If the signals from Las Vegas and Silicon Valley are to be believed, 2026 will not just be another year of incremental improvements in AI maturity and capability. It is set to be the year the technology graduates from generating outputs to driving action.
At CES this week, NVIDIA CEO Jensen Huang captured the hardware world’s attention by declaring that the “ChatGPT moment for Physical AI is near,” envisioning a future where machines possess a grounded understanding of the physical laws governing our reality. Simultaneously, the newly released Google Cloud 2026 AI Agent Trends Report reinforces this shift from a software perspective, predicting that we are moving past the era of one-off prompts towards a future where AI will add value to everyday life.
This is a future where I am confident an autonomous engineering workforce will be born. I envisage sophisticated, multi-agent workflows capable of executing complex tasks at the request of an engineer, or even fully autonomously. Simulation workflows will be triggered from a Jira or PLM update, where fundamental changes are assessed and compared against design requirements.
For the engineering community, this convergence of Physical AI and Agentic AI is the signal to stop experimenting and start integrating. The days of using AI for menial tasks are ending. We are entering an era where AI agents act as active colleagues, capable of reasoning through multi-step engineering problems, orchestrating simulation chains, and navigating the intricate physics that govern product performance.
Starting the new year with 800,000 users on SimScale
This shift helps explain the surging momentum we are seeing within our own platform. In barely more than a year, SimScale has welcomed over 200,000 new users, crossing the 800,000 mark globally. Why such a mass migration of engineering capital toward cloud-native infrastructure? These engineers and leaders realize that the “digital assembly lines” – predicted by the Google report – cannot be built on the brittle foundations of legacy, desktop-bound software. To deploy agents that can reason and act on the right decisions, you need cutting-edge, AI-native software running on modern cloud infrastructure.
How are your plans looking for agentic AI’s breakout year?
Although industry expectations are at an all-time high, our State of Engineering AI 2025 survey uncovered a stark “Execution Gap” across the industry. While 93% of engineering leaders expect AI to deliver substantial productivity gains, only 3% report achieving transformational impact. This 10:1 divide exists because many organizations are trying to bolt modern AI tools onto outdated workflows and scattered and inconsistent datasets. For this technology to help rather than hinder, the AI agents and models need to be so deeply and logically integrated into simulation tools that their use becomes second nature.
Leading organizations are adopting a dual-pronged strategy that mirrors the trends identified in the 2026 forecasts. They are deploying Engineering AI to handle the process—the “digital assembly line” that automates setup, meshing, and compliance checks—and Physics AI to handle the predictions, reducing solve times from hours to mere seconds. When these two capabilities merge, the result is a high-value loop where engineers provide design intent, and the system handles the rigorous execution.
The transition to this agentic future is no longer a question of “if,” but “how fast?” The companies that will lead their markets in 2026 are those effectively leveraging these virtual engineers today, enabling their engineering teams focus on continuous, impactful innovation.
To help you navigate this transition, we have developed the AI Capability Index. It allows you to benchmark your organization against the industry, cutting through the noise to identify exactly where your infrastructure stands in relation to the agentic future.
Find out how your team stacks up against our survey results, and we’ll send you the resources you need to move forward.
You can learn more at our Engineering AI Hub, where we are documenting the practical application of these technologies. You’ll find real-world examples and case studies, exploring how industry leaders like Convion (part of HD Hyundai) are successfully incorporating AI into their engineering workflows today.
