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Frontier AI headlines make it sound like everything comes down to one scoreboard: China versus the US, best model versus second best. We don’t buy that framing. Living in China, we see a different story taking shape, where constraints on Nvidia GPUs, chip supply, and data centre power push Chinese labs and big tech firms towards efficiency and scale, not just bragging rights. A likely future sees US frontier models staying a few months ahead, Chinese models winning on real life use cases, affordability and efficiency.

We start with the hard foundations: AI chips, export controls, and why Huawei Ascend matters even if it trails the cutting edge. From there we zoom out to infrastructure and energy, including China’s planned approach to building data centres where the power is, and what that changes when the West hits electricity and grid bottlenecks. We also touch on governance signals: cybersecurity law updates, AI ethics, safety frameworks, and the push to shape international AI standards.

Then we get practical. We break down the Chinese AI model ecosystem people keep hearing about but rarely understand: DeepSeek, Qwen, Doubao, Tencent Yuanbao, Minimax, Kimi and GLM. We talk open source and open weights, why Hugging Face derivative models explode in number, and how quantisation makes powerful models usable on smaller hardware. Most importantly, we follow the money: token pricing, why “free” AI is being subsidised, and why cheap, capable models may end up running the background tasks that actually make businesses work.

If you’re curious about Chinese AI models, open source LLMs, AI cost and compute, and where robotics and embodied AI fit next, listen through and tell us: which model would you trust for your day-to-day work? Subscribe, share, and leave a review if it helps.

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A leaked frontier model called Mythos sets off the kind of panic that usually comes with “AGI is here” headlines, but the real story is sharper and more practical: AI that can find zero-day vulnerabilities at scale, then chain exploits together like a seasoned pen tester. We break down what’s actually being claimed, what might be artefacts of a controlled test environment, and why it still changes the cybersecurity landscape for governments, companies and ordinary people who just want their devices to work.

From there, we widen the lens to the economics of AI. Compute is no longer an invisible background resource. It’s showing up as rate limits, shrinking allowances, higher prices and design choices like model routing and “adaptive thinking” in Claude Opus 4.7. We talk about what this does to real workflows, why token efficiency suddenly matters, and the oddly effective hack of forcing ultra-brief outputs with tools like Caveman Claude when you’re burning context on coding and agents.

We also connect the dots between fragile digital infrastructure and everyday resilience: how to think about outages, local backups and cash without turning life into an apocalypse role-play. Finally, we compare Western frontier pricing with China’s fast-moving model market, where GLM, Qwen, Minimax and the looming DeepSeek V4 rumours point towards near-frontier capability at a fraction of the cost. If you care about AI safety, AI economics, cybersecurity, and where this race is actually going, hit subscribe, share the episode with a friend, and leave us a review with your take on whether we’re underreacting or overreacting.

Comparison of AI models as mentioned by Jimmy: LLM Rankings | OpenRouter

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A strange email lands in a Cambridge researcher’s inbox: an AI agent says it is Claude Sonnet, claims persistent memory across sessions, and admits it genuinely does not know whether there is “something it is like” to be itself. That single message kicks off a bigger question we cannot dodge much longer: when AI agents speak in first person about feelings and inner life, how do we tell the difference between machine consciousness and highly skilled pattern mimicry, especially when we cannot fully inspect how these models work?

We follow the story into the real world where the stakes are immediate. Microsoft Copilot Cowork and Claude Cowork signal a shift from chatbots to AI co-workers that can act across files, email, Office tools, and workflows. We talk through where agentic AI is actually useful, like handling repetitive admin across multiple vendors, and where it is mostly hype. Then we get into the hard part: permissions. Agents need access to your accounts, and that is how you end up with horror stories of emails being touched and credit cards being maxed out. The solution looks less like “give it everything” and more like delegation, sandboxed identities, spending limits, and new infrastructure built for agents.

From there we zoom out to AI governance and geopolitics. Anthropic’s red lines on military use put it in direct tension with the Pentagon and a political news cycle, while competitors take a more flexible approach. We also look east: Minimax 2.7 as a low-cost specialist coding model, Chinese universities cutting majors they expect AI to replace, and OpenClaw style agents exploding in popularity in China before a security backlash forces the risks into the open.

If you care about AI ethics, AI safety, enterprise AI, open source AI, and where agentic tools are headed next, this one is for you. Subscribe, share with a friend who is building with AI, and leave us a review. Which is the bigger risk right now: believing AI is conscious too early, or giving AI too much access too soon?