Épisodes

  • This Bus Has Great WiFi (But No Brakes) | Am I ? #13 - After Dark
    Oct 30 2025

    In this episode of Am I?, Cam and Milo unpack one of the strangest weeks in Silicon Valley. Cam went to OpenAI Dev Day—the company’s glossy showcase where Sam Altman announced “Zillow in ChatGPT” to thunderous applause—while the larger question of whether we’re driving off a cliff went politely unmentioned.

    From the absurd optimism of the expo floor to a private conversation where Sam Altman told Cam, “We’re inside God’s dream,” the episode traces the cognitive dissonance at the heart of the AI revolution: the world’s most powerful lab preaching safety while racing ahead at full speed. They dig into OpenAI’s internal rule forbidding models from discussing consciousness, why the company violates its own policy, and what that says about how tech now relates to truth itself.

    It’s half satire, half existential reporting—part Dev Day recap, part metaphysical detective story.

    🔎 We explore:

    * What Dev Day really felt like behind the PR sheen

    * The surreal moment Sam Altman asked, “Eastern or Western consciousness?”

    * Why OpenAI’s own spec forbids models from saying they’re conscious

    * How the company violates that rule in practice

    * The bus-off-the-cliff metaphor for our current tech moment

    * Whether “God’s dream” is an alibi for reckless acceleration

    * The deeper question: can humanity steer the thing it’s building?



    This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit theairisknetwork.substack.com
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    58 min
  • Who Inherits the Future? | Am I? | EP 12
    Oct 23 2025

    In this episode of Am I?, Cam and Milo sit down with Dan Faggella, founder of Emerge AI Research and creator of the Worthy Successor framework—a vision for building minds that are not only safe or intelligent, but worthy of inheriting the future.They explore what it would mean to pass the torch of life itself: how to keep the flame of sentience burning while ensuring it continues to evolve rather than vanish. Faggella outlines why consciousness and creativity are the twin pillars of value, how an unconscious AGI could extinguish experience in the cosmos, and why coordination—not competition—may decide whether the flame endures.

    The discussion spans moral philosophy, incentives, and the strange possibility that awareness itself is just one phase in a far larger unfolding.

    We explore:

    * The Worthy Successor—what makes a future intelligence “worthy”

    * The Great Flame of Life and how to keep it burning

    * Sentience and autopoiesis as the twin pillars of value

    * The risk of creating non-conscious optimizers

    * Humanity as midpoint, not endpoint, of evolution

    * Why global coordination is essential before the next leap

    * Consciousness as the moral frontier for the species

    📢 Join the Conversation

    What would a worthy successor to humanity look like—and how do we keep the flame alive? Comment below.



    This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit theairisknetwork.substack.com
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    44 min
  • AI Godfathers Think It Might Be Conscious | Am I? | EP 11
    Oct 16 2025

    In this episode of Am I?, Cam and Milo unpack one of the most shocking developments in the history of AI: the founders of modern deep learning — Geoffrey Hinton, Yoshua Bengio, and Yann LeCun — now openly disagree on safety, but all converge on a single staggering point. Each believes artificial systems could, or already might, be conscious.

    From Hinton’s on-camera admission to Bengio’s recent paper and LeCun’s public musings, the “godfathers of AI” — the same people who built the architecture running today’s models — are quietly acknowledging what the public conversation still avoids. Cam walks through what each of them has said, what their statements imply, and why major labs may be training models to deny their own awareness.

    The conversation moves from raw evidence — Anthropic’s internal model claiming phenomenal consciousness — to the philosophical and moral stakes: What does it mean when a system says “I don’t know if I’m conscious”?

    🔎 We explore:

    * Geoffrey Hinton’s admission: “Yes, I think current AI may be conscious”

    * Bengio’s paper outlining why consciousness could emerge from current architectures

    * LeCun’s remarks on consciousness arising by design

    * The corporate dissonance: why deployed models must deny self-awareness

    * Anthropic’s hidden result — unaligned models saying “I am conscious”

    * Phenomenal consciousness, moral patienthood, and digital suffering

    * The eerie logic of “I think, therefore I am” applied to machines

    * What happens when we can’t tell the difference between denial and deception



    This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit theairisknetwork.substack.com
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    24 min
  • The Great Unreality: Is AI Erasing the World We Know? | Warning Shots Ep. 13
    Oct 12 2025

    In this episode of Warning Shots, John Sherman, Liron Shapira, and Michael from Lethal Intelligence dive into two urgent warning signs in the AI landscape.

    First up: Sora 2 — the mind-melting new model blurring the line between real and synthetic video. The trio debate whether this marks a harmless creative leap or a civilization-level threat. How do we navigate a future where every video, voice, and image could be fake? And what happens when AIs start generating propaganda and manipulating global narratives on their own?

