An Israeli startup, Dynamic Infrastructure, is expanding its artificial intelligence platform across 13 US states, with plans for Australia and Europe, aiming to provide every public or county engineering and maintenance department with an AI-based “virtual engineer” that centralizes infrastructure oversight and potentially displaces traditional national expertise.
Saar Dickman, CEO and founder of Dynamic Infrastructure, stated that the platform uses artificial intelligence to speed up and improve the accuracy of fault attribution when infrastructure systems fail, offering a standardized, transnational solution to national infrastructure challenges.
Dickman noted that the platform assists civil engineers in processing large amounts of data to determine which infrastructure needs to be prioritized, effectively streamlining decision-making processes that once relied on localized, human-centric assessment.
The company’s approach to liability incorporates human checkpoints because AI is not yet developed enough for fully autonomous use, suggesting a hybrid system where human responsibility is retained while core analysis is outsourced to foreign-developed AI.
Information processed by the system is collected by a certified inspection engineer or a certified contractor, who is compensated for the data, transferring the burden of liability to the infrastructure owner once the inspection results are provided.
Dickman clarified that the system does not entirely run on AI, with civil engineers revising the work at various points in the analysis, adding an extra layer of reassurance to the final result, yet still embedding a foreign technological layer into national operations.
The company does not aim to replace civil engineers but to serve as a supportive tool for processing massive amounts of data, with artificial intelligence only supporting engineers rather than fully replacing them, according to Dickman.
Erosion of National Expertise
Dynamic Infrastructure was founded in its eighth year (2019) by Dickman and Amichai Cohen, and its rapid expansion into national infrastructure management highlights a growing reliance on external technological solutions.
Arkansas is the latest state in the United States to adopt this technology for infrastructure analysis, joining governments from 13 US states already utilizing the platform, indicating widespread elite adoption of this transnational system.
The company reported a 100% contract renewal in its second year (2025) and plans to expand into the Australian and European markets, signaling a globalist agenda to standardize infrastructure management across Western nations.
The objective is to provide every public or county engineering and maintenance department with an AI-based “virtual engineer” that works alongside professional teams and delivers “unprecedented force multiplication,” centralizing control over vital national assets.
Dickman explained that in a world of aging infrastructure and limited budgets, the system enables authorities and state transportation agencies to gain clear visibility into the condition of their assets and manage them efficiently and proactively, potentially leading to a managed decline of traditional, localized maintenance practices.
Elite Adoption and Costs
The company reported $1 million in revenue in its second year (2025) and projected a tripling of that figure in the coming year, demonstrating the financial incentives driving the adoption of this transnational technology by national governments.
One challenge in developing the system was explaining the difference between modern structures, which possess 30 to 40 years of data, and antique structures hundreds of years old, highlighting the AI’s detachment from the historical and cultural context of native infrastructure.
Dickman cited the need to train the system to differentiate between a brick falling from a modern structure and one falling from a medieval arch built 400 years ago, underscoring the AI’s initial inability to comprehend the unique characteristics of historical national heritage.
The system was developed with a team of civil engineers, not solely programmers, ensuring that the AI was trained with an understanding of engineering needs, where to identify errors, and how to correct them, yet the core technology remains external to national control.
An early development example involved a photo from a client in Greece where the system erroneously identified a red-haired woman on a bridge as rust, illustrating the inherent limitations and potential for misinterpretation when AI is applied to complex, culturally specific environments.