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These ex-Tesla supply chain managers started an AI inventory firm
The AI venture aims to revolutionize supply chain and demand management through its software platform.
Two former supply chain managers at Tesla have started their own AI inventory firm, which aims to make demand and inventory planning more efficient.
Neal Suidan, Tesla’s former Senior Manager of Global Demand Planning, and Michael Rossiter, former Director of Sales Operations and Senior Manager of Business Planning, announced the launch of Atomic on Tuesday, an AI platform geared toward supply planning. The launch was made alongside the announcement of a $3 million seed funding round from former DVx Ventures, the capital fund run by former Tesla President Jon McNeill, as well as the firm Madrona.
“Planners are the unsung heroes of consumer brands, holding together supply chains through spreadsheets and sheer force of will,” Suidan wrote in a post on LinkedIn. “But they deserve better tools. We built Atomic to be the inventory planning system we always wished we had.”
“Michael and Neil experienced this pain firsthand as leaders at Tesla in the supply chain, and I saw that work first hand — because they worked for me,” McNeill said in an interview with Tech Crunch.
The former Tesla president also explains how delicate the balance between supply and demand is, while a primary part of Atomic’s approach to the software platform is giving business operators the tools to manage these factors more quickly and easily.
“If you have too much capital tied up in inventory, you could really harm the business,” McNeill adds. “And if you have too little, where you don’t have the right things in stock when the customer is ready to purchase, then you’re costing yourself big time.”
Atomic says its AI planning software has previously helped early customers cut inventory costs by between 20 and 50 percent, allowing users to easily simulate scenarios based on real-time data and scenarios.
READ MORE ON FORMER TESLA PERSONNEL: Former Tesla executive aims to raise $50 million for energy startup
Suidan worked with Tesla for nearly six years, while Rossiter was with the company for about two years. Both of the managers also worked closely with McNeill at the time.
The former Tesla president also highlighted the difficulty in ramping Model 3 production as part of the project’s inspiration, a period that Elon Musk has said brought the company weeks away from bankruptcy and had him sleeping on the Fremont factory floor.
McNeill also recalled the Model 3 production ramp in a post on LinkedIn:
Back in 2018, we had a big problem at Tesla.
We needed to scale Model 3 production from 20k to 100k cars per quarter. But the existing supply chain systems simply couldn’t handle this growth. With only a month of cash left, we had to keep the cars moving.
We were far too dependent on spreadsheets for planning. They couldn’t keep up with the business and it was having a serious negative impact.
Neal Suidan and Michael Rossiter, both leading global demand planning, created something remarkable out of necessity: a unit-level planning system that could simulate and track individual cars through the entire supply chain and match them to demand. This reduced Tesla’s inventory from 75 days to just 15, unlocking billions of dollars in working capital at a time when every dollar mattered.
Fast forward 7 years and it occurred to us that thousands of companies can use this. They are now bringing that framework to customers with Atomic.
Several former Tesla employees and executives have gone on to start their own firms, most recently including former SVP Drew Baglino, who announced the grid hardware venture Heron Power last week. Another notable one includes JB Straubel, a Tesla co-founder, who went on to start the battery recycling company Redwood Materials.
This former Tesla engineer now heads a federal tech department
Elon Musk
Starlink passes 9 million active customers just weeks after hitting 8 million
The milestone highlights the accelerating growth of Starlink, which has now been adding over 20,000 new users per day.
SpaceX’s Starlink satellite internet service has continued its rapid global expansion, surpassing 9 million active customers just weeks after crossing the 8 million mark.
The milestone highlights the accelerating growth of Starlink, which has now been adding over 20,000 new users per day.
9 million customers
In a post on X, SpaceX stated that Starlink now serves over 9 million active users across 155 countries, territories, and markets. The company reached 8 million customers in early November, meaning it added roughly 1 million subscribers in under seven weeks, or about 21,275 new users on average per day.
“Starlink is connecting more than 9M active customers with high-speed internet across 155 countries, territories, and many other markets,” Starlink wrote in a post on its official X account. SpaceX President Gwynne Shotwell also celebrated the milestone on X. “A huge thank you to all of our customers and congrats to the Starlink team for such an incredible product,” she wrote.
That growth rate reflects both rising demand for broadband in underserved regions and Starlink’s expanding satellite constellation, which now includes more than 9,000 low-Earth-orbit satellites designed to deliver high-speed, low-latency internet worldwide.
Starlink’s momentum
Starlink’s momentum has been building up. SpaceX reported 4.6 million Starlink customers in December 2024, followed by 7 million by August 2025, and 8 million customers in November. Independent data also suggests Starlink usage is rising sharply, with Cloudflare reporting that global web traffic from Starlink users more than doubled in 2025, as noted in an Insider report.
