Speaker 1: AI daily breathing, I'm ed sharp, thanks for joining me today.
Speaker 1: The inference chip arms race. Why seven hundred million dollars
Speaker 1: flows to Etched, not models? Seven hundred million dollars. That's
Speaker 1: what just landed on a chip company most people outside
Speaker 1: Silicon Valley haven't heard of yet. Etched closed its Series
Speaker 1: D at a twenty one billion dollar valuation with Jane
Speaker 1: Street and Sequoia among the backers, and shipped its first
Speaker 1: production rack directly to Jane Street. That's not a demo,
Speaker 1: that's revenue, and it tells you exactly where the smart
Speaker 1: money thinks the AI bottleneck lives right now. It's not
Speaker 1: the models, it's the hardware running them. The thesis behind
Speaker 1: the Etched round is straightforward. As AI inference scales, throughput
Speaker 1: and memory architecture become the real competitive constraint. Building a
Speaker 1: bigger model is yetting cheaper. Running it fast enough at
Speaker 1: the right cost per query at scale is the harder problem.
Speaker 1: Etched is betting. The inference chip is where margin and
Speaker 1: mote get built. James Street taking the first wreck isn't
Speaker 1: just a vote of confidence. It's a signal that a
Speaker 1: sophisticated trading firm sees speed and cost per compute as
Speaker 1: strategically important enough to deploy capital twice over. Etch isn't alone.
Speaker 1: The Laura AI just raised one hundred and ten million
Speaker 1: dollars at evaluation above one billion to attack the power
Speaker 1: consumption side of the same problem, different layer, same thesis.
Speaker 1: Compute per watt is becoming as valuable as compute per dollar.
Speaker 1: The important distinction is that investors aren't just choosing between
Speaker 1: chip start ups. They're converging on a shared view that
Speaker 1: the AI economy's real infrastructure isn't the model there, it's
Speaker 1: everything underneath it. Stripe made the same bet differently its
Speaker 1: acquisition of open Router, the model routing platform spanning over
Speaker 1: four hundred models, reportedly landed somewhere between seven and eight
Speaker 1: billion dollars, depending on which figure you use. The strategic
Speaker 1: logic is clean. If AI applications are routing query across
Speaker 1: dozens of models based on cost, latency, and capability, then
Speaker 1: controlling the routing layer is controlling the billing layer. Stripe
Speaker 1: already owns payments infrastructure. Now it's extending that position into
Speaker 1: token economics. That's not a bolt on, that's a platform play.
Speaker 1: The most urgent story in this cycle doesn't involve funding.
Speaker 1: Greg Brockman's public warning about zipu AI's GLM dash five
Speaker 1: point three raises a genuinely difficult problem. The model reportedly
Speaker 1: scored eighty four point five percent on cybergym exploit benchmarks.
Speaker 1: Zipu delayed release after discovering unexpected cybersecurity capabilities. The catch
Speaker 1: is that delay is in recall once in open weight
Speaker 1: nodel ships, it ships everywhere. Here's the thing open AI's framing.
Speaker 1: Here is the defender's window. Formal verification tools that could
Speaker 1: authenticate AI behavior at the code level are years away
Speaker 1: from production scale hacking capable open weight models are weeks
Speaker 1: away from public release. That gap is the real exposure.
Speaker 1: The benchmark figure itself is self report. The benchmark figure
Speaker 1: itself is self reported and not independently verified, so some
Speaker 1: caution is warranted, but the structural problem doesn't depend on
Speaker 1: the exact number. The asymmetry between offensive accessibility and defensive
Speaker 1: readiness is real and widening. August second mark the point
Speaker 1: where EUAI Act compliance stopped being a planning exercise. Role
Speaker 1: based accountability, high risk classification requirements across employment, education and
Speaker 1: safety systems, and contractual allocation of liability are now active.
Speaker 1: Organizations that treated compliance as future facing policy work are
Speaker 1: finding out it's a procurement and contracting issue. Today, fines
Speaker 1: run up to fifteen million euros or three per cent
Speaker 1: of global turnover for general purpose AI violations. The timing
Speaker 1: is uncomfortable for one more reason. New research shows that
Speaker 1: the compliance detectors widely deployed across the guardrails market don't
Speaker 1: actually apply the rules they're trained on. They pattern match
Speaker 1: regulatory length, which then ignore it. Only chain of thought
Speaker 1: reasoning approaches appear to escape that failum mode. The signal
Speaker 1: here is that a two point one billion dollar guard
Speaker 1: rails market may be operating on architecture that can't satisfy
Speaker 1: enforcement grades. Screw to me. The execution gap isn't limited
Speaker 1: to Europe. In the uae AI spending rows one hundred
Speaker 1: and five percent by one measure, but only seven percent
Speaker 1: of enterprises have deployed autonomous workflows. Seventy seven percent cite
Speaker 1: data readiness as the primary barrier. That pattern large budget
Speaker 1: limited operationalization isn't unique to the region. It's the dominant
Speaker 1: enterprise AI story globally. Right now, consider this on a
Speaker 1: smaller scale, discovered materials closed a nine million dollar seed
Speaker 1: round to deploy AI agent swarms identifying firmly efficient chip materials.
Speaker 1: It's a narrow bit, but it addresses a constrain that
Speaker 1: pure compute scaling can't solve. Keat and energy density are
Speaker 1: physical limits. Finding better materials is a discovery problem. AI
Speaker 1: Accelerated Discovery is now a funded category. The through line
Speaker 1: across today's developments is that the competitive advantage in AI
Speaker 1: is shifting from who has the best model to who
Speaker 1: controls the infrastructure underneath it, and who can demonstrate that
Speaker 1: infrastructure is actually safe and lawful etched. The Laura and
Speaker 1: Stripe are building three different layers of that stack. The
Speaker 1: cybersecurity window and the guard rail research are both telling
Speaker 1: you that governance hasn't kept pace. The near term metrics
Speaker 1: to watch are James Street's inference performance data from the
Speaker 1: Edge Deployment ZIPU, AI's release decision on g L and
Speaker 1: DASH five point three, and the first wave of EU
Speaker 1: enforcement actions. Those three data points will tell you more
Speaker 1: about where this industry is actually headed than any benchmark announcement.
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