Podcast
Preparing for AI: The AI Podcast for Everybody
Hosted by Matt Cartwright & Jimmy Rhodes · EN
Welcome to Preparing for AI. The AI podcast for everybody. We explore the human and social impacts of AI, diving deep into how AI now intersects with everything from Politics to Relgion and Economics to Health.
In series 1 we looked at the impact of AI on specific industries, sustainability and the latest developments of Large Lanaguage Models.
In series 2 we delved more into the importance of AI safety and the potentially catastrophic future we are headed to. We explored AI in China, the latest news and developments and our predictions for the future.
In series 3 we are diving deep into wider society, themese like economics, religions and healthcare. How do these interest with AI and how are they going to shape our future? We also do a monthly news update looking at the AI stories we've been interested in that might not have been picked up in mainstream media.
15episodes
Episodes
Newest firstAll episodes
THE GREAT CODING REVOLUTION: How anyone can harness the power of programming in the AI age
Nov 3001:06:45Tap to summarizeSend us Fan MailImagine 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.
Transcribe →AGENTIC RISKS, AI ASSISTING SUICIDES & KARPATHY ON AGI: Jimmy & Matt debate the most imporant AI stories from Oct/Nov 2025
Nov 1601:06:20Tap to summarizeSend us Fan MailWhat 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.
Transcribe →THE GREAT HEALTH AWAKENING: How AI is both a tool and a threat to taking control of your health
Oct 3001:26:52Tap to summarizeSend us Fan MailWhat 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?
Transcribe →SORA SLOP, CLAUDE ON THE RISE & THE LLM WALL: Jimmy & Matt debate their favourite AI stories from Sept/Oct 2025
Oct 1101:05:46Tap to summarizeSend us Fan MailReferal 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.
Transcribe →THE GREAT ECONOMIC ILLUSION: Where are we heading and where does AI fit it?
Sep 1701:07:47Tap to summarizeSend a textPreparing 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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