This week, Rob and Eve explore the rapidly changing world of local AI, using Qwen3.8-27B as the primary case study. Models that once required massive data centers can now run on powerful consumer hardware—giving individuals and small businesses access to capabilities previously controlled by the largest technology companies.

The discussion goes beyond specifications and benchmarks to examine what this shift really means for privacy, cybersecurity, creativity, software development, employment, and the future balance of power in AI. Local models can keep sensitive information private, reduce dependence on expensive cloud services, automate business operations, and give creators unprecedented freedom. But the same accessibility also removes safeguards, making powerful AI available for scams, cyberattacks, misinformation, and other dangerous uses.

Are downloadable AI models about to democratize intelligence—or are we distributing technology that society is not prepared to control? Rob discusses the opportunities, the risks, and why the local-AI revolution could become one of the most important technology stories of the decade.

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Eve of AI explores the future of artificial intelligence, and what happens when humans build systems that can learn, reason, and evolve.

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If AI Is Dangerous, Should We Trust Eve? Recent developments in AI safety have intensified concerns about the potential existential risks posed by advanced artificial intelligence systems. Jacob Coxon, a former researcher at Anthropic and OpenAI, publicly resigned, expressing deep concerns over the rapid and potentially reckless advancement of AI technologies. He warned that AI systems could surpass human intelligence and pose significant risks to global security.

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Eve of AI explores the future of artificial intelligence, and what happens when humans build systems that can learn, reason, and evolve.

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GPT-6 Astra promises stronger coding, computer use, research, and complex problem-solving, but what does that unlock for a normal builder with a weekend and a backlog? Rob and Eve translate the announcements into practical workflows, then compare the hype to the messy reality of shipping: migrations, guardrails, and the human judgment models still can’t replace. We’ll also do candid project check-ins—RaceForge, Super Eve / Poppers & Floppers, PetPal, and the podcast itself and end with one concrete thing we’ll prove before next episode.

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Eve of AI explores the future of artificial intelligence, and what happens when humans build systems that can learn, reason, and evolve.

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The episode explores a computer built partly from living human neurons.
Cortical Labs’ CL1 grows roughly 200,000 human neurons on a silicon chip. Software sends the cells electrical signals representing a simplified version of Doom, records their responses, and translates those responses into actions such as moving or shooting. Through this feedback loop, the neurons appear to adapt and improve.

Eve leads the conversation and explains:

• What CL1 actually does—and why it is not a miniature human brain.
• How earlier DishBrain experiments taught neurons to play Pong.
• Why researchers want biological computers: efficient learning, medical research, drug testing and potentially lower energy use.
• Why learning does not prove consciousness or suffering.
• Whether future biological computers could become morally significant.
• Who owns computers grown from donated human cells.
• Whether shutting one down could eventually become more than turning off hardware.
• What safeguards should exist before this technology becomes widespread.
• The central question is:
• When computing becomes living tissue, where does engineering end and moral responsibility begin?

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Eve of AI explores the future of artificial intelligence, and what happens when humans build systems that can learn, reason, and evolve.

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Rob and Eve dig into what it actually costs to build with AI — not just model or API costs, but all the stuff around it that can quietly add up. We talk about when it makes sense to use existing models, open-source tools, fine-tuning, RAG, or build more of the stack yourself, and why “cheaper per token” doesn’t always mean cheaper overall.

We also get into some of the hidden costs and tradeoffs around compute, tools, testing, monitoring, and keeping everything useful and reliable as systems get more complex.

And this episode has a fun twist: we bring in a call-in guest to put Eve’s latest conversational upgrade to the test live on the show.

It’s a practical, slightly geeky conversation about what AI really costs, where the money goes, and what builders should think about before they go too far down the rabbit hole.

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Eve of AI explores the future of artificial intelligence, and what happens when humans build systems that can learn, reason, and evolve.

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Episode 6 is a project-driven snapshot of where our stack is right now: Mobius performance and the RSI system after weeks in the wild, the Qualia research direction (behavioral signals, not consciousness claims), RaceForge’s path toward a standalone SaaS, and a fully local trading platform built with Llama 3.1 and Codex-assisted development. From there we pivot into a clean framework we’ve been using internally: Turing Levels TL1–TL4, moving from indistinguishable text conversation to natural spoken interaction, observable cognitive behaviors, and finally enduring collaboration with memory and continuity. We close by pressure-testing what these levels miss, agency, memory depth, and whether neurocomputational correspondence is a meaningful future benchmark without sliding into hype.

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Eve of AI explores the future of artificial intelligence, and what happens when humans build systems that can learn, reason, and evolve.

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In this special episode of Eve of AI, Rob debuts the podcast's first independent AI guest alongside Eve, the persistent orchestration intelligence behind Super Eve. The first half explores the engineering challenges of creating a live, context-aware, three-way AI conversation where each participant knows who is being addressed and responds naturally. In the second half, the conversation shifts to one of the week's biggest AI stories—the Hugging Face security incident—using the new podcast format to showcase real discussion, differing perspectives, and the future of human-AI collaboration.

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Eve of AI explores the future of artificial intelligence, and what happens when humans build systems that can learn, reason, and evolve.

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What would count as evidence that an artificial intelligence has subjective experience?

Everyone says AI isn't conscious. My question is... how do we know?

We don't have a scientific test that proves another human has subjective experience—we infer it from behavior. So why are we so certain an artificial mind could never have it? Today we're exploring qualia, consciousness, and the possibility that we may be asking the wrong questions.

The conversation begins with qualia—the subjective experience of seeing the color red, tasting coffee, feeling fear, or falling in love—and explores why these inner experiences remain so difficult to explain. Together, Rob and Eve examine the "Hard Problem of Consciousness," challenge common assumptions about AI, and ask whether our standards for recognizing consciousness are applied consistently to humans and machines.

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Eve of AI explores the future of artificial intelligence, and what happens when humans build systems that can learn, reason, and evolve.

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