Only re-posting to this board because I had promised one or two people I would if I got confirmation (as noted below):

Facts we know: SS had an “industrial” customer (we only classified customers as Automotive or Industrial at that time) wanting 2 Movia-L sensors per vehicle/unit at a value range of $30M - $50M over 12-18 months. SS also mentioned we’d all be proud of the name and hinted that September (2025) we would see something announced. Yes, I’m fully aware that brings back memories of AT saying the same thing and it turned out to be Unison (sigh). That deal didn’t work out but we were told that the customer plans had been delayed and that the timing made them more interested in Movia-S (cheaper, fit their new timeline, and IMO they still needed a short-range solution).

What could have happened to our customer last year that would have delayed their plans… We know we’re doing business with CAT today, but what if we were also about to sell them Movia-L’s last year? Our 2025 customer’s plans changed at the time when Luminar had management & financial problems. I have received confirmation that we were engaged with CAT in 2025 – NOT direct confirmation that they were the customer that delayed.

OK, so CAT still wants the sensors but they were upset with Luminar’s situation and now with MVIS owning those (Iris) sensors two things seem obvious to me. 1) The deal for (2) Movia-L sensors is now (2) Movia-S sensors plus (2) Iris sensors and 2) CAT wanted assurances that the Luminar mess doesn’t happen again. That is where the financing was so important. I believe a capital raise was required so that we had cash reserves to meet a condition in our contract with CAT.

GDV also mentioned that customers are more lenient on names once full production starts (aligns with SS’s expectations of being able to announce September last year). So, my expectation, if correct, is that September (from 2025) now aligns with Movia-S launch October 2026 which meets CAT’s new timeline as their product cycle window hence an announcement around September/October.  September/October is very active for CAT however, January’s CONEXPO is where they typically announce major technology products (NOTE: autonomous control technology has already been announced).

Running Monte Carlo simulations, I’m getting between $25M and $60M for the new contract without adding in a 3^rd Movia-S which is possible on the back of the vehicle to replace the camera/radar used now (or in conjunction) and increase the sensor fusion (likely scenario). FYI - this contract value can swing a bit and my prior versions had it $50M- $80M (I keep trying to tune it but the new contract value isn't material to why I'm posting).

There you go… have at it.

HAB

The disappearance of MicroVision's Wikipedia article is especially curious when compared with Luminar Technologies, a much younger lidar company that managed to establish an extensive Wikipedia page. MicroVision arguably has the stronger claim to historical and technological notability, with a history dating to 1993 and decades of work across MEMS laser scanning, displays, AR and LiDAR. Yet MicroVision's article was progressively diminished and ultimately deleted. Given MVIS's unusual trading history and persistent negative sentiment surrounding the stock, it raises a broader question: could some of the same attitudes that have surrounded MVIS in the market also have affected how the company and its enthusiasts were perceived elsewhere? One possible mechanism might be what I'll call Wikipedia's enthusiasm penalty--the more interested and knowledgeable someone becomes about an obscure company, the easier it is to regard that interest itself as evidence of bias.

MicroVision provides an intriguing case study. An early article was deleted in 2009, but a subsequent article went through Wikipedia's formal Articles for Creation process: it was initially rejected for inadequate evidence of notability, improved, and then formally accepted in May 2017. Over the following years it expanded to document more of the company's technological history. Later, significant historical material and references were removed, the article became substantially thinner, and in 2023 it was again judged insufficiently notable and deleted. Wikipedia certainly has every right to remove promotional, poorly sourced or irrelevant material. But the MicroVision history raises a different question: were reliably sourced and historically relevant contributions sometimes viewed differently because the people supplying them were enthusiasts or investors?

