GM
General Motors Company
stock NYSE

Blitz & van Vliet (2007) sort a broad universe into deciles and rebalance monthly. Nobody outside an institution can run that. I wanted to know whether anything survives at a size a person can actually hold.
Setup: 20 names, equal weight, long only, drawn from the S&P 500 as it stood on each date. Re-selected quarterly on 3-year trailing volatility. 20 non-overlapping one-year windows, 2006-2025. Two arms identical except for selector direction lowest vol vs highest vol. Benchmark is RSP (equal-weight S&P) since both books are equal weighted.
https://preview.redd.it/f2d0to5cb5kh1.png?width=763&format=png&auto=webp&s=f4971095211badbe9e3ed2fdc858d81ff571cafe
The low-vol side landed 0.30pp/yr behind the index with roughly half the annual variation (10.0 vs 18.4 points). Before 2020 it was ahead outright: 10.73% vs 9.60%.
The mechanism isn't higher returns. The calm book's arithmetic mean annual return is 1.53pp BELOW the market. Compounded, the gap collapses to 0.30. It's variance drag it doesn't pay a book that doesn't fall 39.7% in 2008 has less to make up afterwards.
The high-vol side didn't replicate. Quarterly re-selection on trailing vol makes it mechanically a "buy whatever just blew up" rule: banks in 2008-10, energy 2016-17, cruise lines and airlines 2020-21. It returned 25.34%/yr from 2020 on vs 6.81% before. In an S&P-only universe the wildest names are large caps in temporary distress, not junk.
Caveats, and they matter: no transaction costs (turnover 59%/70%). 20 of \~500 is the top/bottom 4%, not a decile. The vendor serves no delisted names, so Lehman, Bear Stearns and GM return nothing that bias runs against the paper's direction, so the risk contrast is conservative. And none of the return differences are distinguishable from zero; calm led in 9 of 20 windows, t = -0.55.
Full study with the window-by-window record:
[https://quanterlab.com/research/lower-risk-without-lower-return-testing-blitz-and-van-vliet-on-twenty-sealed-win](https://quanterlab.com/research/lower-risk-without-lower-return-testing-blitz-and-van-vliet-on-twenty-sealed-win)

Yeah that’s definitely my gold coin. Although the gold coin is still cool. My Grandpa and my dad were Millwrights at GM. They got Gold watches when they retired along with a pension.