Starlink’s momentum is increasingly tied to SpaceX’s broader financial outlook. Elon Musk has said the satellite network is “by far” the company’s largest revenue driver, and reports suggest SpaceX may be positioning itself for an initial public offering as soon as next year, with valuations estimated as high as $1.5 trillion. Musk has also suggested in the past that Starlink could have its own IPO in the future.
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NVIDIA Director of Robotics: Tesla FSD v14 is the first AI to pass the “Physical Turing Test”
After testing FSD v14, Fan stated that his experience with FSD felt magical at first, but it soon started to feel like a routine.
NVIDIA Director of Robotics Jim Fan has praised Tesla’s Full Self-Driving (Supervised) v14 as the first AI to pass what he described as a “Physical Turing Test.”
After testing FSD v14, Fan stated that his experience with FSD felt magical at first, but it soon started to feel like a routine. And just like smartphones today, removing it now would “actively hurt.”
Jim Fan’s hands-on FSD v14 impressions
Fan, a leading researcher in embodied AI who is currently solving Physical AI at NVIDIA and spearheading the company’s Project GR00T initiative, noted that he actually was late to the Tesla game. He was, however, one of the first to try out FSD v14.
“I was very late to own a Tesla but among the earliest to try out FSD v14. It’s perhaps the first time I experience an AI that passes the Physical Turing Test: after a long day at work, you press a button, lay back, and couldn’t tell if a neural net or a human drove you home,” Fan wrote in a post on X.
Fan added: “Despite knowing exactly how robot learning works, I still find it magical watching the steering wheel turn by itself. First it feels surreal, next it becomes routine. Then, like the smartphone, taking it away actively hurts. This is how humanity gets rewired and glued to god-like technologies.”
The Physical Turing Test
The original Turing Test was conceived by Alan Turing in 1950, and it was aimed at determining if a machine could exhibit behavior that is equivalent to or indistinguishable from a human. By focusing on text-based conversations, the original Turing Test set a high bar for natural language processing and machine learning.
This test has been passed by today’s large language models. However, the capability to converse in a humanlike manner is a completely different challenge from performing real-world problem-solving or physical interactions. Thus, Fan introduced the Physical Turing Test, which challenges AI systems to demonstrate intelligence through physical actions.
Based on Fan’s comments, Tesla has demonstrated these intelligent physical actions with FSD v14. Elon Musk agreed with the NVIDIA executive, stating in a post on X that with FSD v14, “you can sense the sentience maturing.” Musk also praised Tesla AI, calling it the best “real-world AI” today.
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Tesla AI team burns the Christmas midnight oil by releasing FSD v14.2.2.1
The update was released just a day after FSD v14.2.2 started rolling out to customers.
Tesla is burning the midnight oil this Christmas, with the Tesla AI team quietly rolling out Full Self-Driving (Supervised) v14.2.2.1 just a day after FSD v14.2.2 started rolling out to customers.
Tesla owner shares insights on FSD v14.2.2.1
Longtime Tesla owner and FSD tester @BLKMDL3 shared some insights following several drives with FSD v14.2.2.1 in rainy Los Angeles conditions with standing water and faded lane lines. He reported zero steering hesitation or stutter, confident lane changes, and maneuvers executed with precision that evoked the performance of Tesla’s driverless Robotaxis in Austin.
Parking performance impressed, with most spots nailed perfectly, including tight, sharp turns, in single attempts without shaky steering. One minor offset happened only due to another vehicle that was parked over the line, which FSD accommodated by a few extra inches. In rain that typically erases road markings, FSD visualized lanes and turn lines better than humans, positioning itself flawlessly when entering new streets as well.
“Took it up a dark, wet, and twisty canyon road up and down the hill tonight and it went very well as to be expected. Stayed centered in the lane, kept speed well and gives a confidence inspiring steering feel where it handles these curvy roads better than the majority of human drivers,” the Tesla owner wrote in a post on X.
Tesla’s FSD v14.2.2 update
Just a day before FSD v14.2.2.1’s release, Tesla rolled out FSD v14.2.2, which was focused on smoother real-world performance, better obstacle awareness, and precise end-of-trip routing. According to the update’s release notes, FSD v14.2.2 upgrades the vision encoder neural network with higher resolution features, enhancing detection of emergency vehicles, road obstacles, and human gestures.
New Arrival Options also allowed users to select preferred drop-off styles, such as Parking Lot, Street, Driveway, Parking Garage, or Curbside, with the navigation pin automatically adjusting to the ideal spot. Other refinements include pulling over for emergency vehicles, real-time vision-based detours for blocked roads, improved gate and debris handling, and Speed Profiles for customized driving styles.
Starlink passes 9 million active customers just weeks after hitting 8 million
NVIDIA Director of Robotics: Tesla FSD v14 is the first AI to pass the “Physical Turing Test”