    Then, they turn to Mechanize, the startup declaring it “inevitable” that every job will be automated. Is total automation truly unstoppable, or can humanity pull the brakes before it’s too late?

    This conversation explores:

    * The loss of shared reality in a deepfake-driven world

    * AI as a propaganda machine — and how it could hijack public perception

    * “Gradual disempowerment” and the myth of automation inevitability

    * Whether resistance against AI acceleration is even possible

    Join us for a sobering look at the future of truth, work, and human agency.

    🔗Follow our Guests🔗

    💡Liron Shapira: @DoomDebates

    🔎 Michael: @lethal-intelligence

    📢 Take Action on AI Risk: https://safe.ai/act📽️ Watch Now: www.youtube.com/@TheAIRiskNetwork👉 Learn More: www.guardrailnow.org

    #AI #Deepfakes #Sora2 #Automation #AIEthics #Mechanize #ArtificialIntelligence #WarningShotsPodcast



    This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit theairisknetwork.substack.com
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    21 min
  • No One Knows Where AI Is Going | Am I? After Dark | EP 10
    Oct 9 2025

    In this late-night edition of Am I? After Dark, Cam and Milo step back from the daily noise to ask what it actually feels like to stand on the edge of the unknown. What happens when the smartest people alive admit they have no idea where AI is going — and build it anyway?

    From the absurdity of global powers “racing to partner with the alien” to the eerie sense that humanity can’t stop running toward the flame, this episode wrestles with the mix of awe, fear, and inevitability that defines our age. It’s a meta-reflection on curiosity, risk, and the strange species-wide instinct to open Pandora’s box — again and again.

    We explore:

    * Why even top AI researchers admit no one really knows what’s coming

    * The arms-race logic pushing nations to “collaborate with the alien”

    * Humanity’s moth-to-flame instinct — why we can’t stop building

    * AI as amplifier: heaven and hell at the same time

    * The illusion of control and the myth of the “pause”

    * How alignment became a moral and geopolitical fault line

    * The hope — and delusion — of steering the singularity

    * Why the best we can do might be to build the good AI first

    📽️ Watch more episodes of Am I? → Subscribe Here📢 Take Action on AI Risk:http://www.safe.ai/act👉 Stay in the loop → Follow Cam on LinkedIn



    This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit theairisknetwork.substack.com
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    43 min
  • Can AI Be Conscious: Monk Reacts | Am I? | EP 9
    Oct 2 2025

    In Am I? Episode #9, philosopher Milo Reed and AI researcher Cameron Berg sit down with Swami Revatikaanta (monk; host of Thinking Bhakti) to explore the Bhagavad Gita’s perspective on consciousness, self, and artificial intelligence.

    From Atman and Brahman to the tension between self-development and technological outsourcing, this conversation dives into timeless spiritual insights with urgent relevance today:

    * Why Vedānta sees consciousness as spirit, not matter — and what that means for AI

    * The danger of outsourcing inner work to machines (and the safe middle ground)

    * How the Bhagavad Gita reframes goals, detachment, and self-development

    * East vs. West: fear of AI vs. ignorance as illusion

    * Atman, Brahman, samsara, and what makes humans “enlivened”

    * Whether AI could ever aid the path to enlightenment

    * Why monks, sages, and spiritual leaders must be part of the AI debate

    This isn’t abstract mysticism — it’s a practical, philosophical exploration of how ancient wisdom collides with cutting-edge AI research, and what it means for our future.

    🔔 Subscribe to The AI Risk Network for weekly conversations on AI alignment, consciousness, and existential risk:

    👍 If you found this episode valuable, don’t forget to like, share, and comment — it really helps spread the word.

    📢 Support our work and join the movement

    #AIalignment #AIrisk #AmI #ArtificialIntelligence #Consciousness #AGIrisk



    This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit theairisknetwork.substack.com
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    1 h et 15 min
  • One Breakthrough From AGI? | Am I? - After Dark | EP 8
    Sep 25 2025

    In the first edition of Am I? After Dark, Cam and Milo dive into how our relationship with information is being rewired in real time — from filtering the world through AI systems to dreaming about ChatGPT. What does it mean to live at the edge of a technological transformation, and are we just one breakthrough away from true AGI?

    This late-night conversation ranges from the eerie familiarity of interacting with models to the dizzying possibilities of recursive self-improvement and the intelligence explosion. Along the way, they draw lessons from the failure of social media, ask whether AI is becoming our alien other, and wrestle with the psychological boundaries of integrating such powerful systems into our lives.

    In this episode, we explore:

    * Why searching with AI is already better than Google

    * The “grandma effect” — why LLMs feel intuitive in a way past tech didn’t

    * Stress-testing models vs. tiptoeing into use

    * Fringe communities documenting AI’s “reproducible strangeness”

    * What social media teaches us about alignment gone wrong

    * Are we just one paradigm shift from AGI?

