Speaker 1: AI Daily Briefing, Amed Sharp, thanks for joining me today.
Speaker 1: Who gets to define AI safety? Hassabis versus the market
Speaker 1: demis Hassebis wants the US government to approve AI models
Speaker 1: before they ship. That's the sharpest version of what the
Speaker 1: Google DeepMind CEO proposed this week, calling for a federally
Speaker 1: overseen public private standards body modeled on FINGRA, the Financial
Speaker 1: industry regulator. The proposal includes mandatory thirty day pre release testing,
Speaker 1: federal funding, and a pathway from voluntary to mandatory compliance.
Speaker 1: It's specific enough to be taken seriously and ambitious enough
Speaker 1: to face serious resistance. The important distinction here is who
Speaker 1: this proposal serves. Hassebis frames it around AGI risk, citing cybersecurity, biological,
Speaker 1: and nuclear level threats from frontier models. That framing is genuine,
Speaker 1: But the company's best position to pass a thirty day
Speaker 1: federal review are the ones that already have safety teams, compliance, infrastructure,
Speaker 1: and regulatory relationships. That's Google deep Mind. The proposal doesn't
Speaker 1: just define AI safety, it defines who gets to be
Speaker 1: credible at it. The market's counter argument is speed. Any
Speaker 1: mandatory review window creates a gap that competitives, including state
Speaker 1: backed labs won't observe. Hassabis acknowledges this. His model asks
Speaker 1: industry to fund the body and phase in requirements gradually.
Speaker 1: What's still unresolved is whether voluntary compliance can hold long
Speaker 1: enough to become mandatory, and whether a federal body has
Speaker 1: the technical capacity to evaluate frontier models under a thirty
Speaker 1: day clock. Those aren't minor details, they're the mechanism. Here's
Speaker 1: what makes this moment distinct. Governance is no longer peripheral
Speaker 1: to AI strategy. The companies willing to embrace oversight are
Speaker 1: using it as a signal of legitimacy, and legitimacy is
Speaker 1: converting into enterprise contracts, government partnerships, and regulatory goodwill safety
Speaker 1: frameworks are becoming competitive assets. Apple cleared China's cyberspace administration
Speaker 1: after twenty two months. Apple Intelligence is now approved in
Speaker 1: the world's largest smartphone market, but the terms matter. It
Speaker 1: runs on Ali Barber's Qan and Baydu's models, not Apple's
Speaker 1: own stack. The cloud dependent features are still being negotiated,
Speaker 1: and both Ali Barba and Baidu are on the U
Speaker 1: S Defense Department Section one two six to zero H list,
Speaker 1: which flags firms with military links that creates political friction.
Speaker 1: Apple hasn't fully resolved. The signal for the rest of
Speaker 1: the industry is clear. Market access in China now requires
Speaker 1: handing technical control to domestic partners. Apple's playbook is already
Speaker 1: being studied by Western firms watching from outside. Operate through
Speaker 1: local models, or don't operate. Here's the thing. Moonshot, the
Speaker 1: Chinese AI startup, launched Kimi K three this week. It's
Speaker 1: an open weight model with two point eight trillion parameters
Speaker 1: and a one million token context window, and it's performing
Speaker 1: competitively with anthropics fable on benchmarks. That's a direct challenge
Speaker 1: to the assumption that Chinese lanfe aps trail Western frontier
Speaker 1: models by six months or more. The gap is narrowing,
Speaker 1: and Chemik three is free to deploy the cost and
Speaker 1: capability calculus for enterprise buyers just shifted. Moonshot is also
Speaker 1: raising two billion dollars at a thirty billion dollar valuation
Speaker 1: while releasing that model openly. The unit economics are transparent,
Speaker 1: yet that tension between open weight strategy and valuation sustainability
Speaker 1: is worth watching. Fireworks Ai closed a one point five
Speaker 1: billion dollar Series D at a seventeen point five billion
Speaker 1: dollar valuation The company processes forty trillion tokens daily and
Speaker 1: is running over one billion dollars in annualized revenue. The
Speaker 1: infrastructure layer is consolidating fast, and specialized platforms are proving
Speaker 1: more durable than early observers expected, and Thropic launched clawd reflect,
Speaker 1: an analytics dashboard that visualizes user AI patterns and suggests
Speaker 1: deeper clawed integration. The framing is productivity and mindful usage.
Speaker 1: The function is retention and competitive locking. Those aren't mutually exclusive,
Speaker 1: but they're worth distinguishing. In Japan, in Vidia's Jensen Huhan
Speaker 1: announced the Notre partnership with Sony and Honda, anchored by
Speaker 1: a twenty seven five hundred GPU national AI superfactory. The
Speaker 1: focus is physical AI and robotics. In Vidia also resumed
Speaker 1: a thirty year Sega Gaming partnership. Japan is positioning itself
Speaker 1: as a hardware AI manufacturing hub, and this is the
Speaker 1: infrastructure bet that underpins it. The thread across all of
Speaker 1: this is governance's leverage. HASSEBIS wants to define the standard.
Speaker 1: China already controls the gate. Anthropic is shaping behavior. Japan
Speaker 1: is building the infrastructure. The question of who defines AI
Speaker 1: safety isn't just philosophical. It's determining who operates, at what
Speaker 1: scale and in which markets. Watch whether the Hasabis proposal
Speaker 1: gains any congressional traction, and watch whether Apple's China approval
Speaker 1: survives its Pentagon friction. Those two data points will tell
Speaker 1: us a lot about where the governance frame actually holds.
Speaker 1: Thanksfully listening. This podcast was built using AI technology, A
Speaker 1: Yes We production