Concept: Interaction-Nets

The actor model isn’t new. Carl Hewitt introduced it at MIT in 1973, the same year that Ethernet was invented. For fifty years, this elegant model of computation, where independent actors maintain state and communicate through messages, has powered everything from Erlang’s telecom switches to WhatsApp’s billions of messages. But until now it has required specialized runtimes, complex deployment, or significant infrastructure overhead. Today’s “AI agents” are essentially rediscovering what distributed systems engineers have known for decades: isolated, message-passing actors are the natural way to build resilient, scalable systems.

The technology industry has developed an unfortunate habit of wrapping straightforward engineering advances in mystical language. When sites online boast of claims to “blur the lines between P and NP,” they’re usually describing something far more mundane: dealing with technology problems more efficiently. The mathematical complexity remains unchanged, but it shouldn’t be used as a barrier to understanding the practicalities. This isn’t cheating or transcending mathematics - it’s recognizing that most real-world performance barriers come from architectural mismatches, not algorithmic limits.

F#’s computation expressions represent one of the language’s crown jewels - a unified syntax that makes complex control flow feel as natural as writing straight-line code. Yet beneath this syntactic elegance lies a mathematical structure that most compilers never fully exploit. In the Fidelity framework, we highlight that most CEs naturally decomposes into one of two fundamental patterns: delimited continuations for sequential effects, or interaction nets for true parallelism. This isn’t just optimization; it’s a recognition of an essential duality that enables us to develop a compilation path that yields true zero-cost computation graphs.