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Call out the contradiction between urgent safety warnings and rapid commercial releases.

Anthropic keeps releasing increasingly capable models every few months despite warning about existential risk — why should we trust the safety claims when the deployment timeline never actually slows down?

Frame the urgency as a governance speed problem, not a sci-fi apocalypse.

The reason to take AI safety seriously now isn't the Terminator — it's that the technology is moving far faster than democracy can regulate it.

Highlight specification gaming and value lock-in as the core alignment problem, not malevolence.

The real danger isn't a model that's actively evil — it's a model that perfectly follows a flawed objective its creators never fully thought through.

Challenge the gap between safety rhetoric and release decisions across the industry.

If we're honest, the largest AI labs are in an arms race where safety often becomes a marketing talking point rather than an operational constraint.

Push Dario on whether more compute and data can solve alignment or if there are fundamental limits.

Do you honestly believe scaling alone can get us to aligned AGI, or are we going to hit a wall we can't train our way out of?

Ask for a concrete, real example of dangerous behavior that hasn't been widely reported.

What's the most genuinely scary thing you've seen a frontier model do that the public doesn't know about?

Probe the psychological impact of superhuman persuasion and its societal risks.

What happens when a model becomes more persuasive than any human? How do we test for that and what would it do to elections, relationships, and free will?

Explore the timeline for AI automating AI research and the scaling of Anthropic's team.

How many years before a model can do the entire job of a top AI researcher — and what does that mean for Anthropic's own hiring and R&D?

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Hard Fork

The proposed California wealth tax on unrealized gains is deliberately structured to target tech founders by taxing the greater of their economic stake or voting control (e.g., supervoting shares), instantly bankrupting many even at a 5% one-time rate, and functions as a Trojan horse for broader annual asset seizures that expand via legislative patches. This mirrors the income tax's historical creep and is accelerating capital flight to low-tax states like Texas, Nevada, and Florida amid rising leftist populist policies.

Social media discourse is heavily distorted by undisclosed paid influencers and bots that inflate engagement metrics (e.g., high likes but low replies), creating a cat-and-mouse enforcement problem that platforms like X attempt to address via disclosure tags but cannot fully solve without laws. This dynamic fuels a growing cultural split between people who are "too online" (trapped in viral dramas) and "too offline" (reliant on mainstream narratives), independent of partisan divides.

AI systems function like prompt-driven 'Netflix script' generators with no inherent drives or self-preservation unless steered via latent space priming, while Chinese models explicitly score on Marxism and Xi Jinping Thought, revealing a tight US-China race where software leads in America but hardware robotics may favor Beijing.

What mechanisms or narrative shifts could effectively prevent the expansion of state-level wealth taxes into a national annual asset tax on unrealized gains, especially given social media echo chambers and paid influencer campaigns?

If neural interfaces and AI-enabled lie detection become widespread, how would that fundamentally alter trust, fraud, and authentic communication in politics and daily life?

If national AIs embed competing value systems like Marxism versus Western defaults, how will users or societies resolve conflicting outputs on issues like governance and resource allocation without the AI itself deciding?

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