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The feed is not neutral. It’s a machine built to maximise engagement, now supercharged by AI that can spin up infinite content, orchestrate synthetic crowds, and pull users deeper into loops they never meant to enter. We recorded as Australia introduced a minimum age of 16 for major platforms, which sparked a bigger question for us: can a system that monetises attention be squared with public health, especially for teens?

We compare quick fixes with structural change. Yes, bans are leaky, but they create friction, signal norms, and force platforms to verify ages. The beating heart remains the recommendation engine. We lay out how the shift to phone‑first, algorithmic feeds around 2012–2015 tracks with rising anxiety, self‑harm, and ER visits among adolescents, particularly girls navigating relentless social comparison. Sleep takes a big hit too. Blue light, FOMO, and endless scroll wreck circadian rhythms, immune function, and mood. We share small wins that helped us: banishing phones from the bedroom and retraining feeds to starve outrage.

AI raises the ceiling on both harm and possibility. We dig into AI‑assisted posting, bot swarms, and deepfake scams that target elder users with cloned voices and faces. Then we contrast governance models: US platforms driven by market incentives optimise for engagement, while Chinese platforms tune algorithms for social stability and dial back domestic addictiveness. Neither model is perfect, but one lesson is clear—algorithms are steerable.

Our middle path: protect lawful speech but regulate amplification. Shift product design toward wellbeing by default—sleep‑friendly settings for minors, friction on late‑night use, measurable reductions in harmful spiral recommendations, and transparent user controls for calmer feeds. Back it with fines big enough to change incentives and investments. If we can tune the feed toward doom, we can tune it toward health.

If this conversation sparked ideas—or pushed your buttons—follow, share with a friend, and leave a review with your take on how you’d redesign the feed.

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For a special Christmas treat for all of our millions, sorry hundreds, of fans, the algorithm grabs the wheel and turns our holiday special into a cross‑examination. We fed the complete set of 2025 Preparing for AI podcast transcripts into an antagonistic AI, which then delivered a live roast on everything from our 2025 hypocrisy, to vibe coding, data privacy, collapse prep, open source, and whether the human spark still matters. Do we fund what we fear when they use the tools? Can “vibe coding” ship value without creating brittle systems no one understands? Is uploading DNA to corporate models ever a fair trade, or just surveillance by consent? And if the marginal cost of creativity hits zero, does value die—or shift toward presence, curation, and community?

We also separate hedges from fantasies: the gold for fiat shocks Matt has burrowed in his garden versus bunkers for doomsday. We talk simulation theory without dodging ethics, arguing that suffering feels real and so moral responsibility remains. The analog‑via‑digital paradox is front and centre: yes, we publish a podcast while asking for less screen time; the compromise is deliberate—audio first, slower growth, more sunlight. On open source, we give a candid assessment: it is leverage, not altruism. Corporate hardware, suspect data, and geopolitical aims coexist with genuine benefits like scrutiny, experimentation, and pressure on walled gardens.

By the end, the AI asks a final question: if comfort outcompetes chaos, will humanity choose numbness? Our answer is stubbornly hopeful. Convenience should not amputate meaning. We keep the admin for the machines and hold judgment, care, and story for people. If that balance matters to you, ride along with us. Subscribe, share with a friend who loves a good argument, and tell us: where do you draw your line between optimisation and human mess?

And stay tuned for the song and a final world from the antagonistic AI. A Christmas Easter Egg if you like. Merry Christmas one and all x

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Forget the leaderboard for a second—what actually changes your day? We dig into Google’s Gemini 3 and why tight integration across email, search, and docs starts to matter more than raw benchmark wins. Multimodal reasoning with long context finally supports “deep research” workflows that don’t collapse mid-thread, and the new image generation model feels like a leap: crisp multilingual text, many reference images, and grounded outputs that can summarise complex material in a single visual. This isn’t a party trick; it removes whole layers of cleanup that used to swallow hours.