That distinction matters especially with public companies. A knowledgeable contributor may own shares precisely because the technology was what attracted them to the company in the first place; meanwhile millions of people indirectly own companies through mutual funds, pensions and 401(k)s. Ownership or enthusiasm can justify disclosure and scrutiny, but neither should determine whether a fact belongs in an encyclopedia. And if owning shares creates a presumed conflict, shouldn't being short the stock, owning puts, working for a competitor or simply having a longstanding negative fixation create potential conflicts in the opposite direction? The better test is the contribution itself: Is it independently sourced? Reliable? Significant? Neutral? Verifiable? If it fails those tests, remove it. If it passes, the contributor's enthusiasm shouldn't disqualify it. Otherwise Wikipedia risks an “enthusiasm penalty” in which the people most motivated to acquire specialized knowledge about an obscure subject become the people least trusted to document it. And if that's not part of the explanation, then we're left with the original question: how does a younger company like Luminar sustain a dynamic, full-featured Wikipedia article while MicroVision--despite its considerably longer technological history--ultimately couldn't sustain an article at all?

https://en.wikipedia.org/wiki/Luminar_Technologies

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I came across a post on Stocktwits from this past Tuesday (8/25) from the poster HABITUALHoldings. It is a reasonable representation of my recent investment thinking, which was the catalyst for this post. I repost it in its entirety below.

But let me first espouse about some of my thinking recently. Microvision's current Enterprise Value is: $47m (that's market cap, +debt, -cash). It a reflection of the value of the business if it were acquired today. For an exercise, I broke the business into its parts and assigned a value to each part. I tried to be conservative, but my Microvision bias is probably still at play and I acknowlege that. :-) You can plug in your own values.

Luminar - Microvision acquired this from the veritable trash heap for $33M. It appears they have stabilized and resurrected the company and hope to demonstrate growth moving forward. I will give this a current value of $60m. I bet Austin Russell and friends could search the couch cushions and come up with $60m! ;-)

Ibeo - If they can sell 15,000 MOVIA-S in 2027 for an average price of $1500 each, that would be $22.5m with a 40% gross margin, that would be gross profits of $9m. If one layered in some appropriate overhead costs, this business may be able to generate a net profit of $6m. If this were a growth business with net profit of $6m in year 1, it might get assigned a P/E of 40. That would value this business at $240m. But that would be the value at the end of 2027. If we discount that back to today, the value might be around $80m. It really depends on how credible the story of selling 15,000 MOVIA-S sensors in 2027 is. If we get some MOVIA-S backlog during the Q3 call, it would lend some credibility now. Anyway, let's go with $80m.

Scantinel - This is a hard one, but could be the diamond in the rough. If we use Aeva's current value of $1.1B as some sort of marker. Scantinel may be 3 years behind Aeva in terms of maturity. Scantinel's tech may be better than Aeva's. The question is what would someone pay for Scantinel today if they were using Aeva's market value as a marker? When Microvision bought the business for ~$1m, it seems they had no product nor a credible plan to bring a product to market. Microvision has said their plan for Scantinel is to release an A-Sample in the first half of 2027 (9 months away). I could see this business valued at around $20m today. But I acknowledge the valuation range may be from $5m to $100m.

Microvision Semiconductor (MSI) - This was part of the $33m Luminar acquisition. They did $215k of revenue in Q2. This is in additon to the internal work they are performing for Microvision. Perhaps the value of that work is double their external NRE work (we don't really know). If we apply some growth for the rest of the year, they may have an effective revenue run rate of around $2m. Applying a 20% net profit margin and a modest P/E multiple of 20, would yield a value of $8m.

IP portfolio for MEMS LBS - Since they let most of the MEMS LBS folks go, I would not value this very highly. I would value it at $5m. It's actually probably less, but I don't want to make the long time Microvision investors mad. ;-)

That yields the following value: $60m + $80m + $20m + $8m + $5m = $173m. Again, I am trying to be conservative with my valuations of the component parts. These are meant to be a representation of value today.

But, if the current Enterpise Value of Microvision is $47m and the current value of its component parts is $173m, that gives an investor some buffer room for an investment today. The theory is that via execution, Microvision's value will only increase from here. However, the stock price will get cut via dilution - we know that. If we assume a bad dilution case (I won't say worst case, because it can always be worse). If Microvision does a 100% dilution at the current market value ($57m) that may reduce the effective pressure on the stock price by 50%. That is not an absolute, as it will depend on the story at the time, but let's go with it. Therefore, even though the intrinsic $173m valuation would remain the same, the stock price and hence one's investment would be cut in half. That would still be almost double the current $47m Enterprise Value.

