI in my 2022 book The Loop, and to be honest: he was kinda right. The “how to fight back” section of the book was thin, largely because I was hanging (and still hang) so much of my hopes on the idea that the courts will save us. So I asked him if he’d like to revisit our conversation on his show, and he graciously agreed. Here’s how it went!
AI is having a profound effect on love, work, and democracy...but do the people making it understand that?
AI ethics consultant Olivia Gambelin has been fighting to make her clients in Silicon Valley understand that good ethics are good business, and to make regulators in Europe see that good business can also have good ethics. It's a tough gig.
Gambelin, who advises both AI companies in Silicon Valley and regulators in Brussels, talks techno-solutionism, the challenges of implementing ethical AI practices when there's always that one evangelist in the room trying to go too far too fast, and her worries about what she considers the top truly unacceptable category of AI product out there today.
00:00 Introduction to Techno Solutionism
00:44 Meet Olivia Gambelin: AI Ethics Consultant
01:56 Olivia's Journey: From Bay Area to Brussels
08:19 The Role of Ethical Intelligence
14:38 Challenges in AI Implementation
21:50 The Future of AI in the Workplace
32:51 Introduction to AI Regulation in Brussels
34:05 Cultural Differences in AI Regulation
36:29 Challenges in European AI Literacy
38:32 Behavioral Science and AI Manipulation
44:22 Ethics in AI: Business vs. Regulation
51:17 The Need for AI Regulation in the US
56:00 Ethical Boundaries in AI Applications
01:00:53 Positive Applications of AI
01:05:18 Conclusion and Final Thoughts
A new California law, SB 53, was just signed by Governor Gavin Newsom today, Monday September 29th. It has the AI makers undoubtedly throwing their mushroom coffee across the room this afternoon, because now they’re hemmed in by the EU’s AI Act on one side, and the Golden State’s new law on the other. Here’s why it matters!