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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.
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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
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Tesla crosses major Unsupervised Self-Driving milestone
Tesla has reached a notable benchmark in its autonomous driving program after its Robotaxi fleet surpassed one million miles of unsupervised operation. The company made the announcement during its Cybercab event in Austin on September 3.
Tesla Vice President of AI Ashok Elluswamy told attendees he was happy to report the fleet had achieved one million miles of unsupervised Robotaxi operation as a testament to safety.
The new total marked a sharp increase from the 380,000 unsupervised miles Tesla disclosed during its second-quarter 2026 earnings update in late July.
In roughly six weeks, the company added about 620,000 miles. That acceleration followed Tesla’s decision to remove in-vehicle safety monitors from most of its operations outside the San Francisco Bay Area.

Credit: Tesla
Tesla first launched Robotaxi service in Austin in June 2025 with safety drivers present. It later began fully unsupervised rides and expanded into Dallas, Houston, Miami, Orlando, and Tampa. The San Francisco Bay Area remains the exception, where a safety monitor still rides in the vehicle under California permitting rules.
The company has not released a city-by-city breakdown of the one million unsupervised miles.
The milestone arrived as Tesla began offering public Cybercab rides in Austin. The purpose-built vehicle has no steering wheel or pedals and is designed only for autonomous ride-hailing. Production versions joined the existing fleet of modified Tesla vehicles already operating in the service.
Tesla’s unsupervised mileage is growing at a double-digit weekly rate according to earlier company comments, yet its fleet size remains modest compared with established competitors. Waymo has accumulated more than 200 million fully autonomous rider-only miles. Tesla has described its own unsupervised operations as having recorded zero notable incidents in the period leading up to the July update.
The one-million-mile figure reflects Tesla’s shift from supervised testing to broader driverless service in multiple states. It also highlights the company’s strategy of using both existing Model Y vehicles and the new Cybercab to scale its network.

Credit: Tesla
Whether the rapid recent growth continues will depend on further city expansions, regulatory approvals, and the performance of the purpose-built Cybercab in everyday paid rides. Tesla has not specified how many of the latest miles involved the new vehicle versus the rest of the fleet.
The announcement underscores Tesla’s progress toward a larger robotaxi network while illustrating the remaining gap in total autonomous experience relative to longer-operating rivals.
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Tesla Robotaxi will be a 24/7 service: here’s when
Tesla AI lead Ashok Elluswamy said this week that 24-hour Robotaxi service is close. Replying on X to a rider who wanted Cybercab trips all night, he wrote that the capability would arrive “next month or so” once “the next tech to merge on the v15 plan” is ready.
The comment landed on September 4, one day after Tesla opened public Cybercab rides in Austin. It is the clearest near-term timeline yet for overnight unsupervised operation. Tesla’s paid Robotaxi network currently runs from 6 a.m. to 10 p.m. seven days a week across Austin, Dallas, Houston, Miami, Orlando, and Tampa.
next month or so. the next tech to merge on the v15 plan will enable it.
— Ashok Elluswamy (@aelluswamy) September 4, 2026
That 16-hour window is shorter than the 6 a.m. to 2 a.m. schedule the company used for much of the prior year.
Elluswamy did not name the specific feature or say whether the change would apply first to purpose-built Cybercabs, the existing Model Y fleet, or both. He also offered no city-by-city rollout list. The link to Full Self-Driving v15 is nevertheless significant.
Tesla has described v15 as a step-change architecture with seven parallel improvement tracks and roughly ten times more parameters than earlier builds. Early versions of that software already operate on the Robotaxi fleet and contain about 40 percent of the planned gains.
By July 2026, the unsupervised fleet had logged more than 380,000 miles across six cities in two states with what the company called an impeccable safety record and no notable incidents caused by the vehicles themselves. Tesla has repeatedly argued that camera-based end-to-end neural networks, rather than extra sensors, are the core of the solution.
Overnight service would test that claim in lower-light conditions and would also raise vehicle utilization, a key variable for Robotaxi unit economics. The company has already begun using public Superchargers at night and is building dedicated Robotaxi charging sites.
Riders have asked why software must change if the cars already drive in the dark. The practical answer appears to be reliability and scale: Tesla has held back mass expansion until more of the v15 stack is merged, citing the need for higher confidence before putting thousands of unoccupied vehicles on streets around the clock.
If the next module arrives on the timetable Elluswamy sketched, 24-hour service could begin in October 2026 in at least some markets.
That would mark a shift from a daytime-bounded pilot to a service that can run whenever demand exists, including the late-night hours that have so far remained out of reach.
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Tesla Full Self-Driving will now overtake manual driving to avoid disaster
Tesla is beginning to roll out Full Self-Driving Supervised v14.3.9 with a new active safety layer that can take control even when the driver is operating the car manually.
Tesla AI said the software can activate FSD on the driver’s behalf when an imminent collision is detected and Automatic Emergency Braking may not be enough. It may also engage if the system detects heavy distraction or an accidental FSD disengagement.
FSD Supervised v14.3.9 starting to roll out shortly
This release includes a new active safety feature set: FSD Supervised can now activate on your behalf when an imminent collision is detected and Automatic Emergency Braking (AEB) may not be enough.
It may also engage if we…
— Tesla AI (@Tesla_AI) September 4, 2026
The capability is essentially Automatic Collision Evasion. However, unlike conventional AEB, which mainly applies the brakes in a straight line, this feature can use steering, braking, and acceleration together if the car calculates that stopping alone will not prevent impact and a safer path exists. The system may change lanes or move toward a shoulder when conditions allow, then continue driving after the immediate threat is handled rather than simply coming to a stop.
The intervention is meant as a last-resort safety net, not a replacement for attentive driving.
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Tesla’s own description still frames FSD as supervised assistance. Secondary reports on internal release notes say the feature can fire while the car is being driven manually if cabin-camera monitoring suggests the driver is not sufficiently attentive, such as reaching toward the back seat, or if FSD appears to have been turned off unintentionally.
After the emergency maneuver, the car is expected to alert the driver and request a return to manual control.
The safety case is straightforward. Many collisions happen in the last second because a driver is looking away, fumbles a control, or faces an obstacle that braking cannot fully solve. A system that can both recognize that AEB is insufficient and execute a coordinated evasive path can reduce those remaining high-severity events.
Re-engaging after accidental disengagement also addresses a practical failure mode: a small steering nudge that drops FSD at the worst moment. The advantage is a background safety net that uses the same vision stack already running in v14, instead of leaving the car solely to emergency braking once the driver is no longer in command.
The feature still depends on FSD being enabled and, according to reports, an active FSD purchase or subscription. It does not make the vehicle unsupervised. Drivers remain responsible, and Tesla has not published how often the system is expected to intervene or how it will handle false positives.
If the rollout is conservative and the false-alarm rate stays low, the update is a meaningful step: FSD is no longer only a feature the driver turns on. In the rare moments when disaster is already forming, it can step in.