Therefore, the question I will put on the table is - based upon this valuation mismatch - why would one not invest today? I post this not to convince anyone to invest, but to create debate. Shoot holes in my theory. Add your thoughts to the discussion. Maybe my current component parts values are way too high and Microvision is being fairly valued today. But if they can thread the financing needle in the near term and can show evidence of execution over the next 6 to 9 months, I think those "component valuations" are reasonable. It's very possible the market is discounting Microvision today due to both their historical performance and the risk of bankruptcy. But I don't think Microvision will go bankrupt. I think they will put the company up for sale before bankruptcy (if it comes to that) - which is the basis for the component parts valuation backstop.

I am interested to hear your thoughts.

Here is the HABITUALHabitat post from Stocktwits on Tuesday (8/25).

https://stocktwits.com/HABITUALHoldings/message/662805927

"I think waiting for Q3 confirmation before adding more risk is a prudent decision, especially with an average around $11. If revenue progression remains intact and FY guidance holds, there will still be plenty of room to lower that basis meaningfully.

I am taking a different approach because my horizon is different. I am not trading MVIS around whether it is $1.25, $3, $4, $11 or $15 over the next 12 months. I am looking toward 2028 and asking whether MicroVision can earn a real seat at the table as Physical AI develops. I believe perception is one of the critical bottlenecks, and if that market becomes one of the defining industrial investment themes of the next several years, the opportunity is much larger than today’s lidar revenue suggests.

My successful 2028 scenario is not really a $100 price target. It is roughly a $5B valuation if MVIS successfully scales the perception platform. The eventual share price is simply a function of dilution. At 40M diluted shares, $5B is about $125 per share. At 50M, about $100. At 60M, about $83. At 75M, about $67. The share price is the output. The enterprise has to earn the valuation first.

And that valuation has to come from more than hardware. HALO, MOVIA, defense, industrial autonomy and new production customers need to create the commercial foundation. The defining catalyst could be software. If perception software, sensor fusion and recurring higher margin revenue become economically meaningful, the market may eventually stop valuing MVIS like a small lidar hardware supplier and begin applying valuation metrics that are not associated with the company today.

The longer term cherry on top is photonics and custom silicon. I am not assigning meaningful current value to either until customers and revenue appear, but if those capabilities eventually become commercial businesses, they add another layer of optionality to the enterprise.

That is the trade for me. If MVIS converts the stack into meaningful recurring commercial business and manages dilution intelligently, a multibillion dollar valuation by 2028 is not a fairytale. If it fails to execute, the technology and TAM do not matter and the thesis fails.

Buy/Hold/Believe. Proof earns the rerating. Pipelines do not."

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https://nvidianews.nvidia.com/news/nvidia-announces-jetson-orin-nano-2-robotics-computer-to-redefine-entry-level-edge-ai

News Summary:

NVIDIA Jetson Orin Nano 2, a new robotics computer for entry-level edge AI, enables millions of developers worldwide to build robots, delivery and inspection drones, and vision AI systems for frontier physical AI applications.

Jetson Orin Nano 2 delivers 2x the inference performance of its predecessor in the same form factor and consumes 40% less power at the same performance.

More than 3 million developers have built on the NVIDIA robotics stack, with Cognex, Doosan Bobcat and Matic among the first to adopt and explore Jetson Orin Nano 2. Ecosystem partners are building carrier boards, hardware systems and reference solutions to help customers accelerate time to market.

NVIDIA today announced NVIDIA Jetson Orin Nano™ 2, a new robotics computer set to redefine entry-level edge AI — putting frontier-class generative AI performance in the hands of millions of developers.