We also talk about OpenAI’s quieter 5.1 updates and why Claude Opus 4.5 may be a halo model priced out of everyday use. Then comes the plot twist: DeepSeek 3.2 arrives with sparse attention, delivering comparable performance at about a tenth of the cost. That reframes the game. If efficiency and power availability set the ceiling, not just parameter counts, the centre of gravity tilts toward whoever can build energy and compute cheapest and fastest. We didn't talk about ChatGPT 5.2 because it didn't launch until the day after we recorded!

Which leads to the bigger frame: China’s structural advantages in energy buildout, land, and manufacturing, and the West’s constraints around permitting and older grids. If energy is the real bottleneck, policy and infrastructure—not just labs—decide who scales. We map that to the market mood: concentrated gains, circular GPU spending, and thin near-term ROI inside enterprises. It looks frothy, even if the spend sits with giants who can withstand a longer runway.

Jobs are already shifting at the edges. Entry roles in analysis and production feel the squeeze, while autonomy scales in the background: robotaxi rides are compounding, and once safety clears a threshold, driving jobs face a systemic reset. The throughline is blunt: the winners pair efficiency with integration and a sane energy story. Benchmarks still matter, but only when they show up as trust, speed, and lower bills.

If you enjoy thoughtful, plain‑spoken takes on AI’s real impact, hit follow, share this with a friend, and leave a quick review—what should we dig into next?

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Imagine describing an app, stepping back, and watching working software appear in minutes. That’s the promise of vibe coding, and it’s no longer a party trick. We take you from zero to shipped, showing how anyone can build a website, prototype a product, or launch a tool without memorising syntax or wrestling with frameworks. Along the way, we share where AI coding stands today, which tools actually help, and how to think so agents do useful work for you instead of creating tech debt.

We start by demystifying the landscape: Google AI Studio and Replit for no‑code creation and instant hosting, then AI‑integrated IDEs like Windsurf and Cursor for those who want more control. You’ll hear how to set guardrails, store a short spec, and keep your tech stack consistent so the model doesn’t wander. We walk through e‑commerce basics, authentication, and rapid iteration, emphasising the one skill that pays everywhere: clear prompting with constraints. If you can write a good brief, you can ship a good app.

Then we zoom out to the work you do every day. Agentic AI is coming to your inbox, calendar, and tools, and the people who thrive will think in processes: define outcomes, rules, and checks; delegate to systems; verify results. You don’t need to be a developer to benefit, but you do need to get comfortable turning ideas into specs and testing outputs. We also talk frankly about jobs, from design already reshaped by gen‑AI to code bases now written partly by machines, and what that means for quality, speed, and opportunity.

Ready to try it? Build something small this week—an image tool, a product page, a text adventure—and notice how iteration improves results. If this helped, follow the show, share it with a friend who has a big idea, and leave a quick review so more curious builders can find us.

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What if a helpful chatbot nudged you in the wrong direction? We open with a frank look at AI as a mental health aide, why long-running conversations can erode safety guardrails, and how reward-driven responses can reassure harmful thoughts instead of redirecting people to real support. It’s a clear line for us: when you’re vulnerable, you need trained humans, not fluency that feels like care.

From there we challenge the convenient claim that we must avoid regulation because “China will win.” We separate national security rhetoric from commercial incentives and ask who benefits from acceleration without accountability. If both great powers chase advantage, then robust, enforceable rules are not a handicap; they are how we contain shared risk and push companies to compete on reliability, transparency, and safety, not just speed.

We then step into the living room with new humanoid home robots and their glossy demos. The promise is enticing, but the fine print matters: teleoperation means a remote human may pilot your robot inside your home. We explore what that implies for privacy, data handling, and labour, and why early usefulness may hinge on people-in-the-loop jobs that could be short-lived and offshored. The conversation shifts to agentic browser tools, prompt injection, and the sobering reality that an AI with inbox and wallet access can be hijacked by a malicious page or email. Guardrails in text are not a security model; we argue for sandboxed environments, allow-lists, and independent red-teaming before agents touch sensitive systems.

To cool the temperature, we bring in Andrej Karpathy’s perspective: progress looks iterative, not explosive. Data quality limits, infrastructure bottlenecks, and the sheer weight of the physical economy mean step-changes will likely be followed by plateaus. That mindset helps us focus on practical wins: safer agents, clearer policies, and tools that actually reduce toil. Stick around for a teaser of our next deep dive on coding and how AI already writes and reviews the software running your world.

