You’ve probably never heard of Carl Hewitt. Or maybe you have, if you’ve spent any time digging through the dusty basements of MIT’s computer science archives. Back in 1973, Hewitt, along with Peter Bishop and Richard Steiger, introduced something called the actor theory of everything (formally known as the Actor Model). It wasn't just another boring coding framework. It was a radical, almost philosophical shift in how we think about machines talking to each other.
At the time, people thought he was a bit out there.
Computers were mostly sequential then. You gave a command, the computer did it, and then it moved to the next line. Linear. Predictable. But Hewitt looked at the world and saw something different. He saw a messy, chaotic swarm of independent entities—"actors"—that don't share a brain, don't wait for permission, and certainly don't follow a single, central clock. He was trying to build a digital version of a physics "theory of everything," a unified way to handle how information moves across massive, distributed networks.
Honestly, we are only just now catching up to what he realized fifty years ago.
What the Actor Theory of Everything Actually Is
Think of an actor like a person with a private mailbox. That’s basically it. An actor is a tiny, self-contained unit of "stuff" that can do exactly three things when it receives a message:
- Send a bunch of messages to other actors it knows.
- Create new actors to help with a task.
- Decide how to handle the next message it gets.
That’s the whole ballgame. No shared memory. No "global state" where every part of the program is reaching into the same cookie jar at the same time. If Actor A wants Actor B to change something, it can't just reach over and flip a switch. It has to send a letter.
This matters because sharing memory is where modern computing breaks. If you have two different parts of a program trying to update a user's bank balance at the exact same microsecond, and they both grab the same piece of data, things go south. Fast. You get "race conditions," crashes, and corrupted data. The actor theory of everything solves this by saying, "Nobody touches my data but me."
It’s isolated. It’s safe. It’s incredibly fast when you scale it up to thousands of processors.
Why "Theory of Everything"?
Hewitt wasn't being humble. He argued that the Actor Model was the fundamental building block of all digital logic. Whether you were building a tiny calculator or a global artificial intelligence, he believed the actor was the "atom" of computation.
In his view, even the most complex systems are just actors all the way down.
🔗 Read more: What Does Zulu Mean in Time? Why the World Operates on One Clock
The Erlang Connection and Why WhatsApp Works
If this sounds like academic fluff, look at your phone. If you use WhatsApp, you are using the actor theory in its most successful real-world form. WhatsApp was built using a language called Erlang.
Erlang was created by Joe Armstrong at Ericsson in the 80s to handle massive telephone exchanges. He needed a system that never, ever went down. If one switch failed, the whole network couldn't just stop. He independently arrived at a system that mirrored Hewitt's actor model.
In WhatsApp, every single person chatting is represented by a tiny "actor" (a process) on a server. When you send a "Hey" to your friend, your actor sends a message to their actor. Because these actors are isolated, if one person's connection glitches, it doesn't crash the server for the other 2 billion users.
It’s distributed. It’s resilient. It’s basically magic.
And it’s not just WhatsApp. Discord uses it. Cloudflare uses it. Even the massive multiplayer backends for games like League of Legends rely on these principles to keep thousands of players in sync without the whole thing exploding into a fiery heap of lag.
The Massive Misconception: It’s Not Just Object-Oriented Programming
A lot of people—even some senior devs—think the actor theory is just Object-Oriented Programming (OOP) with a fancy name.
Nope.
In standard OOP (like Java or C++), when you call a "method" on an object, you are usually handing over control. You wait for it to finish. In the actor model, you send the message and then you go about your business. You don't wait. You don't care if the other actor is busy or sleeping. You just drop the mail in the slot and move on. This "asynchronous" nature is the secret sauce.
Constraints and Hard Truths
It isn't a silver bullet.
The biggest headache with the actor model is that it's hard to reason about. Humans like sequences. We like "A then B then C." In an actor-based system, everything is happening at once. Messages can arrive out of order. An actor might die halfway through a task.
Debugging a system where ten thousand actors are shouting at each other is a nightmare compared to debugging a single line of code. You have to think in terms of "eventual consistency." You have to accept that you might not know the exact state of the whole system at any given moment.
That’s a scary thought for someone used to total control.
Why We’re Talking About This in 2026
We are hitting a wall with hardware. We can't make individual processor cores much faster without them melting through the motherboard. Instead, we’re just adding more cores. 8 cores, 16 cores, 64 cores.
Standard programming isn't built for this. It’s like trying to run a city's traffic through a single-lane bridge.
The actor theory of everything is the multi-lane highway. Because actors don't share memory, they can run on different cores—or even different servers across the world—without ever getting in each other's way. This is why languages like Elixir and frameworks like Akka (for Java/Scala) are seeing a massive resurgence.
If you want to build something that handles millions of concurrent users, you basically have to use Hewitt’s ideas. There is no other way to scale.
Real-World Impact: More Than Just Code
Interestingly, the actor model has started bleeding into how we think about organizational management and even biology.
Biological cells act a lot like actors. They don't have a "central commander." They respond to chemical signals (messages), change their internal state, and signal their neighbors. Some systems theorists argue that our bodies are the ultimate implementation of actor theory.
In business, "Microservices" are essentially the actor model applied to company architecture. Instead of one giant, monolithic software program, companies break their tech into tiny, independent teams (actors) that communicate via APIs (messages). It allows Amazon or Netflix to update their site a thousand times a day without taking the whole thing offline.
How to Actually Apply This
If you’re a developer or a tech lead, you don't need to go out and rewrite everything in Erlang tomorrow. That would be a disaster. But you should start leaning into "Actor-ish" patterns.
Start by isolating state. If you find yourself using global variables or shared databases where everyone is writing to the same row at once, stop. You're building a bottleneck.
Use message queues. Tools like RabbitMQ or Kafka are essentially big "actor mailboxes" for your entire infrastructure. They allow different parts of your system to talk without being tightly coupled.
Actionable Next Steps:
- Audit your bottlenecks: Look for any part of your system where multiple processes are waiting for a single "lock" on a database or file. That’s your first candidate for an actor-style refactor.
- Study the "Let it Crash" philosophy: Read up on Erlang’s approach to error handling. Instead of trying to catch every single error, you build "supervisors" that just restart actors when they fail. It sounds counterintuitive, but it’s the key to 99.999% uptime.
- Experiment with Elixir: If you want to see the actor model in its most modern, beautiful form, spend a weekend playing with the Elixir programming language. It brings Hewitt’s 1973 theories into the modern era with a syntax that won't make your eyes bleed.
- Map your communication: Literally draw out how data flows between your services. If it looks like a spiderweb where everything touches everything, you haven't implemented actors; you've built a "distributed monolith." Simplify the paths.
The actor theory of everything isn't just a relic of 70s computer science. It is the blueprint for the next generation of the internet. As we move further into the world of AI agents and edge computing, the "actor" is going to be the only thing standing between us and total digital chaos.