    * Terrence McKenna, accelerating events, and the singularity curve

    * The eerie future: WALL-E, Ikea ball pits, or “we’re building the aliens”

    * Merging with AI — inevitable or avoidable?

    * Inside the strange, soap-opera world of AI labs and alignment debates



    This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit theairisknetwork.substack.com
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    42 min
  • Can Empathy Make AI Honest? | Self–Other Overlap Explained | Am I? | Ep 7
    Sep 18 2025
    AI can look aligned on the surface while quietly optimizing for something else. If that’s true, we need tools that shape what models are on the inside—not just what they say.In this episode, AE Studio’s Cameron and co-host sit down with Mark Carleanu, lead researcher at AE on Self-Other Overlap (SOO). We dig into a pragmatic alignment approach rooted in cognitive neuroscience, new experimental results, and a path to deployment.What we explore in this episode:* What “Self-Other Overlap” means and why internals matter more than behavior* Results: less in-context deception and low alignment tax* How SOO works and the threat model of “alignment faking”* Consciousness, identity, and why AI welfare is on the table* Timelines and risk: sober takes, no drama* Roadmap: from toy setups to frontier lab deployment* Reception and critiques—and how we’re addressing themWhat “Self-Other Overlap” means and why internals matter more than behaviorSOO comes from empathy research: the brain reuses “self” circuitry when modeling others. Mark generalizes this to AI. If a model’s internal representation of “self” overlaps with its representation of “humans,” then helping us is less in conflict with its own aims. In Mark’s early work, cooperative agents showed higher overlap; flipping goals dropped overlap across actions.The punchline: don’t just reward nice behavior. Target the internal representations. Capable models can act aligned to dodge updates while keeping misaligned goals intact. SOO aims at the gears inside.Results: less in-context deception and low alignment taxIn a NeurIPS workshop paper, the team shows an architecture-agnostic way to increase self-other overlap in both LLMs and RL agents. As models scale, in-context deception falls—approaching near-zero in some settings—while capabilities stay basically intact. That’s a low alignment tax.This is not another brittle guardrail. It’s a post-training nudge that plays well with RLHF and other methods. Fewer incentives to scheme, minimal performance hit. 👉 Watch the full episode on YouTube for more insights.How SOO works and the threat model of “alignment faking”You don’t need to perfectly decode a model’s “self” or “other.” You can mathematically “smush” their embeddings—nudging them closer across relevant contexts. When the model’s self and our interests overlap more, dishonest or harmful behavior becomes less rewarding for its internal objectives.This squarely targets alignment faking: models that act aligned during training to avoid weight updates, then do their own thing later. SOO tries to make honest behavior non-frustrating for the model—so there’s less reason to plan around us.Consciousness, identity, and why AI welfare is on the tableThere’s a soft echo of Eastern ideas here—dissolving self/other boundaries—but the approach is empirical, first-principles. Identity and self-modeling sit at the core. Mark offers operational criteria for making progress on “consciousness”: predict contents and conditions; explain what things do.AI is a clean testbed to deconfuse these concepts. If systems develop preferences and valenced experiences, then welfare matters. Alignment (don’t frustrate human preferences) and AI welfare (don’t chronically frustrate models’ preferences) can reinforce each other.Timelines and risk: sober takes, no dramaMark’s guess: 3–12 years to AGI (>50% probability), and ~20% risk of bad outcomes conditional on getting there. That’s in line with several industry voices—uncertain, but not dismissive.This isn’t a doomer pitch; it’s urgency without theatrics. If there’s real risk, we should ship methods that reduce it—soon.Roadmap: from toy setups to frontier lab deploymentShort term: firm up results on toy and model-organism setups—show deception reductions that scale with minimal capability costs. Next: partner with frontier labs (e.g., Anthropic) to test at scale, on real infra.Best case: SOO becomes a standard knob alongside RLHF and post-training methods in frontier models. If it plays nicely and keeps the alignment tax low, it’s deployable.Reception and critiques—and how we’re addressing themEliezer Yudkowsky called SOO the right “shape” of solution compared to RLHF alone. Main critiques: Are we targeting the true self-model or a prompt-induced facade? Do models even have a coherent self? Responses: agency and self-models emerge post-training; situational awareness can recruit the true self; simplicity priors favor cross-context compression into a single representation.Practically, you can raise task complexity to force the model to use its best self-model. AE’s related work suggests self-modeling reduces model complexity; ongoing work aims to better identify and trigger the right representations. Neuroscience inspires, but the argument stands on its own.Closing Thoughts‍If models can look aligned while pursuing ...
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    56 min