Speaker 1: AI Daily Briefing. I'm ed sharp, thanks for joining me today.
Speaker 1: Space debris crisis gets commercial fix. The Pentagon just handed
Speaker 1: commercial space companies eight point four million dollars to solve
Speaker 1: one of the most urgent infrastructure problems in orbit. Firefly,
Speaker 1: Deorbit and Catalyst each received contracts to design spacecraft that
Speaker 1: can autonomously deorbit unprepared satellites, satellites that weren't built with
Speaker 1: any removal mechanism. That's the hard part, and it's never
Speaker 1: actually been demonstrated. Here's what matters about this move. The
Speaker 1: Pentagon isn't building a government fleet. It's buying a service
Speaker 1: paper deorbit, not own and operate. That's a deliberate shift
Speaker 1: of operational risk and capital cost onto private contractors. If
Speaker 1: this works, it validates an entire commercial space servicing industry
Speaker 1: estimated at three trillion dollars. If the autonomous capture fails,
Speaker 1: the debris claw keeps running. Right now, it's at two
Speaker 1: point eight days between significant conjunction events in low Earth orbit.
Speaker 1: That's not a slow moving problem. Shifting to AI infrastructure,
Speaker 1: Open AI shipped the GPT five point six family free
Speaker 1: variants soul Terror and Lunar the headline isn't raw capability.
Speaker 1: Its economics soul now matches GPT five point five extra
Speaker 1: high performance at a cost drop from thirty three dollars
Speaker 1: to one dollar and thirty three cents per million. Tokens,
Speaker 1: persistent reasoning, multi agent orchestration, and programmatic tool calling are
Speaker 1: all built in on financial research tasks. That combination is
Speaker 1: cutting token costs by around twenty one percent. The signal
Speaker 1: here is that model selection is changing. It's no longer
Speaker 1: default a flagship decision. Hybrid routing small plus media models
Speaker 1: for the right task is now cost competitive with yesterday's
Speaker 1: premium tier. That's an economics inversion, and enterprise teams will
Speaker 1: notice quickly. Meanwhile, Ali Barber's q N three point eight
Speaker 1: twenty seven billion parameter dense model has outperformed clawed opu's
Speaker 1: four point six max on s WE bench bro. The
Speaker 1: part worth watching is that it runs on a single
Speaker 1: twenty four bigabyte GPU and ships under APATCHE two point zero.
Speaker 1: Third party verification is still pending, so treat the benchmark
Speaker 1: with appropriate caution. But if it holds, the open weight
Speaker 1: frontier has reached parity with closed proprietary tiers that cost
Speaker 1: multiples more that's not a gradual trend. That's a supply
Speaker 1: shock to proprietary model positioning. On the security side, AI
Speaker 1: no taking platform TLM, AI no taking platform TLM DV
Speaker 1: left one hundred and eighty one thousand, eight hundred and
Speaker 1: seventy four meeting records queriable by any signed up user
Speaker 1: for six months, two million users affected. The access required
Speaker 1: no additional authentication. This isn't a sophisticated attack. It's a
Speaker 1: permission's failure, sitting undetected for half a year inside enterprise workflows.
Speaker 1: Here's the thing. The liability scope is still unquantified. GDPR exposure,
Speaker 1: customer retention impact, incident response adequacy. None of that is resolved.
Speaker 1: The broader implication is harder to ignore as AI tools
Speaker 1: capture more ambient workplace activity. The blast radius of a
Speaker 1: misconfiguration scales with adoption. That's the data security debt the
Speaker 1: industry is accumulating. At Crypto twenty twenty six, a new
Speaker 1: dedicated track formerly treats trained neural networks as mathematical objects
Speaker 1: subject to cryptanalytic attack. One hundred and eighty nine papers
Speaker 1: across ten volumes, a record model extraction attacks and water
Speaker 1: mark robustness now have rigorous formal frameworks. The practical consequence
Speaker 1: is that IP theft from model weights is no longer
Speaker 1: a loose threat. It's a defined attack surface with documented methods.
Speaker 1: Security teams at model providers need to be watching this closely.
Speaker 1: Two financial signals to close on, and Thropic posted ten
Speaker 1: point nine nine billion dollars in quarter two revenue, up
Speaker 1: one hundred and thirty percent year over year, with five
Speaker 1: hundred and fifty nine million dollars in operating profit, two
Speaker 1: years ahead of their own schedule. The important caveat is
Speaker 1: that a SpaceX bulk discount inflated those results. Q three
Speaker 1: will show whether enterprise growth holds without a single META
Speaker 1: customer carrying the margin. And open AI's S one filing
Speaker 1: is expected imminently, with a September listing target, two billion
Speaker 1: dollars per month in revenue, fourteen billion dollar projected loss
Speaker 1: for twenty twenty six, and Microsoft revenue share terms all
Speaker 1: disclosed ahead of the prospectus. The numbers are out, the
Speaker 1: interpretation is just beginning. The real watch points heading into
Speaker 1: next week whether the d orbit capture demonstration succeeds, what
Speaker 1: open AI's public filing actually reveals about its cost structure,
Speaker 1: and whether q wen's benchmark lead survives independent verification. Three
Speaker 1: separate proof points. Each one resolves a different kind of uncertainty.
Speaker 1: Thanks for listening. This podcast was built using AI technology.
Speaker 1: A Yes Sweet production