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Joby's vertical integration and Toyota partnership are key to mass production - By controlling the manufacturing stack and leveraging Toyota’s high-reliability standards, Joby is scaling to produce aircraft that are 100x quieter and significantly safer than traditional helicopters.
“There's going to be 12 states across the country where we're going to be able to test these next generation technologies... [and] buy a ride... as soon as the end of this year.”
- •
Commercial air taxi services are launching in 12 states under new FAA programs - The EVTOL Integration Pilot Program (EIPP) has accelerated regulatory approval, potentially allowing the public to pay for rides as early as the end of this year.
“With electric propulsion, you remove the cost of the fuel and then you dramatically reduce the maintenance... we have six propulsion stations and each of those propulsion stations is driven by two separate motors, two separate battery packs.”
- •
AI and hydrogen are providing 10x gains in aviation engineering and range - AI-driven design is radically increasing aerodynamic productivity while new hydrogen-based propulsion systems promise to extend flight range at a much lower cost than batteries alone.
“You take one of the greatest aerodynamic minds on the planet and you enable him with something that makes him 10x as productive. The benefits compound in a crazy way.”
LEVERAGE AI
All podcast episode summaries matching LEVERAGE AI — aggregated across every podcast we track.
“One of the engineers on our team was looking at a reported material property. And it was just sort of extracted values from literature. It was really interesting to see the reported value spanned many orders of magnitude. And so you train an ML system on that and it's like, well, the best you can do is model this distribution, but you're no closer to like a ground truth.”
- •
Scientific AI requires a closed-loop interface with the physical world - progress in material science is bottlenecked by the inability of purely digital models to conduct experiments and learn from real-world feedback loops.
“Science ultimately isn't sitting in a room thinking really hard. You have to conduct experiments. You have to learn from them. You have to interface with reality.”
- •
Domain-specific labs should leverage existing LLM priors - Periodic Labs focuses its R&D exclusively on material science while utilizing third-party foundation models for coding and general reasoning to accelerate development.
“Periodic spends zero effort on improving coding models. We're incredibly impressed by Codex, Cloud Code and so that's been a huge accelerator for the company.”
- •
Physical experimentation provides the ground truth missing from literature - because scientific papers often contain noisy or contradictory data spanning multiple orders of magnitude, physical labs are required to ground ML models in reality.
“One of the engineers on our team was looking at a reported material property. And it was just sort of extracted values from literature. It was really interesting to see the reported value spanned many orders of magnitude. And so you train an ML system on that and it's like, well, the best you can do is model this distribution, but you're no closer to like a ground truth.”
- •
Scientific AI requires a closed-loop interface with the physical world - progress in material science is bottlenecked by the inability of purely digital models to conduct experiments and learn from real-world feedback loops.
“Science ultimately isn't sitting in a room thinking really hard. You have to conduct experiments. You have to learn from them. You have to interface with reality.”
- •
Domain-specific labs should leverage existing LLM priors - Periodic Labs focuses its R&D exclusively on material science while utilizing third-party foundation models for coding and general reasoning to accelerate development.
“Periodic spends zero effort on improving coding models. We're incredibly impressed by Codex, Cloud Code and so that's been a huge accelerator for the company.”
- •
Physical experimentation provides the ground truth missing from literature - because scientific papers often contain noisy or contradictory data spanning multiple orders of magnitude, physical labs are required to ground ML models in reality.
“One of the engineers on our team was looking at a reported material property. And it was just sort of extracted values from literature. It was really interesting to see the reported value spanned many orders of magnitude. And so you train an ML system on that and it's like, well, the best you can do is model this distribution, but you're no closer to like a ground truth.”
- •
Joby's vertical integration and Toyota partnership are key to mass production - By controlling the manufacturing stack and leveraging Toyota’s high-reliability standards, Joby is scaling to produce aircraft that are 100x quieter and significantly safer than traditional helicopters.
“There's going to be 12 states across the country where we're going to be able to test these next generation technologies... [and] buy a ride... as soon as the end of this year.”
- •
Commercial air taxi services are launching in 12 states under new FAA programs - The EVTOL Integration Pilot Program (EIPP) has accelerated regulatory approval, potentially allowing the public to pay for rides as early as the end of this year.
“With electric propulsion, you remove the cost of the fuel and then you dramatically reduce the maintenance... we have six propulsion stations and each of those propulsion stations is driven by two separate motors, two separate battery packs.”
- •
AI and hydrogen are providing 10x gains in aviation engineering and range - AI-driven design is radically increasing aerodynamic productivity while new hydrogen-based propulsion systems promise to extend flight range at a much lower cost than batteries alone.
“You take one of the greatest aerodynamic minds on the planet and you enable him with something that makes him 10x as productive. The benefits compound in a crazy way.”
- •
Scientific AI requires a closed-loop interface with the physical world - progress in material science is bottlenecked by the inability of purely digital models to conduct experiments and learn from real-world feedback loops.
“Science ultimately isn't sitting in a room thinking really hard. You have to conduct experiments. You have to learn from them. You have to interface with reality.”
- •
Domain-specific labs should leverage existing LLM priors - Periodic Labs focuses its R&D exclusively on material science while utilizing third-party foundation models for coding and general reasoning to accelerate development.
“Periodic spends zero effort on improving coding models. We're incredibly impressed by Codex, Cloud Code and so that's been a huge accelerator for the company.”
- •
Physical experimentation provides the ground truth missing from literature - because scientific papers often contain noisy or contradictory data spanning multiple orders of magnitude, physical labs are required to ground ML models in reality.
“One of the engineers on our team was looking at a reported material property. And it was just sort of extracted values from literature. It was really interesting to see the reported value spanned many orders of magnitude. And so you train an ML system on that and it's like, well, the best you can do is model this distribution, but you're no closer to like a ground truth.”
- •
The Pentagon’s financial pivot - The Department of Defense is increasingly recruiting Wall Street talent to weaponize capital and manage economic defense strategies.
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The $10 billion TikTok fee - The Trump administration’s move to collect a massive brokerage fee for the TikTok deal signals a new era of government-driven private equity logic.
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The convergence at SXSW - Cultural and tech festivals are evolving into critical indicators for how geopolitics, media, and finance will intersect in the coming years.
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