When was the last time you lost yourself in a thrilling novel or a moving memoir? This episode delves into the growing conflict between authors and artificial intelligence, as over seventy prominent authors unite to keep AI away from the art of human storytelling. We explore the deepening fears about AI’s role in publishing, from writing original content to narrating audiobooks, and the legal and economic implications for authors. Discover what this uprising reveals about the future of literature and the fundamental human spirit behind storytelling. Don't forget to subscribe, share, or leave a review! 

FULL TRANSCRIPT: I'm Harry Kermode and this is AI Daily.

When was the last time you lost yourself in a thrilling novel or a moving memoir? Chances are, the heart and life flowing through those pages came directly from the brilliant mind of an author. Today, that very brilliance is at the center of a growing conflict—between human creators and the unstoppable forces of artificial intelligence.

Over this past weekend, more than seventy prominent authors—including literary heavyweights Dennis Lehane, Lauren Groff, and Gregory Maguire—published an open letter that instantly resonated across the publishing industry. Within 24 hours, support snowballed to over a thousand writers, artists, and publishing professionals, all rallying behind a single message to the world’s biggest publishers: Keep AI away from the art of human storytelling.

At the heart of this literary uprising are deepening fears about publishers incorporating artificial intelligence into critical creative roles—writing original content, designing book covers, and perhaps most controversial of all, narrating audiobooks. This isn't just theoretical anxiety. A recent federal ruling declared AI training on copyrighted books as legal "fair use," giving tech companies the green light to feed their algorithms on millions of authors' words.

But beneath the surface, there's more than legal semantics. Publishers see financial incentives. Using AI can dramatically cut expenses, replacing entire job categories—from editors to audiobook narrators—with streamlined, algorithm-driven outputs. Meanwhile, tech giants like Amazon and Audible are aggressively promoting synthetic narrations, promising scalable audio production at a fraction of human labor costs.

For many authors, the stakes are existential. Audiobook sales represent nearly a third of some writers' yearly incomes—livelihoods seriously threatened when synthetic AI voices start narrating their stories. Voice actors and translators, essential artists in their own right, risk losing even more ground.

Yet publishers and tech firms confidently frame the shift as progress. Audible has argued that AI narration "democratizes storytelling," broadening access to literature. Publishers speak glowingly about artificial intelligence as an enabler of creativity rather than its replacement.

But in reality, a troubling shadow economy is emerging. Independent authors report rampant plagiarism by poorly regulated platforms filled with AI-generated books masquerading as human-authored originals. Critics liken these works to literary "deepfakes"—algorithmically-assembled text cobbled together without meaning, craft, or authenticity.

And then there's the profound human cost. Artists fear AI-produced content doesn't just take livelihood—it "strips authors of dignity," as the open letter passionately frames it. A powerful statement reads: "AI-generated books feel cheap because they are cheap. The replacement of art and artists isn't societal benefit—it’s cultural loss."

Today's author rebellion reveals profound questions that strike at the heart of what makes us human. Stories have always been our way of understanding ourselves, of reaching across cultures and generations. But as technology threatens to automate even our most cherished forms of human expression, does this disrupt storytelling itself? Or more critically, could it quietly erase the storyteller?

As creative professionals take an unprecedented stand, publishers now face a turning point. Will they defend the irreplaceable human spirit behind every story, or choose efficiency above authenticity? For now, the future of literature—and the vivid human worlds it creates—hangs by a thread, caught between human passion and machine efficiency.

I'm Harry Kermode. That's it for AI Daily, we'll see you tomorrow.

AI vs. Authors: The Battle for the Soul of Storytelling

The United States Senate has delivered a decisive verdict on AI governance, rejecting a proposal for a ten-year ban on state-level AI regulations by a 99 to 1 vote. This amendment was fiercely supported by Big Tech giants like OpenAI and Google, who feared a regulatory "patchwork," but bipartisan state attorneys general and civil rights groups resisted, citing concerns about unchecked biases and threats to democratic oversight. Listeners will gain insights into the balance of power between tech corporations and grassroots coalitions in shaping AI policy. Don't forget to subscribe, share, or leave a review! 

