Nvidia CEO Jensen Huang and Dassault Systèmes CEO Pascal Daloz announced a major partnership combining virtual twin technologies with AI infrastructure, open models, and accelerated computing libraries. The collaboration represents a fundamental shift in how industrial companies will approach engineering, design, and manufacturing in the age of artificial intelligence.

What Does This Partnership Actually Mean for Engineers?

At its core, the Nvidia-Dassault partnership commits both companies to integrating Virtual Twin technologies, which are digital replicas of physical systems, with AI infrastructure and Nvidia's CUDA-X libraries, which are specialized software tools that accelerate computing tasks. Nvidia is also adopting Dassault's model-based systems engineering (MBSE) platform to design its own AI factories, marking the first time Nvidia has outsourced this critical design work.

The partnership introduces four major application areas that will reshape industrial work:

• Molecular Discovery: Nvidia's BioNeMo platform, integrated with Dassault's Biovia science-validated world models, targets molecular and materials discovery for pharmaceutical and materials science companies.
• Physics Simulation: SIMULIA's AI-based virtual twin physics draws on Nvidia's CUDA-X libraries to simulate real-world physical behavior before manufacturing begins.
• Production Systems: DELMIA's software-defined production systems run on Nvidia Omniverse physical AI, enabling factories to optimize operations in real time.
• AI Companions: Dassault introduced three AI agents at the 3DExperience World conference: Aura for knowledge orchestration, Leo for engineering challenges inspired by Leonardo da Vinci, and Marie for scientific expertise inspired by Marie Curie.

These companions operate within an engineer's own environment, are not shared or cloud-based, and amplify capability without replacing human judgment.

How Will AI Companions Change Engineering Work?

Huang addressed a widespread concern head-on: the fear that AI will eliminate engineering jobs. His argument reframes the relationship between engineers and AI entirely. Rather than replacing engineers, AI companions will work alongside them, each trained in specific skills, domain knowledge, and design preferences.

"For every engineer that uses Dassault tools, there will probably be 100 AI agents that also use the tools," said Jensen Huang, Nvidia founder and CEO.

Jensen Huang, Founder and CEO at Nvidia

Huang described the companion model as a repository of codified expertise built up over a career. Each companion learns from an engineer's inbox, preferences, and past decisions, then continues working on related tasks. The result is not automation of the past, but amplification of human capability to invent the future.

Dassault's CEO Pascal Daloz emphasized this distinction: engineers don't want to automate routine work; they want to expand their creative capacity. This shift from computer-aided design (CAD) to what Daloz calls "cognitive aided design" will open design capability to a far wider professional constituency, including smaller manufacturers who previously lacked the resources for automation.

Pascal Daloz

Why Did Nvidia Choose Houston to Announce This?

The venue for the announcement carried symbolic weight. Nvidia chose Houston, home to NASA's Johnson Space Center and the operational hub of the US space program, rather than Silicon Valley. The city is defined by the application of engineering rather than research and development, making it the perfect audience for Huang's argument about AI as industrial infrastructure.

"Just as water was infrastructure, electricity is infrastructure, internet was infrastructure, now artificial intelligence will be infrastructure," said Jensen Huang.

Jensen Huang, Founder and CEO at Nvidia

Huang's position centered on a radical claim: in the past, engineers spent roughly one-third of their working time in the digital domain. In the future, that proportion reaches 100 percent. This redefines the conditions of engineering work itself, not merely its efficiency. Tools, platforms, and digital design become the infrastructure of industry.

What Is an AI Factory, and Why Does It Matter?

The physical expression of this infrastructure is the AI factory: gigawatt-scale facilities with capital costs of approximately 50 billion dollars per installation. These are not traditional manufacturing plants but rather facilities that produce intelligence itself, supplying the cognitive capacity of industries, research institutions, and governments.

Nvidia is already applying this concept to its own operations. The company is using Dassault's MBSE platform to design its Rubin-generation AI factories, planning and simulating systems in a virtual twin before construction begins. This approach eliminates costly mistakes by making everything digital-first.

The AI factory supply chain spans three distinct industrial categories, all expanding in parallel: chip fabrication plants, computer assembly facilities, and the AI factories themselves. This represents what Huang describes as "the largest industrial infrastructure build-out in human history".

How Will This Partnership Help Smaller Manufacturers?

Small and medium manufacturers face a significant barrier to automation: programming complexity. A robot configured for a single high-volume automotive task requires software engineering investment that precision manufacturers in the 50 million dollar bracket cannot justify. However, AI-driven robots that learn from demonstration rather than explicit programming alter this calculation fundamentally.

"They're finally going to be lifted, not left behind," said Jensen Huang, referring to smaller manufacturers gaining access to automation technology.

Jensen Huang, Founder and CEO at Nvidia

This democratization of automation technology could reshape the competitive landscape for mid-sized manufacturers, enabling them to adopt advanced production techniques without the massive software engineering investment previously required.

What About Energy Costs and Grid Modernization?

Huang's position on energy runs counter to the standard narrative about AI's power demands. While AI infrastructure does place substantial pressure on power grids, Huang argues this creates a market-driven force for grid modernization. Social and regulatory pressure for sustainable energy has historically lacked the economic incentive for rapid capital deployment, but AI infrastructure demand supplies it.

According to Huang, energy costs will actually drop as a result of this infrastructure investment. When energy supply increases due to market demand, energy costs move down. This creates a virtuous cycle where AI infrastructure investment drives grid modernization, which in turn reduces energy costs for all users.

The Nvidia-Dassault partnership represents a watershed moment in industrial AI. By combining virtual twin technology with AI infrastructure and introducing AI companions that amplify rather than replace human expertise, the two companies are laying the groundwork for a fundamental transformation in how engineering work gets done. The message is clear: AI is not coming to eliminate engineers; it is coming to make them more powerful.