Can a factory think for itself? That’s the idea Siemens and NVIDIA floated at CES 2026 — only they didn’t call it science fiction. They called it an Industrial AI operating system: a stack that strings together GPU-accelerated simulation, generative simulation models and physics-aware digital twins so companies can design, test and run real-world systems virtually before touching a single bolt.
The announcement is both technical and strategic. At its heart sits a simple promise: speed up engineering and shrink risk by running more accurate, faster simulations. NVIDIA explained the initiative in its newsroom, laying out plans to provide AI infrastructure, simulation libraries, models and blueprints while Siemens contributes its industrial software, domain experts and automation know-how. Together they want to remake everything from semiconductor design to whole factories.
What they’re building (and who’s testing it)
Siemens will complete GPU acceleration across its simulation portfolio, integrating NVIDIA CUDA-X libraries and PhysicsNeMo models into electronic design automation (EDA) tools. That’s the heavy stuff: layout, verification and process optimization for chips. Siemens and NVIDIA are targeting roughly 2x–10x speedups in key EDA workflows and adding AI-assisted capabilities such as layout guidance and debug support to boost engineer productivity.
On the factory side, Siemens introduced a new Digital Twin Composer built on NVIDIA Omniverse libraries; PepsiCo announced a multi-year collaboration to convert selected plants and warehouses into high-fidelity 3D digital twins. PepsiCo says early pilots already produced a roughly 20% increase in throughput, near-complete design validation and 10–15% savings in capex by uncovering hidden capacity before any physical change.
NVIDIA’s Jensen Huang framed the effort as a transformation of digital twins from passive models into active intelligence. Roland Busch of Siemens described the work as a way to "redefine how the physical world is designed, built and run" — starting with a Siemens electronics factory in Erlangen as a blueprint for the so-called AI factory.
Why this matters beyond marketing
Two trends collide here. First, digital twins have matured: photorealistic, physics-accurate models plus streaming sensor data let simulations reflect reality closely. Second, GPUs and generative-simulation models have made running complex scenarios fast and economical. Combine them and companies can test dozens or thousands of operational changes in software, then push validated ones to the shop floor.
For chip makers, that could shave months from design cycles. For manufacturers and supply chains, it promises fewer surprises, faster rollouts and the ability to plan capacity without immediate capital expenditure. Customers already evaluating the platform include Foxconn, HD Hyundai and KION Group, alongside PepsiCo.
The technical underpinnings — and limits
NVIDIA brings Omniverse libraries, CUDA acceleration and PhysicsNeMo models for physics-aware generative simulation. Siemens brings EDA tools and domain-specific industrial stacks. Together they aim to push workloads that once ran on CPUs into GPUs, allowing higher-fidelity models at lower run times.
That raises practical challenges: energy, cooling and infrastructure at scale. The companies say they will design blueprints balancing density, power and automation needs, but deploying GPU-heavy environments at factories and across global supply chains is a nontrivial task — one that echoes other ambitious infrastructure efforts in the AI era (see related work on distributed and experimental data-center concepts like Project Suncatcher).
There are also software and integration hurdles. EDA has painfully strict manufacturability constraints; AI-assisted layout must honor those rules or risk costly silicon re-spins. The partners are betting that GPU-accelerated simulation plus domain-aware models will bridge that gap.
A practical glimpse: PepsiCo’s pilots
PepsiCo is using Siemens' Digital Twin Composer and NVIDIA Omniverse to map machines, conveyors, pallets and operator paths at selected U.S. facilities. The company reports rapid validation of new layouts and the ability to simulate agents that anticipate and adapt to demand.
That pilot is an early real-world test of the Industrial AI thesis: if physics-accurate digital twins plus AI agents can identify 90% of issues before physical changes and boost throughput materially, the economic case for broader adoption becomes compelling.
What to watch on adoption and safety
Expect a phased rollout. Siemens and NVIDIA will likely show more proofs of concept across verticals before enterprise-wide deployments. Watch for metrics on energy use and real-world yield improvements from semiconductor partners. Also watch how these systems handle real-time safety and compliance constraints — a digital twin that suggests faster cycle times is useful only if it doesn’t put workers or equipment at risk.
This push also intersects with broader questions about how AI systems get their data, how models are validated and how operators trust automated recommendations. As industries stitch AI into the physical world, the stakes — and the scrutiny — rise. For a sense of how AI is being woven into consumer and enterprise products elsewhere, note how research and search tools are evolving (for example, Gemini Deep Research).
If the Siemens–NVIDIA partnership delivers on its speed and fidelity promises, we could be looking at the next layer of industrial digitization: not just digital twins that mirror factories, but twins that help run them autonomously, and do so with engineering-grade rigor. For now, the pilots with PepsiCo and the semiconductor accelerations are the most tangible evidence that the idea is moving from slides to shop floors.
Sources: NVIDIA Newsroom, Siemens and PepsiCo announcements reported at CES 2026.