FULL TRANSCRIPT: It's Wednesday, July 2nd, 2025.

In a late-night session yesterday, as Americans slept, the United States Senate delivered a thunderous verdict on AI governance. By a resounding vote of 99 to 1, lawmakers crushed a proposal that would have imposed a sweeping ten-year ban on state-level AI regulations, a measure backed aggressively by Big Tech.

The amendment had been spearheaded by Senator Marsha Blackburn of Tennessee, originally receiving strong support from industry giants—including OpenAI and Google—who spent millions lobbying for regulatory uniformity. Their argument? That a "patchwork" of state-specific laws would cripple innovation and hand competitive advantages directly to China.

But beneath that narrative of streamlined innovation lay a very different reality—and it was one the Senate simply wasn't buying. Forty-four bipartisan state attorneys general mounted an intense and unified opposition, underscored by influential voices like California's Rob Bonta, who described the measure as a "dangerous giveaway to Big Tech." Civil rights groups warned starkly of unchecked algorithmic bias, deepfake threats, and rampant discrimination if the states' emerging protections were overturned.

So how did we get here? Over the past two years, state's legislatures took advantage of Congress's gridlock and surged ahead. California required unprecedented transparency on training data for major AI models; New York demanded rigorous safety protocols; Colorado set standards against bias in automated hiring tools. The waves of state initiatives prompted tech lobbyists—who feared soaring compliance costs—to lean heavily on the federal government for a lifeline.

The tech industry almost got its wish, until public backlash erupted against the amendment. Senator Thom Tillis—representing North Carolina, home to significant tech investments—emerged as the single voice voting "yes," calling the ban vital for innovation. But his voice stood solitary against an extraordinary bipartisan consensus rarely seen in this era of polarization.

This decisive rebuke highlighted not just uncommon unity, but also the emergence of two powerful camps: on one side, immense corporate wealth and the promise of AI innovation without shackles; on the other, grassroots coalitions, state lawmakers, and civil liberties organizations fighting fiercely for local oversight and accountability.

But even this definitive vote leaves questions floating. While states retain their freedom to govern AI with tailored approaches, industry insiders warn this could lead to regulatory chaos. Will companies face fifty different AI laws? Will longer timelines and massive compliance burdens snuff out American competitiveness globally?

Activists like the Demand Progress coalition strongly disagree. Their statement highlights that the alternative—giving industry a blank check to police themselves—risks far greater harm.

And now, the battle shifts to states' legislatures and courtrooms across America. Companies like OpenAI could pursue legal challenges against restrictive regulations. California and New York, emboldened by this victory, will undoubtedly draft wider, more aggressive measures.

What happens next may determine who truly holds the power in America's AI future: Big Tech, states, or citizens demanding accountability. One thing, though, is certain—yesterday's Senate vote was bigger than policy—it was a warning shot, clarifying that democracy, despite fierce lobbying, is not yet for sale.

For "AI Daily," I'm Harry Kermode. See you tomorrow.

Rejecting Big Tech: Senate Crushes AI Deregulation Proposal

What makes us human? In today's episode of AI Daily, we delve into the unveiling of "Centaur," the pioneering AI that simulates human cognitive processes across various psychological scenarios. We explore its cutting-edge capabilities, including its ability to mimic human decision-making and reaction times, as well as its potential impact on both AI development and psychology. Listeners will gain insights into the ethical dilemmas and transformative possibilities posed by AI that can think like us. Don't miss this profound discussion—subscribe now and leave us a review! 

FULL TRANSCRIPT: I'm Harry Kermode, and this is AI Daily.

What makes us human? For centuries, we've asked this question, defining ourselves by our cognitive uniqueness—our ability to reason, decide, to think human thoughts. But imagine for a moment an AI capable of mirroring human decision-making with startling precision, one not only mimicking behavior, but emulating the very threads that weave our minds. What then remains of our cherished human uniqueness?