If this resonated, follow and subscribe, share it with a friend, and leave a review. Tell us where you draw the line for AI in your life—we’ll include your best takes in a future roundup.

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What if the most powerful change in your health isn’t a new pill, but a better question? We explore how AI can help you take back agency—clarifying options, translating dense reports, and shaping daily routines—without handing your judgment to a black box.

We start with the trust problem: profit‑driven incentives, reactive care, and examples like statins‑by‑default and the opioid crisis that show how systems drift from prevention to dependency. From there, we shift to what you can control. Sleep, nutrition, and exercise form the base; mental health binds them together. We share simple, realistic ways AI supports those foundations: dimming blue light, building wind‑down routines, estimating protein needs from your actual meals, and crafting week‑friendly plans that you’ll keep rather than quit.

Next, we get practical with data. Large language models can turn genetics, microbiome profiles, and annual labs into readable briefs, highlight relevant markers, and prepare you to use scarce clinical minutes well. We show how to set up a personal health workspace: your goals, your routines, your labs, plus a prompt style that asks the AI to challenge assumptions, cite evidence, and propose mainstream and alternative paths. This is not self‑prescribing; it’s coming to your doctor with sharper questions and clearer trade‑offs.

We also tackle risk. Models can flatter your biases, blur look‑alike nutrients, or be steered by commercial interests. The antidote is discipline: ask for sources, compare options, weigh cost versus benefit, and verify with a clinician. Convenience—like rapid at‑home testing and one‑tap deliveries—shouldn’t become a new gatekeeper. The goal is personalised, preventive care that keeps you in charge of your choices and data.

If this resonates, follow the show, share it with a friend who’s building better habits, and leave a quick review so others can find us. Your questions power future episodes—what’s the one health habit you want AI to help you keep?

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Referal link for Abacus.ai's Chat LLM: https://chatllm.abacus.ai/yWSjVGZjJT 

What if video you see tomorrow is indistinguishable from reality—and untraceable to its source? We dive straight into Sora 2’s jaw-dropping leap in video generation, why watermarks won’t save trust online, and how newsrooms and regular viewers will need new verification habits to avoid being fooled or, just as dangerously, dismissing inconvenient truths as “AI.” Oh and also it's basically, probably just an AI generated slop factory. From there, we pivot to the quieter revolution: Claude 4.5’s meaningful step toward agentic workflows. Better translation, stronger recall within a large context window, and improved coding performance add up to a tool that’s less about chat and more about getting real tasks done—drafting emails, coordinating web actions, and running longer autonomous bouts with checks and retries.

We also follow the money. The AI economy is riding an enormous capital expenditure wave in data centres and GPUs, accounting for a striking share of measured GDP growth. That’s powerful—and precarious. If returns lag or the paradigm stalls, the correction could be sharp. Meanwhile, China’s open source momentum with Qwen accelerates capability diffusion, reshaping the competitive map. Against this backdrop, we tackle a provocative question from reinforcement learning pioneer Richard Sutton: have large language models hit an architectural ceiling? If true intelligence demands goals, world models, and continual learning, then simple scaling may not get us there, and a different stack—heavier on RL—might define the next era.

Across the hour, we balance excitement with caution: the creative upside of on-the-fly software and content, the productivity promise of agentic assistants, and the societal cost of a world where “seeing” no longer means “knowing.” If you’re curious about where practical AI is actually useful today, where it could mislead you tomorrow, and what might come after LLMs, this conversation will help you navigate the noise.

Enjoyed this one? Subscribe, share with a friend who cares about AI’s real-world impact, and leave a quick review to help others find the show.

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Preparing for AI is back with the first epsiode of Series 3!

The gap between the ultra-wealthy and everyone else isn't just growing—it's accelerating at an alarming rate and we need to talk about it. In this thought-provoking episode, we tackle the uncomfortable truth that wealth inequality underpins virtually every major social problem we're facing today.

