GM
General Motors Company
stock NYSE

Nvidia with its huge and unsustainable margins has to increase prices in order not to disappoint. A decline in gross margins could lead to an important correction. AMD however is not in the same position. They have good GM but not extreme. Also the new exponential growth in its CPUs business led by agentic Ai has the best GM in the company product line. Amd has more flexibility in pricing than Nvidia right now for its ai GPUs.

|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))|
That took one prompt and a few minuets. 5 years ago I'd have still been researching and making that table after a few hours. That saved real world time and is used for actual productive means (see the fourth column) in the real world.

Nvidia with its huge and unsustainable margins has to increase prices in order not to disappoint. A decline in gross margins could lead to an important correction. AMD however is not in the same position. They have good GM but not extreme. Also the new exponential growth in its CPUs business led by agentic Ai has the best GM in the company product line. Amd has more flexibility in pricing than Nvidia right now for its ai GPUs.

|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))|
That took one prompt and a few minuets. 5 years ago I'd have still been researching and making that table after a few hours. That saved real world time and is used for actual productive means (see the fourth column) in the real world.

**Proof**:
[coindex.app/a/S5gdRT](http://coindex.app/a/S5gdRT)
**Shipping**: $7-$9 / $11-$13
**Payment**: Zelle (0+ flair) & Venmo (10+ flair)
———————————————
**Gold**:
x1 1907 Cinco Pesos (Non-Restrike) - $615
x1 2020 1/10 Kookaburra - $550
———————————————
**Silver:**
x1 1953 Corvette GM Round (issued to plant employees 70's/80's)- $90
x1 Hoffman and Hoffman Unicorn - $85
x1 Hoffman and Hoffman High relief trade unit - $80
———————————————
**EXTRA**
**Liability**: 100/0 until dropped off; 50/50 transit; 0/100 dropped off
**Insurance**: Registered (slow & expensive)
**Offers**: considered on multiple items
I reserve the right to change prices if an error occurs

The demand for silicon is way outpacing the supply. When demand exceeds supply, prices go up. NVIDIA wouldn’t be raising prices if they thought their customers wouldn’t be able to afford it. This increase should more than offset the wafer increase TSMC announced in July meaning there should be a 1-5% increase in GM and probably covers the additional OPEX we have already started seeing with the hyperscalers earnings in July.

Good post! You hit the nail on the head. A lot of folks don’t realize, we’re not going to hear specific customer names and how much we’re getting from GM or Ford etc etc. NDA’s keep this hush hush, Ram and Jeep now has the more advanced ADAS rolling out in there systems now and OTA, you will see the demand from the ARR of automakers compared to years past and will help you gauge just how much money these new systems are bringing in. Anyway like Red said, we’re very early on, don’t fret about the stock price, because you’re still early, down the road you will be glad you were in it early.
Here’s my original thesis for anyone that needs to understand the bargain you have gotten in, even at $13 or $14 you are in good shape, don’t let it make you nervous.
https://takedown2021.substack.com/p/blackberry-just-beat-every-estimate?r=8g54co&utm\_medium=ios
Cheers!

Since the spike in share price August 18, naturally the shorts flocked like flies spiking the cost to borrow from 58% to 600%
Company raised cash at $2.05, share price dumped and dipped to $1.50 Friday August 21.
Now the cash raise is out of the way, shorts still around.. Good luck..
Defsec US Army endof August testing — Last line before potential procurement contracts.
June 2025: Delivered initial BLDS units for armored vehicle trials.
April/May 2026: Shipped updated networked BLISS systems to the Yuma Test Center.
August 2026: Completed primary evaluations for Yuma vehicle program and secured follow-on trials
All eyes on the follow on trials commencing end of this August “This event puts BLISS™ in front of the operators and decision-makers."
Success at Yuma moves a system to a high Technology Readiness Level (TRL). This validation is mutually recognized by NATO allies, making the system eligible for fast-tracked procurement through Foreign Military Sales.
Traditional systems merely warn an operator they are being targeted. BLISS successfully proved its "networked" capability at Yuma—capturing and analyzing exact laser pulse signatures to identify the specific threat type, source, and intent, then sharing that data across the network.
Massive defense prime contractors (like Northrop Grumman, Saab, and Elbit Systems) have dominant, highly established hull-mounted LWR systems standard on heavy armor.
Where DEFSEC avoids competing head-to-head with the giants is scale and cost. Traditional LWRs are expensive, heavy, and isolated to a single vehicle. BLISS is designed for battlespace saturation. The sensors are tiny and cheap enough to be bolted onto light vehicles, fixed infrastructure, or even worn directly by dismounted soldiers.
Most older legacy systems do not talk to each other. BLISS integrates directly into TAK-enabled (Tactical Assault Kit) networks. This means if a soldier's vest detects a laser, the entire vehicle convoy instantly sees the threat vector on their digital maps. Very few competitors have bridged the gap between cheap hardware and advanced software data-sharing.
High cost to borrow meaning many traders want to bet that the stock price will drop. It also means scarse inventory, few remaining shares are left in broker pools or institutional portfolios available for lending. Short sellers are willing to pay steep, annualized daily fees because they strongly expect the stock to fall.
\+400% cost to borrow, crazy numbers on a 4 million float, 5 million market cap and US army testing.
There is a dilution overhang, 2.6 million warrants which exercise at C3.30 and C$4.39.
That means C$9.4 million in potential cash waiting to be unlocked if the stock rises past C$3.30 and C$4.39.
Those who participated in the $2.04 financing rounds clearly betting this won't be a $1.50 stock for much longer. And in my opinion, if BLISS gets in to the US and NATO armies, that warrant overhang will get devoured like little pacman candies.
Board, Management, their former companies and ties to General Dynamics:
David Luxton — Chairman: Founded Simunition then acquired by General Dynamics; Allen-Vanguard
David Ibbetson: 25 years at General Dynamics, former GM of GD Mission Systems International
Gen. Rick Hillier: Former Chief of the Canadian Defence Staff
James Yersh: Former CFO of BlackBerry, Mitel, KORE Wireless
Paul Fortin: Defence/security executive; Borden Ladner Gervais
Niel Marotta: Fidelity fund manager; former Indiva CEO
Luxton + Ibbetson + Webster + Preston is tied to general dynamics.
That's multiple senior people with direct GD/GDMS experience, alongside a former Canadian military chief.
For a $5–10M defence company.. Special.
Not financial advice, I have a position in $DFSC
https://defsectec.com/product-brochures/

Autoindustrie Update #4: China baut Batterien, GM baut Chinesen und VW baut Stellen ab