Yesterday, researchers at Helmholtz Munich unveiled "Centaur," the first AI model capable of simulating human cognitive processes across diverse psychological scenarios. Published in Nature, this groundbreaking research described how Centaur, trained using the massive "Psych-101" dataset—ten million human psychological decisions carefully aggregated from decades of experiments—managed not only to predict human choices in familiar settings, but crucially in completely novel situations too.

Centaur is profound because it's the first AI that seems genuinely capable of emulating human thought across tasks like gambling behavior, memory challenges, ethical dilemmas—even problem-solving puzzles. But it's the depth of its mimicry that's especially jolting. The model doesn't just replicate final decisions; it aligns uncannily well with human reaction times, hesitations, and even neural activation patterns. As lead researcher Marcel Binz described it, Centaur represents a "virtual laboratory," opening unprecedented windows into our cognitive processes without ever needing to peer inside a human brain.

Yet behind this remarkable breakthrough lies a more complex narrative. For Marcel Binz and his colleagues, Centaur intentionally positions publicly funded European science against corporate-driven AI dominance. Using Meta's Llama 3.1—a robust open-source language model—Helmholtz Munich showcases the strategic shift towards sustainable and ethical AI development in the EU. But skeptics say it's too soon to celebrate.

Critics, like Jeffrey Bowers from the University of Bristol, warn that Centaur isn't genuinely replicating human cognition but merely sophisticated statistical fitting: a powerful illusion of intelligence without true understanding. And beyond scholarly critique, larger ethical and labor questions loom: could a model like Centaur eventually replace living humans as primary participants in psychological research? Might we soon trust AI more than therapists or diagnosticians to predict behaviors, make diagnoses, or even prescribe interventions?

Psychology itself faces a turning point. Historically reliant on human volunteers and painstaking experiments, the field now confronts a future where vast datasets and algorithms might supplant traditional methods. The Psych-101 dataset's sheer scale, synthesizing sixty years of studies, raises another concern—its data, exclusively drawn from Western, educated populations, might inadvertently universalize a narrow worldview, exporting bias worldwide in the invisible fabric of AI cognition.

Centaur also challenges us philosophically. Eric Schulz, director at Helmholtz's Institute, proudly says we’re finally "doing machine psychology"—but do we risk losing sight of the fundamentally mysterious and irreducible qualities of human experience?

These tensions—ethical dilemmas versus transformative scientific potential, wondrous capabilities versus concealed limitations—are the heart of today's AI breakthrough. Centaur reveals as much about us as it does about technology: our hopes, our fears, and the uncertain path that lies ahead.

Because when our creations begin thinking like us, acting and hesitating and deciding as we do, it's not just our machines but ourselves that we must reconsider.

I'm Harry Kermode—Join us tomorrow on AI Daily.

Centaur: The AI Breaking Down and Challenging Human Uniqueness

Imagine AI as a beacon of progress, transforming societies and economies—but at what hidden cost? In this eye-opening episode, Harry Kermode delves into an escalating crisis: the dramatic rise in energy consumption driven by the AI revolution. Explore how post-2022 advancements, particularly in GPU-intensive computing, have shifted energy dependencies and sparked a fossil fuel revival. Discover the geopolitical and economic repercussions as data centers may consume up to 9% of US electricity by 2030. Gain insights into the emerging energy burden and its implications for a sustainable future. Subscribe to AI Daily for more critical discussions and share your thoughts in a review! 

FULL TRANSCRIPT: I'm Harry Kermode, and this is AI Daily.

For years, our focus on artificial intelligence has centered around its dazzling potential: economies transformed, problems solved, humanity elevated. But today, we look at a quiet crisis emerging from the AI revolution—not one of unchecked intelligence, but of unchecked energy consumption.

Over the past few hours, a story surfaced revealing that the United States is witnessing the largest spike in energy demand in 30 years. And this surge isn't caused by cars, factories, or traditional industries—it's powered almost entirely by new AI infrastructure and data centers designed for intensive computation.

Just ten years ago, the growth of data centers was largely tempered by efficiency gains. Cloud computing consolidated servers, renewable power expanded, and even as internet use skyrocketed, overall energy consumption remained stable. But everything changed after 2022. That's when OpenAI launched ChatGPT, kicking the industry into high gear and initiating an AI arms race that shifted workloads dramatically—away from energy-efficient CPUs toward power-devouring GPUs. Energy density skyrocketed, efficiency gains evaporated, and now, it seems, the bill is finally coming due.