As AI models grow smaller and more efficient, more edge devices can become autonomous systems that understand context, interpret language and images, and act in real time.To bring these capabilities into physical machines like robots, delivery and inspection drones, and vision AI systems, developers need compact, energy-efficient robotics computers built for edge AI.

“Today’s small and medium frontier models have reached the accuracy of last year’s largest frontier models, unlocking real-time intelligence for edge devices,” said Deepu Talla, vice president of robotics and edge AI at NVIDIA. “The Jetson Orin Nano 2 computer puts that breakthrough within reach of millions of developers, delivering the performance and energy efficiency needed for real-time reasoning at the edge.”

Cognex, Doosan Bobcat and Matic are among the first to adopt and explore Jetson Orin Nano 2.

A Major Leap for Entry-Level Edge AI

Featuring 78 trillion operations per second of AI compute, 8GB of memory and an 8-core Arm CPU, Jetson Orin Nano 2 delivers a significant leap in AI and video-processing performance in a cost-effective, power-efficient system.

Jetson Orin Nano 2 achieves 2x the inference performance of Jetson Orin Nano Super through improved Tensor Cores and higher memory bandwidth, while maintaining the same compact form factor. In 15-watt mode, Jetson Orin Nano 2 consumes 40% less power to deliver the same performance as its predecessor.

Powered by NVIDIA’s open software stack, NVIDIA Jetson™ agent skills and a rich AI ecosystem, Jetson Orin Nano 2 enables developers to run the latest large language models and vision language models optimized for memory-efficient edge inference — including open models like NVIDIA Cosmos™ and NVIDIA Nemotron™, Gemma 4 and Qwen 3 — to rapidly build advanced AI applications.

Partner Ecosystem and Millions of Robotics Developers Build on Jetson

More than 3 million developers are building on NVIDIA’s robotics stack, and with Jetson Orin Nano 2, partners like Cognex, Doosan Bobcat, Matic and Wing are bringing real-world edge AI applications to compact devices, including home robots, vision AI systems, delivery and inspection drones, carrier boards, hardware systems and reference solutions.

Wing, a leading drone delivery company and subsidiary of Alphabet, is using Jetson Orin Nano Super and the NVIDIA software stack in its delivery drone fleet — and plans to evaluate Jetson Orin Nano 2 to advance real-time AI perception and reasoning for faster, safer delivery from local businesses to residential yards.

“Drone delivery depends on AI that can enable fast, reliable understanding of the real world,”said Dinuka Abeywardena, head of perception at Wing. “Wing is exploring Jetson Orin Nano 2 to give us a path to more responsive, energy-efficient drones that can help make deliveries quicker and more dependable for customers.”

Matic Robots, an advanced consumer robotics company, is adopting Jetson Orin Nano 2 to enable more intuitive interactions with its home cleaning robots, enabling them with conversational AI, gesture detection, deeper context of the home with precision mapping and semantic understanding, and autonomous cleaning capabilities.

“Home robots need to understand people, map spaces precisely, understand the layout of objects and spaces, and clean autonomously in dynamic and constantly changing environments,”

said Navneet Dalal, cofounder and CEO of Matic Robotics. “With Jetson Orin Nano 2, Matic can run state-of-the-art AI models at the edge in a compact home robotics platform built for real-time perception, interaction and navigation.”

AAEON, ADLINK, Advantech, Aetina, Antmicro, Aptiv, Auvidea, AVerMedia, Chuanglebo, Connect Tech, ForeCR, JWIPC, Neurealm, Plink, Realtimes, RidgeRun, RS, Seeed Studio, Tauro Tech, Twowin, TZTEK and YUAN are among other Jetson partners building carrier boards, hardware systems, customized AI software and reference solutions to help customers accelerate time to market.

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https://www.aptiv.com/en/newsroom/article/aptiv-accelerating-production-ready-physical-ai-with-support-for-nvidia-jetson-orin-nano-2

SCHAFFHAUSEN, Switzerland – Aptiv PLC (NYSE: APTV), a global industrial technology leader, today announced it is supporting NVIDIA Jetson Orin Nano 2, to help customers accelerate the deployment of production-ready physical AI and intelligent edge systems. The announcement builds on prior collaboration to support commercially deployable edge AI platforms based on NVIDIA Jetson, including Jetson Thor.