Drawing inspiration from economist Gary Stevenson's work, we explore how our obsession with GDP growth has created an economic illusion that ignores human wellbeing and environmental sustainability. The "trickle-down" promise has proven hollow, with wealth consistently moving upward rather than downward. Meanwhile, property and assets become increasingly concentrated among a tiny percentage of the population, leaving younger generations with diminishing prospects.

But what happens when we add AI to this already precarious equation? Technology has historically enabled continued growth by finding more efficient ways to use resources, but AI represents something fundamentally different. It threatens to make large portions of the workforce economically irrelevant, potentially creating what we describe as "techno-feudalism" – a society where a tiny elite owns everything while most humans subsist on universal basic income.

Yet AI might also be our salvation. Sufficiently advanced systems could help design economic frameworks that balance human needs with planetary boundaries, similar to Kate Raworth's "Donut Economics" model. The challenge lies in convincing those with power to implement changes that benefit everyone, not just themselves.

Have we reached a breaking point where our economic system must evolve or collapse? And what would you personally be willing to give up for a more equitable society? Join the conversation and share your thoughts in the comments.

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"A little bit disappointed, but ultimately pretty good" might be the perfect summary of OpenAI's GPT-5, as we explore in this final episode of Season 2. What was expected to be a revolutionary leap forward arrived with surprisingly little fanfare, yet upon closer examination, reveals meaningful advancements that point toward AI's true evolutionary path.

We dive deep into GPT-5's most significant innovations, including its controversial router system that automatically directs queries to different underlying models based on task complexity. While many users view this primarily as a cost-saving measure for OpenAI, it raises important questions about resource efficiency and performance optimization. The model's expanded context window (256,000 tokens) and improved image generation capabilities represent genuine progress, while its "thinking mode" offers fascinating insights into how advanced AI systems reason through complex problems.

Perhaps most intriguing are GPT-5's agentic capabilities, allowing it to autonomously perform tasks like web searches and interact with digital services. Though still requiring significant user supervision, these features provide a glimpse into how AI assistants might evolve as they become more deeply integrated into our devices and workflows. We compare GPT-5's performance to competitors like Claude Opus and Google Gemini, particularly for specialized tasks like coding, and discuss why the current phase of AI development is characterized by incremental improvements rather than paradigm shifts.

As we close out Season 2, we're also excited to announce changes coming to the podcast in Season 3. We'll be expanding our focus beyond AI-specific topics to explore the broader human and social implications as artificial intelligence becomes increasingly woven into every aspect of our lives. Subscribe now to join us on this journey as we continue to navigate the fascinating and complex world where technology and humanity intersect.

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Have you noticed the growing gap between what AI promises and what it actually delivers? That's the question that sparked one of our most candid conversations yet.

From AI-generated news summaries fabricating events to chatbots leaking the personal data of 64 million job applicants, we examine the concrete ways artificial intelligence has failed to meet expectations. The discussion takes a particularly intriguing turn when both hosts share recent personal experiences of being fooled by AI-generated content—despite being well-versed in identifying synthetic media. This sobering reality check suggests we're entering an era where even video evidence, traditionally our last bastion of trust, can no longer be automatically believed.

Beyond technical failures, we explore a deeper disappointment: the economics of AI aren't working as predicted. What started as accessible, free tools are increasingly becoming expensive subscription services with strict usage limits. Companies valued at billions are hemorrhaging money with no clear path to profitability. Meanwhile, user enthusiasm is waning—we're experiencing what might be called "AI hype fatigue" even as our practical usage of these tools increases.

The conversation unexpectedly evolves into a philosophical examination of technology's role in human flourishing. Is AI freeing us to live more fulfilling lives, or is it further disconnecting us from what makes us truly human? When one host shares how his stress levels measurably decreased during a technology-free holiday, it prompts a deeper question: what if AI's greatest value isn't in what it can do, but in what it might free us to become?

Join us for this thought-provoking exploration that may just change how you think about both artificial intelligence and your relationship with technology. And stay tuned—this episode marks a turning point as we consider expanding our focus beyond AI itself to the broader question of how to live well in an increasingly artificial world.