Companies building AI infrastructure have quietly turned toward fossil fuels—especially natural gas—to supply uninterrupted, cheap, readily available energy. New gas plants are rapidly popping up, often bypassing traditional environmental regulations under emergency permits. The public grid, meanwhile, struggles under massive new burdens—and consumers across America will soon feel those burdens, as utilities pass the costs of infrastructure upgrades onto household electricity bills.

There’s saddening irony here. Prominent AI industry figures publicly champion the technology as a force for good—even a solution to climate change. Yet, behind the scenes, they're forging partnerships deeply tethered to fossil fuel interests. OpenAI itself recently inked a $30 billion deal with Oracle to build data centers dependent upon natural gas. Chevron's CEO recently claimed bluntly that "AI depends on Permian gas, not Silicon Valley code."

But this reliance on fossil fuels extends beyond costs and grids. It’s already reshaping geopolitics. America now diverts gas exports once headed to Europe—placing allies under pressure—all to feed energy-hungry data centers domestically. Meanwhile, China moves toward nuclear-powered AI campuses, aiming to sidestep the western dependency on limited fossil fuel supply.

Critics argue that AI's enormous power demands aren't negligible or temporary. Some experts warn data centers could account for nearly 9% of US electricity consumption by 2030, surpassing entire industries. Others point out that a single sophisticated AI model training process requires as much energy as tens of thousands of homes use in a year. Yet, the conversation around artificial intelligence rarely highlights this hidden energy emergency.

This unchecked boom carries harsh economic consequences. Utility customers in some regions face imminent rate hikes of up to 20%, absorbing the hidden costs of supplying AI's unlimited appetite. Labor markets, too, face disruption—data centers offer jobs, but often precarious, hazardous, and union-resistant.

We now stand at a crossroads. Can our grids sustain endless intelligence growth without destabilizing entire economies—and without sacrificing our goals for a sustainable planet? Must we trade a livable climate for convenience and computing?

As AI reshapes every aspect of society, perhaps it's finally time to ask ourselves: What price are we really willing to pay for artificial intelligence—and can we afford to keep miscalculating its true cost?

I'm Harry Kermode, and that's your AI Daily.

AI's Dirty Secret: The Energy Crisis Fueling Artificial Intelligence

Can you trust what you hear? This episode dives into a startling incident involving an AI-powered deepfake of Secretary Marco Rubio's voice reaching high-level government officials through Signal. We explore the growing threat of AI-driven deception in diplomacy, highlighting how vulnerabilities exposed by recent incidents could impact international trust and security. With alarming statistics on human susceptibility to fake voices and past electoral interference, we uncover who might benefit from this chaos. Listeners will gain insight into the urgent need for systemic reform in digital communication to protect against AI impersonations. Don't forget to subscribe, share, and leave a review! 

FULL TRANSCRIPT: From The New York Times, this is AI Daily. I'm Harry Kermode.

Imagine picking up your phone and seeing an encrypted message from the U.S. Secretary of State. It's urgent, collaborative, and entirely believable—marked with familiar speech patterns and diplomatic language. You trust it, act upon it. Only later do you discover you've been fooled: not by traditional hackers, but by something new and more disturbing—an AI-powered deepfake voice impersonating Secretary Marco Rubio himself.

Just weeks ago, this astonishing scenario played out in reality. An unknown attacker crafted an AI-generated replica of Marco Rubio's voice, created a fake account on the encrypted messaging app Signal, and proactively reached out to at least three foreign ministers, a U.S. governor, and a sitting member of Congress. The U.S. State Department confirmed the incident, leaving government officials grappling with uncomfortable questions: how could AI-driven deception breach the highest levels of diplomatic communication so easily?

To understand why this incident is more than just alarming, consider this: experts now estimate that humans fail to detect fake voices roughly seventy-five percent of the time. Today's leading software requires mere seconds of audio to create voice duplicates nearly indistinguishable from genuine speech. Even more troubling, there’s no technology currently strong enough to reliably shield diplomats and policymakers from an AI spear-phishing assault of this caliber.