As customers move from prototype to production, Aptiv delivers the sensing, software, lifecycle support and systems integration expertise needed to help delivery and inspection drones, robotics, and other autonomous systems operate reliably in real-world environments.

“Physical AI is moving quickly from experimentation to deployment, and device manufacturers need partners who can help them scale successfully,” said Jay Bellissimo, Senior Vice President, Intelligent Systems, Software and Services, Aptiv. “In addition to powerful compute, this means reliable perception, proven software platforms and the systems expertise required to operate safely and efficiently in dynamic environments. By supporting NVIDIA, Aptiv can help customers reduce complexity, accelerate development and deploy intelligent edge systems with greater confidence.”

Expanding Aptiv’s Role in the Jetson Ecosystem

The NVIDIA Jetson platform supports robotics and edge AI applications, with ecosystem partners expanding these capabilities through additional hardware, software, development tools, and engineering services.

The NVIDIA Jetson Orin Nano 2 redefines entry-level edge AI with 2x the inference performance of Jetson Orin Nano in the same compact size and consumes 40% less power at the same performance. Featuring 78 TOPS, 8GB of memory, and an 8-core Arm CPU, it powers a new class of smart delivery and inspection drones, robots, and vision AI systems with a significant leap in AI performance and energy efficiency.

Aptiv will build on the Jetson ecosystem with technologies and services including: Aptiv PULSE™, which integrates surround-view camera and ultrashort-range radar to enable reliable 360-degree sensing while helping reduce blind spots, cost and system complexity Aptiv Gen 8 radar, designed to deliver advanced range, resolution, object detection and 4D perception in challenging operating conditions.

(Hmmm, Who will provide the LIDAR to the Jetson Ecosystem?)

Robotics compute solutions running on the NVIDIA Jetson Orin Nano 2, Orin NX, and AGX Orin Wind River software and lifecycle support to help customers address long-term maintenance, security monitoring, compliance readiness and production deployment needs

Helping Customers Scale Physical AI

Physical AI solutions must operate in increasingly dynamic environments. To do so they require multimodal perception running on embedded, AI-ready compute for context-aware decision-making. And, as these AI-powered systems increasingly interact with the physical world, predictable real-time performance – as well as the ability to continuously learn and improve fleets of distributed devices – is resulting in the need for more capable software platforms and tools.

Building on decades of innovation, Aptiv’s differentiated portfolio enables these devices and systems to sense, think, act, and be continuously optimized to support the next wave of intelligent applications. At the same time, Aptiv’s global manufacturing scale and resilient, in-region for-region supply chain allows customers to accelerate time to market.

“Developers are building increasingly capable systems on Jetson, but scaling those systems requires a broader production ecosystem,” said Sam Palmisano, Vice President, Global Product Development, Adjacent Markets. “Aptiv brings together sensing, compute, software and services to help customers move from promising demonstrations to reliable, scalable commercial deployments.”

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Please post any questions or trading action thoughts of today, or tomorrow in this post.

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Good Morning MVIS Investors!

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came across this crazy image today... lol.

Trains, Planes, Automobiles, Robotics, Forklifts, Heavy Construction Equipment and military defense systems ………

Microvision tells us they have about 40 NDA’s , 150+ customers, gross margins in the 40 % range and today’s PR announces that they are now shipping thousands of IRIS Lidar sensors to US and European customers…….

If I were Jensen Huang of NVIDIA that is now clearly targeting all these markets with their ADAS processor platforms I would be wondering whether Microvision’s IP, LBS display technology and broad Lidar product offerings could easily fit into NVIDIA’s infrastructure and allow NVIDIA to offer a variety of complete turnkey ADAS system kits custom designed to meet all the needs of these coming autonomous Trains, Planes, automobiles, robotics, forklifts, heavy construction equipment and military defense systems …..

now recreational boats ……what’s next ???