TSLA： What's the ER Show?
1.
TSLA Q3 EARNINGS
• Sales $28.1B vs Est. $26.4B
• EPS $0.50 vs Est. $0.54
• Gross Margin 18% vs Est. 17%
• Operating Income $1.62B vs Est. $1.65B
2.
Elon Musk basically turned the $Tesla Motors(TSLA)$ earnings call into a manifesto for control, chips & embodied AI.
He made it clear that he wants voting power locked in before scaling what he calls a “robot army.” That’s the subtext behind the November 6th vote.
On the product side Tesla positioning itself as both an AI compute company & a manufacturing company. The A5 chip, built with $Taiwan Semiconductor Manufacturing(TSM)$ & Samsung, is designed to be at least 2x more efficient and up to 10x cheaper per dollar than competitors, giving Tesla control over its own inference and training stack. That’s a massive shift from relying solely on $NVIDIA(NVDA)$ .
Optimus was the center of gravity on the call with Musk calling it the “infinite money glitch” arguing that embodied AI has no productivity ceiling. The real challenge, he said, isn’t intelligence but scale. No one has the supply chain for humanoids so Tesla’s vertical integration becomes the moat.
He also said robotaxi will go live in multiple states by year-end with 6 billion supervised miles already logged. That’s Tesla flexing its real-world data advantage.
The call made one thing obvious: Tesla is an AI infrastructure company building its own silicon, robots & autonomy network and Musk wants to make sure no one else can tell that system what to do.
3.
$Tesla Motors(TSLA)$ just confirmed they’re now building advanced AI chips in the U.S. with Samsung and expanded their in-house training stack to 81K H100 equivalents.
That’s Tesla quietly turning into one of the largest AI compute owners on the planet.
Technical diagram with text blocks explaining Tesla's FSD deployment in October for robotaxi services including emergency vehicle handling and complex scenarios. Details expansion of Bay Area both-hailing service and efforts for Robotaxi in Austin using universal model. Announces deal with Samsung for scaling semiconductors for AI inference and training bringing Cortex to total 81K H100 equivalents in US. Includes sections on artificial intelligence software and hardware with repeated emphasis on FSD model portions.
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