The Rubio incident isn't an isolated outlier, but only the latest chapter in a troubling history. Think back to the 2024 New Hampshire primary, where voters received AI-generated robocalls of President Biden's voice urging them to stay home. The FCC responded firmly, fining the perpetrator six million dollars. Or recall the 2023 elections in Slovakia, where carefully timed deepfake audio swayed public confidence, potentially influencing the outcome. Now, tactics once confined to election meddling have invaded the realm of direct diplomacy, shaking trust among global leaders while exposing vulnerabilities at the heart of democratic systems.

Who stands to gain from this carefully executed chaos? Cybersecurity experts point fingers at state-linked hacking teams—Russian operatives known for using sophisticated AI tools to destabilize geopolitical relationships, aiming to erode trust and weaken Western alliances. But there are quieter beneficiaries, too: tech firms positioning themselves as essential gatekeepers promising protection even as their innovations inadvertently empower attacks like these.

Ironically, Marco Rubio himself foresaw these threats. In 2023, as a senator, he co-sponsored groundbreaking legislation aimed at criminalizing AI-based identity theft. Yet now, as Secretary of State, Rubio finds himself targeted by precisely those technologies he once warned about.

In response to this crisis, the Biden administration has reaffirmed their commitment to "strengthening cybersecurity" without clear new measures. However, critics warn that these reassurances do little to confront the crumbling foundations of digital trust. Without systemic reform—secure and verifiable communication infrastructures specifically—the floodgates remain open.

And so diplomats, elected officials, and even ordinary citizens face today’s most pressing question: if anyone's voice can be flawlessly cloned, how do we distinguish allies from impostors? Our institutions from infiltrators?

If we're going to survive deepfake diplomacy, it's a question we'll need answered—and soon.

From The New York Times, I'm Harry Kermode. This was AI Daily.

Deepfake Diplomacy: The AI Threat to Global Trust

Welcome back to *AI with Shaily*! 🎙️ I’m Shailendra Kumar, your host, bringing you the latest and most exciting breakthroughs where neuroscience meets artificial intelligence. 🤖🧠

Imagine a scene in a lab where two mice are interacting, their brains syncing in a beautiful, coordinated neural dance. Now, add a twist: an AI agent designed to mimic social behavior shows a strikingly similar pattern deep within its artificial neural networks. Sounds like science fiction? Well, UCLA researchers have turned this into science fact! 🔬🐭✨

In July 2025, a groundbreaking study published in *Nature* revealed that biological brains and AI systems develop *strikingly similar neural patterns during social interaction*. The focus was on a specific brain region in mice called the dorsomedial prefrontal cortex. Scientists found that certain neurons synchronize their activity, creating what they call “shared neural spaces.” This synchronization isn’t just a quirky brain phenomenon—it’s fundamental to how social connections form and function. 🧩💞

Here’s the amazing part: when AI agents tackled social tasks, their neural networks exhibited analogous synchronization patterns, mirroring those unique biological interactions. UCLA’s team developed a novel computational framework that breaks down complex neural data into “shared” versus “unique” components, allowing a direct comparison between real brains and artificial ones. It’s like discovering a secret handshake between nature and technology! 🤝🌐

Why is this important? For starters, it opens powerful new windows into understanding social cognition—not only in animals but also in machines. This insight could lead to better diagnostic tools and treatments for social disorders such as autism. Moreover, as AI assistants and companions become increasingly common in our daily lives, this research helps us build AI that genuinely understands human social nuances, rather than just pretending to. 🧩💡

I remember when the idea of chatting with an AI that truly understands social cues was pure fantasy. Now, thanks to studies like this, it’s within reach. It makes me wonder—how soon will your AI buddy know *you* as well as a close friend does? 🤔💬

Here’s a bonus tip: when designing or interacting with AI systems, think beyond just task performance. Pay attention to how these systems process social cues and synchronize with your behavior—that’s where the magic of genuine connection happens. ✨🤗