lol you think image generation is the main use cases of A.I?
Did we not just see A.I make 16 new viruses? A.I didn't just solve Mathematical problems that have stumped Fields Medalists?
How about hardware company that just released a product 4 months ahead of schedule because A.I assisted with it?
|Company|Physical product|What employees are using AI for|Measurable impact|
|:-|:-|:-|:-|
|**Eaton**|**Electrical lighting fixtures**|Engineers give generative AI the thermal, electrical, optical, mechanical, manufacturing and cost requirements; AI searches thousands of possible designs and engineers validate the finalists.|**16 weeks → 2 weeks**, an **87% reduction in design time**. Eaton also cut high-speed gear design time 65%. ([aPriori](https://www.apriori.com/resources/case-study/eatons-generative-ai-cuts-product-design-time-by-87/))|
|**HARTING**|**Industrial electrical connectors** used in EV charging, industrial equipment, data centers, etc.|AI takes requirements, refines 2D layouts, checks details, generates NX 3D models and performs basic thermal FEM before engineers finish the design.|A custom prototype that could take **weeks can now be generated in minutes**; standard configuration fell from 15–20 minutes to \~1 minute. ([Siemens](https://news.siemens.com/en-us/harting-siemens-microsoft/))|
|**GM**|**Cars, EVs and their physical systems**|Designers and engineers use AI for concept generation, aerodynamics, crash structures, controls, thermal behavior and powertrain calibration.|Concept visualization: **months → <1 day**. Roof-crush simulations: **8–40 hours → <5 minutes**. Aerodynamic feedback that took days/weeks can increasingly happen in real time. ([GM News](https://news.gm.com/home.detail.html/Pages/topic/us/en/2026/jun/0618-ai-virtual-labs.html))|
|**Ford**|**Cars and trucks**|Engineers use AI to turn sketches into 3D designs and create surrogate models for structural, CFD and aerodynamic simulation.|Ford described an engineering simulation that normally takes about **15 hours being predicted in \~10 seconds**. The explicit objective is shortening vehicle-development cycles. ([The Wall Street Journal](https://www.wsj.com/articles/ford-looks-to-innovate-faster-with-ai-agents-and-nvidia-gpus-05de57df?utm_source=chatgpt.com))|
|**BMW**|**BMW automobiles**|AI assists engineers with crash simulation, aerodynamics and autonomous-driving simulations, reducing dependence on expensive physical prototypes.|BMW explicitly says this makes its **development cycles faster**; it is now expanding AI-based crash engineering with Mistral AI. ([BMW Group](https://www.bmwgroup.com/en/innovation/artificial-intelligence.html?utm_source=chatgpt.com))|
|**PPG**|**Automotive paint / clear coat**|Materials scientists used an AI system trained on PPG chemistry and formulation data to search combinations humans hadn't considered.|AI proposed a formulation within minutes; PPG subsequently launched **Deltron Premium Glamour Speed Clearcoat**, its first AI-assisted new product. It cuts drying time by more than half. ([The Wall Street Journal](https://www.wsj.com/tech/ai/faster-drying-paint-and-better-smelling-soap-ai-tries-product-development-e7a544d7?utm_source=chatgpt.com))|
|**Sumitomo Riko**|**Automotive vibration isolators and hoses**|Engineers train AI surrogate models on previous high-fidelity engineering simulations instead of rerunning expensive simulations for every geometry.|New-design performance predictions take **under 5 minutes**, saving more than an hour for each design evaluation while maintaining accuracy comparable to high-fidelity simulation. ([Ansys](https://www.ansys.com/news-center/press-releases/10-8-25-ansys-ai-accelerates-sim-for-sumitomo-riko?utm_source=chatgpt.com))|
|**Medtronic**|**Pulse oximeters**|Medtronic R&D uses AI while developing pulse-oximetry technology, including improving its ability to distinguish valid pulse signals and addressing accuracy differences across patients.|The company says AI has helped improve the accuracy of its Nellcor pulse-oximetry technology; this is more of a **better-product** example than a documented time-to-market reduction. ([Medtronic News](https://news.medtronic.com/shining-light-through-the-fog-how-ai-makes-every-pulse-count-newsroom))|
|**GE HealthCare**|**X-ray and ultrasound equipment**|GE HealthCare is using NVIDIA's physical-AI simulation environment to develop and virtually test autonomous imaging systems before building as much physical hardware.|The partnership explicitly aims to **fast-track development** of autonomous X-ray and ultrasound systems, though there isn't yet a clean “24 months → 12 months” public number. ([NVIDIA Newsroom](https://nvidianews.nvidia.com/news/nvidia-and-ge-healthcare-collaborate-to-advance-the-development-of-autonomous-diagnostic-imaging-with-physical-ai?utm_source=chatgpt.com))|

Everyone remember that time that a now famous ex member of the board told me BB had nothing to do with GM and Supercruise wouldn’t be successful……ARR says otherwise

GM Anach!

General Motor's Super Cruise is looking for Staff Software Engineer to integrate Linux / QNX based platform OS development on current production Super cruise Programs. Just confirming GM's Super Cruise program based on Nvidia AGX Thor and foundational QNX OS for safety and security. Triple whammy?

MP has the best volume confirmation and the most durable catalyst path (Q3 NdPr above 1,000 tonnes, GM shipments in Q4), but it's pinned at its 200-day.
NBIS has the strongest fundamental delta and proven day-three follow-through, with the worst balance-sheet risk.
ESTC has the cleanest structure and a dated catalyst 11 days out, which means next week is positioning rather than event risk — and it's the only one of the three where you'd be entering before the print rather than after.