Connect with us

News

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.

Published

on

Credit: Tesla

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.

Advertisement
-->

“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.

Advertisement
-->

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.

Advertisement
-->

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

Zach is a renewable energy reporter who has been covering electric vehicles since 2020. He grew up in Fremont, California, and he currently lives in Colorado. His work has appeared in the Chicago Tribune, KRON4 San Francisco, FOX31 Denver, InsideEVs, CleanTechnica, and many other publications. When he isn't covering Tesla or other EV companies, you can find him writing and performing music, drinking a good cup of coffee, or hanging out with his cats, Banks and Freddie. Reach out at zach@teslarati.com, find him on X at @zacharyvisconti, or send us tips at tips@teslarati.com.

Advertisement
Comments

News

Tesla FSD (Supervised) v14.2.2 starts rolling out

The update focuses on smoother real-world performance, better obstacle awareness, and precise end-of-trip routing, among other improvements.

Published

on

Credit: Grok Imagine

Tesla has started rolling out Full Self-Driving (Supervised) v14.2.2, bringing further refinements to its most advanced driver-assist system. The new FSD update focuses on smoother real-world performance, better obstacle awareness, and precise end-of-trip routing, among other improvements.

Key FSD v14.2.2 improvements

As noted by Not a Tesla App, 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 let users select preferred drop-off styles, such as Parking Lot, Street, Driveway, Parking Garage, or Curbside, with the navigation pin automatically adjusting to the user’s ideal spot for precision.

Other additions include pulling over for emergency vehicles, real-time vision-based detours for blocked roads, improved gate and debris handling, and extreme Speed Profiles for customized driving styles. Reliability gains cover fault recovery, residue alerts on the windshield, and automatic narrow-field camera washing for new 2026 Model Y units.

FSD v14.2.2 also boosts unprotected turns, lane changes, cut-ins, and school bus scenarios, among other things. Tesla also noted that users’ FSD statistics will be saved under Controls > Autopilot, which should help drivers easily view how much they are using FSD in their daily drives.  

Key FSD v14.2.2 release notes

Full Self-Driving (Supervised) v14.2.2 includes:

Advertisement
-->
  • Upgraded the neural network vision encoder, leveraging higher resolution features to further improve scenarios like handling emergency vehicles, obstacles on the road, and human gestures.
  • Added Arrival Options for you to select where FSD should park: in a Parking Lot, on the Street, in a Driveway, in a Parking Garage, or at the Curbside.
  • Added handling to pull over or yield for emergency vehicles (e.g. police cars, fire trucks, ambulances).
  • Added navigation and routing into the vision-based neural network for real-time handling of blocked roads and detours.
  • Added additional Speed Profile to further customize driving style preference.
  • Improved handling for static and dynamic gates.
  • Improved offsetting for road debris (e.g. tires, tree branches, boxes).
  • Improve handling of several scenarios, including unprotected turns, lane changes, vehicle cut-ins, and school buses.
  • Improved FSD’s ability to manage system faults and recover smoothly from degraded operation for enhanced reliability.
  • Added alerting for residue build-up on interior windshield that may impact front camera visibility. If affected, visit Service for cleaning!
  • Added automatic narrow field washing to provide rapid and efficient front camera self-cleaning, and optimize aerodynamics wash at higher vehicle speed.
  • Camera visibility can lead to increased attention monitoring sensitivity. 

Upcoming Improvements:

  • Overall smoothness and sentience.
  • Parking spot selection and parking quality.
Continue Reading

News

Tesla is not sparing any expense in ensuring the Cybercab is safe

Images shared by the longtime watcher showed 16 Cybercab prototypes parked near Giga Texas’ dedicated crash test facility.

Published

on

Credit: @JoeTegtmeyer/X

The Tesla Cybercab could very well be the safest taxi on the road when it is released and deployed for public use. This was, at least, hinted at by the intensive safety tests that Tesla seems to be putting the autonomous two-seater through at its Giga Texas crash test facility. 

Intensive crash tests

As per recent images from longtime Giga Texas watcher and drone operator Joe Tegtmeyer, Tesla seems to be very busy crash testing Cybercab units. Images shared by the longtime watcher showed 16 Cybercab prototypes parked near Giga Texas’ dedicated crash test facility just before the holidays. 

Tegtmeyer’s aerial photos showed the prototypes clustered outside the factory’s testing building. Some uncovered Cybercabs showed notable damage and one even had its airbags engaged. With Cybercab production expected to start in about 130 days, it appears that Tesla is very busy ensuring that its autonomous two-seater ends up becoming the safest taxi on public roads. 

Prioritizing safety

With no human driver controls, the Cybercab demands exceptional active and passive safety systems to protect occupants in any scenario. Considering Tesla’s reputation, it is then understandable that the company seems to be sparing no expense in ensuring that the Cybercab is as safe as possible.

Tesla’s focus on safety was recently highlighted when the Cybertruck achieved a Top Safety Pick+ rating from the Insurance Institute for Highway Safety (IIHS). This was a notable victory for the Cybertruck as critics have long claimed that the vehicle will be one of, if not the, most unsafe truck on the road due to its appearance. The vehicle’s Top Safety Pick+ rating, if any, simply proved that Tesla never neglects to make its cars as safe as possible, and that definitely includes the Cybercab.

Advertisement
-->
Continue Reading

Elon Musk

Tesla’s Elon Musk gives timeframe for FSD’s release in UAE

Provided that Musk’s timeframe proves accurate, FSD would be able to start saturating the Middle East, starting with the UAE, next year. 

Published

on

Tesla CEO Elon Musk stated on Monday that Full Self-Driving (Supervised) could launch in the United Arab Emirates (UAE) as soon as January 2026. 

Provided that Musk’s timeframe proves accurate, FSD would be able to start saturating the Middle East, starting with the UAE, next year. 

Musk’s estimate

In a post on X, UAE-based political analyst Ahmed Sharif Al Amiri asked Musk when FSD would arrive in the country, quoting an earlier post where the CEO encouraged users to try out FSD for themselves. Musk responded directly to the analyst’s inquiry. 

“Hopefully, next month,” Musk wrote. The exchange attracted a lot of attention, with numerous X users sharing their excitement at the idea of FSD being brought to a new country. FSD (Supervised), after all, would likely allow hands-off highway driving, urban navigation, and parking under driver oversight in traffic-heavy cities such as Dubai and Abu Dhabi.

Musk’s comments about FSD’s arrival in the UAE were posted following his visit to the Middle Eastern country. Over the weekend, images were shared online of Musk meeting with UAE Defense Minister, Deputy Prime Minister, and Dubai Crown Prince HH Sheikh Hamdan bin Mohammed. Musk also posted a supportive message about the country, posting “UAE rocks!” on X.

Advertisement
-->

FSD recognition

FSD has been getting quite a lot of support from foreign media outlets. FSD (Supervised) earned high marks from Germany’s largest car magazine, Auto Bild, during a test in Berlin’s challenging urban environment. The demonstration highlighted the system’s ability to handle dense traffic, construction sites, pedestrian crossings, and narrow streets with smooth, confident decision-making.

Journalist Robin Hornig was particularly struck by FSD’s superior perception and tireless attention, stating: “Tesla FSD Supervised sees more than I do. It doesn’t get distracted and never gets tired. I like to think I’m a good driver, but I can’t match this system’s all-around vision. It’s at its best when both work together: my experience and the Tesla’s constant attention.” Only one intervention was needed when the system misread a route, showcasing its maturity while relying on vision-only sensors and over-the-air learning.

Continue Reading