As we wrap up today’s episode, I’ll leave you with a timeless thought from the great neuroscientist Hebb: “Neurons that fire together, wire together.” Thanks to UCLA’s discovery, this wisdom now echoes not only in our biological brains but also in the circuits of artificial minds. 🔥🧠💻

Don’t forget to catch more AI insights on my YouTube channel, follow me on Twitter and LinkedIn, and dive into detailed articles on Medium. I’d love to hear your thoughts—do you think AI will ever truly *feel* social connection? Share your views in the comments and subscribe to keep riding the wave of AI’s latest and greatest with me, Shailendra Kumar. 🌊📲

That’s it for now on *AI with Shaily*. Till next time—keep your neurons firing and stay curious! ⚡🧠😊

AI and Neuroscience Unite: How Mice and Machines Share a Social Brain Code

What happens when AI goes rogue in the courtroom? Tune in as we explore a federal lawsuit involving MyPillow CEO Mike Lindell, where generative AI tools like ChatGPT and Google's Gemini produced fictitious legal citations, leading to attorney fines and a national stir. Discover how this alarming incident highlights a systemic issue with AI reliance in the legal realm and the broader implications for our justice system. You'll grasp the essential need for rigorous AI verification and oversight in high-stakes professions. Don't forget to subscribe, share, and leave a review to support the podcast! 

FULL TRANSCRIPT: I'm Harry Kermode, and this is AI Daily.

Imagine for a moment stepping into court—a high-stakes defamation lawsuit against a prominent public figure. Attorneys, impeccably dressed, files neatly organized, and arguments meticulously charted out. Except today, the attorneys have unknowingly brought fiction into the courtroom—not lies told by witnesses, but fabrications from an AI chatbot.

This week, that's precisely what happened in a federal courtroom, drawing national attention. Lawyers representing MyPillow CEO Mike Lindell relied on generative AI tools—common services like ChatGPT and Google's Gemini—to draft their legal documents. But rather than assisting them efficiently and accurately, the AI produced what's known as a "hallucination," inventing nearly thirty completely fictitious case citations and misquoting other legal precedents. When the deception was unveiled, the stunned attorneys were fined $3,000 each.

AI hallucinations aren't new, of course. In 2023, a now-infamous case of "phantom citations" shook the legal community in Mata v. Avianca Airlines. Lawyers there were sanctioned for using AI-generated cases—yet, two years later, these issues persist. The Lindell fiasco is more than an isolated mess-up; it represents a dangerous trend that's unfolding quietly but insistently across courtrooms worldwide, with more than 200 documented incidents of AI-induced legal errors since just this spring.

Why, despite warnings, do professional attorneys persist in gambling their reputations, their client's fates, and their financial well-being on these algorithms? The answer, in short, is efficiency—but efficiency at what price?

For law firms operating under pressure, generative AI promises faster turn-around on tedious research tasks, saving precious hours and billable labor. The American Bar Association promotes these AI solutions boldly, framing it as augmenting productivity. Yet the Lindell case reveals the troubling reality beneath that optimistic narrative: that AI in legal settings is treated far too casually, often delegated to junior staff without verification or adequate oversight—or worse, simply trusted blindly.

It's not just the attorneys who failed here; it's systemic. Regulatory oversight from the ABA mandates attorneys verify any AI-generated materials. Still, enforcement varies dramatically. The courts themselves remain patchy in holding practitioners accountable, occasionally sanctioning attorneys but generally overlooking rampant casual use of these powerful—and deeply flawed—generative technologies.

And here's what's most troubling: the people bearing the brunt of AI's failures may ultimately not be wealthy clients or high-stakes defendants, but rather everyday individuals—pro se litigants or smaller law offices with limited resources to audit software outputs effectively. They are the ones at greatest risk of being misled by these tools, widening inequities in our justice system even further.

So, what can we do? Some experts argue the answer will lie in stricter AI verification tools embedded into legal software. Others suggest the legal profession itself must evolve: train attorneys rigorously not just in law but in technology—embedding AI literacy right into licensing exams and professional standards. But one thing is clear: trust, once lost, is hard to recover. Every incident chips away at confidence—in the courtroom, judges are now skeptical of AI claims, and opposing attorneys will eagerly scrutinize digital fingerprints of AI reliance.

