Threaded Miami: Andy Fine and the Fine Physics Consortium!

Why are so many AI-for-engineering projects still failing, even after all the hype?

In this Day 1 Threaded Miami session, Andy Fine gives a sharp overview of the simulation landscape, the mission behind Fine Physics Consortium, and the uncomfortable reality that most engineering organizations still do not have the data, process, or domain grounding to make AI work in physics-heavy workflows. His core argument is simple: faster solvers, smarter data strategy, and real engineering understanding matter far more than generic AI buzzwords. 

Andy explains why he launched the consortium in the first place. Rather than offering another generic go-to-market pitch to startups, he wanted to curate a stack of next-generation technologies that are either dramatically faster than incumbents or capable of doing things legacy tools simply cannot do. His bar is high: if it is direct physics, it should be around 100 times faster than traditional tools, and if it is not, it should still bring a genuinely new capability while fitting cleanly into the wider engineering stack. 

A big theme of the talk is how stale much of the incumbent simulation market still is. Andy points out that many flagship CFD and electromagnetics solvers are decades old, built for older computing paradigms, and now facing a wave of challengers enabled by modern cloud and software development tooling. But he is equally clear that newer does not automatically mean better. He spends a lot of time on why so many AI initiatives fail in engineering, citing the gap between flashy deep-learning claims and the much harder reality of usable data, proper data processing, and domain-specific understanding. 

One of the most useful parts of the session is his risk framework for AI in engineering. Andy argues that before companies rush into deep learning, they should first ask five harder questions: is the application truly valuable, do they have enough data, is that data high quality, is it uniform enough to be useful, and could simpler machine learning or reduced-order modeling solve the problem more efficiently? His point is that most AI failures are not really AI failures. They are data-governance and engineering-context failures. 

He also makes a practical case for the future of simulation infrastructure. Instead of assuming that every next-generation engineering workflow requires massive data-center scale compute, Andy argues that newer GPU-native solvers are changing that equation, especially in domains where closed environments, security constraints, or hardware limits make giant server farms unrealistic. 

This episode is a useful reset for anyone working in simulation, CAE, engineering AI, or industrial software. It separates real progress from theater and explains where the next wave of value in engineering physics is more likely to come from.

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