V11 Participates in Atoms’ Series A

V11 is pleased to announce its participation in the Series A financing of Atoms, Travis Kalanick’s industrial automation platform focused on applying AI, robotics, and software to repetitive physical work across food, mining, and transportation.

Atoms raised $1.7 billion in July 2026 in a financing led by Andreessen Horowitz, with Ben Horowitz joining the company’s board.

We believe Atoms represents an emerging model for physical AI: rather than building robotics technology in isolation and then searching for applications, the company is developing automation directly inside real operating environments.

From Software to Physical Automation

The first wave of AI adoption has largely focused on digital workflows. The next frontier is increasingly physical.

Labor-intensive industries such as food production, mining, logistics, and manufacturing still depend on large amounts of repetitive human work. Advances in autonomy, computer vision, robotics, and AI are beginning to make more of these workflows economically automatable.

Atoms is targeting environments where tasks, routes, and inputs are sufficiently predictable for automation to produce measurable improvements in labor efficiency, equipment utilization, reliability, and operating cost.

The company currently operates across three primary divisions:

I - Food

Atoms’ food ecosystem includes CloudKitchens, ProFood, Otter, Lab37, and Picnic, spanning kitchen infrastructure, restaurant software, order management, food preparation, and distribution.

The objective is to automate increasingly large portions of the food-production workflow, reducing labor requirements while improving consistency and throughput.

II - Mining

Through Pronto AI, Atoms is applying autonomous-driving technology to industrial trucks used in mines and quarries.

Unlike systems designed primarily for new fleets, Pronto can retrofit existing vehicles, allowing operators to automate equipment they already own. The potential value proposition is straightforward: higher equipment utilization, lower labor intensity, and reduced safety exposure in active mining environments.

III - Transport

Atoms is also developing a standardized mobility platform designed to support multiple robotic systems and payloads.

Instead of engineering a new vehicle platform for every industrial application, the company aims to reuse the same underlying movement, power, and control infrastructure across different automation use cases.

The Operating Model Is the Thesis

What we find particularly compelling is not simply Atoms’ exposure to several large industrial markets. It is how the company develops automation. Atoms can deploy technology inside businesses and facilities it already operates, giving it direct access to real workflows, deployment environments, and operating data.

That creates a feedback loop: operate → automate → collect data → improve economics → expand deployment

Rather than depending entirely on external customers to validate new technology, Atoms can test systems internally, measure cost savings, identify failures, and improve performance before scaling them more broadly.

The same capabilities in autonomy, computer vision, fleet management, robotics, and operational software can then potentially be reused across multiple industries.

This is what makes Atoms particularly interesting to us: the company is not simply developing individual robots. It is building a platform for converting labor-intensive physical operations into increasingly automated infrastructure.

Why Now

We believe physical AI is approaching an important adoption inflection point. AI models are becoming more capable, sensors and compute are improving, autonomy technology has matured significantly, and industrial operators are under continued pressure to improve productivity and reduce labor dependency.

As these enabling technologies converge, the economics of automation are beginning to improve across a broader set of real-world applications.

The critical question is shifting from “Can machines perform the task?” toward “Can automation perform the task more economically and reliably than the existing workflow?”

Atoms is designed around that second question. Its focus is ultimately on reducing the labor cost required to produce and move physical goods—whether measured as worker hours per meal, driver hours per ton moved, or operator hours per industrial task.

Building the Infrastructure for Physical AI

At V11, we look for companies positioned at the intersection of major technological shifts and large economic markets.

Atoms sits at the convergence of artificial intelligence, robotics, autonomy, and industrial infrastructure. The opportunity extends beyond any individual robot or application.

If Atoms can repeatedly identify labor-intensive workflows, automate them inside controlled operating environments, demonstrate superior unit economics, and then replicate that model across industries, we believe it has the potential to become an important platform in the transition from digital AI to physical AI.

We are excited to participate in the company’s Series A and support its next phase of growth.