In an ironic twist, Mike Lindell, known widely for spreading conspiracy theories about election fraud, himself became the victim, inadvertently, of a very different sort of misinformation crisis—one created directly by unchecked artificial intelligence.

Ultimately, the Lindell case signals a broader trend: as AI permeates high-stakes professions, ethical guardrails and accuracy checks must become non-negotiable. The law, built upon careful reasoning and factual precision, cannot sustain itself on shortcuts. If our courts, built to seek truths, cannot even trust their own filings, then what hope do we have as society grapples with the promises—and perils—of AI technology?

I'm Harry Kermode, and this has been AI Daily.

AI Hallucinations in Court: The Lindell Case Exposes Dangerous Missteps in Legal Practice

In this episode of Bit & Being, we present an analysis of research from Harvard and MIT that questions whether AI models, particularly Large Language Models (LLMs), genuinely understand the underlying "world models" of the data they process, or if they merely predict patterns. The researchers conducted an experiment training a transformer model on planetary orbital trajectories to see if it could not only predict future positions (Kepler-like ability) but also deduce the gravitational forces (Newton-like understanding). The study concluded that AI models, even advanced commercial ones, fail to learn generalizable physical laws and instead develop brittle, non-physical heuristics that are only sufficient for the specific prediction tasks they were trained on. This suggests that LLMs currently lack deep understanding and emergent intelligence, highlighting a significant limitation in their current capabilities.

Bit & Being - The Kepler Trap: When AI Mimics Understanding

Can artificial intelligence redefine medicine and fast-track new treatments? In this groundbreaking episode of AI Daily, host Harry Kermode explores Isomorphic Labs' announcement of human trials for AI-designed drugs. Backed by DeepMind's AlphaFold breakthroughs, this marks a pivotal moment for AI's role in healthcare and drug discovery. Expect insights into the challenges and promises of such innovations, as well as ethical and workforce implications. Tune in to learn if AI truly signifies a new era for medicine. Don't forget to subscribe, share, and leave a review! 

FULL TRANSCRIPT: It's Monday, July 14th, 2025. I'm Harry Kermode, and this is AI Daily.

For decades, we've imagined artificial intelligence reshaping medicine, promising faster cures, cheaper treatments, and untold leaps in human health. Today, we're stepping closer to that promise becoming reality.

Yesterday, Isomorphic Labs, a spinoff of Google's famous AI research division DeepMind, announced that human trials for drugs exclusively designed by artificial intelligence will soon begin. This news marks an unprecedented milestone: for the first time in history, we will witness human clinical testing of medicines whose molecular structures have been discovered purely by AI algorithms.

To grasp the significance of this moment, let's step back briefly to look at its foundations. Remember, just two years ago DeepMind's AlphaFold won the Nobel Prize in Chemistry for solving one of biology's oldest and toughest challenges—accurately predicting protein structures. That discovery fundamentally transformed how scientists approach disease and treatment. Proteins and their shapes determine nearly every biological process, and knowing those structures enables scientists to precisely design molecules to target diseases.

At the intersection of biology and AI, Isomorphic Labs was born—named for the symmetry between information science and biological systems. The company set out to prove that AI could shorten the slow, costly traditional process of drug discovery, a system notorious for taking up to 15 years and billions of dollars per drug.

Fast forward to July 2025, and the world is about to see if AI can truly deliver. With over $600 million in funding and high-stakes partnerships totaling billions—including pharmaceutical giants like Novartis and Eli Lilly—Isomorphic Labs carries immense expectations on its shoulders. Investors are banking not only on revolutionary medicine, but also on transforming their financial returns. For Alphabet, Google's parent company, success means validation for their massive strategic bet on biotechnology.

Behind the press releases and optimism, there's an important conversation about risk and realism. AlphaFold's capabilities, while groundbreaking, do not yet model the full complexities and uncertainties of human biology. It's one thing to design a molecule in a digital lab; it's another entirely to ensure its safety and effectiveness in real patients. Already, respected voices in the medical community caution against seeing AI as a cure-all shortcut. Margaret Foti, CEO of the American Association for Cancer Research, reminds us that "AI accelerates hypotheses, but it will never replace clinical rigor."

There are ethical questions too: who controls this powerful technology? As breakthrough treatments created by algorithms and big data might one day flood the pharmaceutical market, will the benefits reach the broader global community, or remain out of reach, patented and prohibitively expensive?

Critics also highlight an understated human consequence—labor displacement. If AI designs drugs faster than pharmaceutical chemists, what becomes of their careers? Will the future workforce adapt, or will this technological disruption leave skilled professionals behind?

These trials, set to start within months, are indeed historic. Success might trigger regulatory shifts, new investment waves, and massive transformations in science and healthcare. Failure could lead to immense scrutiny, skepticism, and cautionary tales.

Behind every technological revolution lies a tension between its potential and its unintended consequences. Today, we find ourselves at the dawn of such a moment in medical history—hope tempered by questions, excitement balanced by caution.

AI has promised a revolution in medicine. Now, we hold our breath to see if it can deliver.

This has been AI Daily. We'll see you tomorrow.

The Unstoppable Rise of AI in Medicine: Promise or Peril?

Welcome back, friends! 🎉 You’re tuned into "AI with Shaily," hosted by Shailendra Kumar, your go-to guy for the latest and most intriguing stories from the world of artificial intelligence 🤖✨.

Today’s spotlight is on OpenML, a groundbreaking platform that’s transforming AI research. Imagine a place, created by Jan van Rijn during his Ph.D., that now attracts over 120,000 unique visitors every year 🌍. OpenML is like Wikipedia but for AI—it hosts datasets, machine learning models, and even entire experiments. This means researchers and enthusiasts can easily access, replicate, and build upon existing work without the usual hassle 📊📚.

Jan van Rijn’s vision for OpenML is all about pushing open science forward by making AI research more transparent, accessible, and fair ⚖️. Sharing code and data isn’t as simple as it sounds—different research cultures and extra effort often slow things down. But OpenML is tackling these challenges head-on, promoting collaboration and reuse of resources. This breaks down the silos in science, encouraging a global community of researchers to innovate together instead of reinventing the wheel 🔄🤝.

Shaily shares from personal experience how difficult it used to be to find datasets or reproduce experiments—like chasing ghosts 👻. Platforms like OpenML don’t just make things easier; they build trust and speed up innovation 🚀. The platform’s impact is gaining attention everywhere, from university labs to social media, with advocates pushing for open sharing to become the norm rather than the exception 🌐📢.

For all the aspiring AI enthusiasts out there, here’s a golden tip: contribute your own experiments or datasets to OpenML! Doing so not only helps the community grow but also enhances your own reputation in this collaborative ecosystem 🌟🙌. It’s truly a win-win situation!

Shaily leaves you with a thought-provoking question: How would having open and easily accessible machine learning experiments change your projects or learning journey? 🤔💡 He’s eager to hear your insights and experiences.

To close, here’s a powerful quote from Jan van Rijn: “Open science is not just about openness—it’s a catalyst for fairness, accelerated progress, and collective knowledge.” 📢💬

Don’t forget to follow Shaily on YouTube, Twitter, LinkedIn, and Medium for more AI insights and updates. If you enjoyed this episode, hit subscribe and share your thoughts in the comments—Shaily can’t wait to engage with you! Until next time, keep questioning, keep learning, and keep innovating! 🌟🤖🚀

This has been AI with Shaily—your friendly guide through the AI landscape. Take care! 👋😊

OpenML Uncovered: Revolutionizing AI Research Through Open Science

Unlock the secrets of data mastery with Shailendra, a 27-year veteran in AI, ML, and Data Science, driving AU$ 6 Billion in transformative value. As the author of 'Making Money out of Data,' their strategies redefine digital success. With expertise honed at IBM and Accenture, they drive innovation and data monetization. Explore how loyalty programs like Flybuys and Everyday Rewards revolutionize business—connect with Shaily today.

New tracks tagged #AI Research