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Every March, everything seems to converge at once—Nvidia Graphics Processing Unit Tech Conference (GTC), Game Developers Conference (GDC), ETH SF, and International Women’s Month. It’s the kind of overlap that makes you feel like you should be everywhere, seeing everything, keeping up with it all.
This year, I made a different call.
Instead of running between conferences, I stayed in—heads down building my app and refining something I’ve been thinking a lot about lately: a personal Life Operating System. Not only productivity, but how I structure time, attention, inputs, and outputs—how I actually play my days.
And ironically, stepping back made one thing clearer than ever: a lot of what we’re watching unfold in AI started with games.
Not only chess or Go—but environments, simulations, feedback loops. Systems where intelligence emerges through play.
So instead of another conference recap, this is the post I’ve been wanting to write:
GameAI, the simulated multiverse, and how ideas like self-play and reinforcement learning connect all the way to Game Theory Optimal (GTO) poker—and even how I think about building my own systems for life.
Because once you see it, it’s hard to unsee:
We didn’t only teach AI how to play games.
We used games to understand intelligence itself.
And maybe that says something about the game we’re in, too.
In This Post
“How Did AI Come To Be Today?”
Outside of the founder story or the very technical jargon of talking about the evolution of the paper “Attention is All You Need,” and the transformer algorithm, let’s talk about OpenAI Gym and its predecessors in GameAI to understand the early foundations of AI.
OpenAI Gym (2018) as the foundation: AI learned intelligence by playing games.
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When I first started writing my Creating Augmented and Virtual Realities: Theory and Practice Next-Generation Spatial Computing book in 2018, I wanted the book to cover two key areas: computer vision, AI, data visualization (my favorite topics), and cross-platform development (programming apps across different Head Mounted Displays - HMDs). At the time, the head of Deep Learning Research at Unity, Nicolas Meauleau and Arthur Juliani (Deep Learning Engineer) presented me with their chapter idea on AI agents within a VR setting. This graphic below introduces their framework for understanding AI agents within an XR environment. They show the architecture for what they call the “decision layer,” which plans a behavior and takes various actions (namely motion), both by AI and Non-Player-Characters (NPCs) in XR applications and video games.
Figure 10-1 from the chapter of former Deep Learning team at Unity, Nicolas Meuleau and Arthur Juliani “Character AI and Behaviors” of our O’Reilly Media Book, Creating Augmented and Virtual Realities: Theory and Practice for Next-Generation Spatial Computing
“Are We All Playing a Game?”
As author of Creating Augmented and Virtual Realities: Theory and Practice Next-Generation Spatial Computing, I saw that our book ranked #2 on Book Authority’s list of top VR books after we published in 2019 as one of the top VR books to read. What ranked #1 was The Simulation Hypothesis: An MIT Computer Scientist Shows Why AI, Quantum Physics, and Eastern Mystics All Agree We Are in a Video Game by Rizwan Virk. The book is great, and the sequel, The Simulated Multiverse: An MIT Computer Scientist Explores Parallel Universes, The Simulation Hypothesis, Quantum Computing and the Mandela Effect is even better.
If you know me, you know I’ve been obsessed with the idea that life is a game—long before AI made that framing mainstream. Years ago, I remember a conversation with DJ Q-Bert talking about how life was like a video game, very similar to the entire framework of the book, Reality is Broken: Why Games Makes Us Better and How They Can Change The World by Jane McGonigal.
The “meta” realization: if we are characters in a game, and OpenAI Gym showed us AI can level up by playing — what does that mean for how we play our own lives?
“Upgrading Your Engine”
Part of the reason I named this Substack—and my broader brand—Creating Your Reality (aside from referencing my first technical book, Creating Augmented and Virtual Realities) is that I kept encountering the idea everywhere: you create your reality.
One of the most compelling versions of this comes from Shannon Lee, daughter of Bruce Lee, in Be Water, My Friend: The Teachings of Bruce Lee
“We create our reality by how we choose to act and how we choose to respond to things outside ourselves from within ourselves.”
This idea shows up across modern creator culture as well. TikTok creator @cherrylimesoda frames it in a more “meta” way: if we are all characters playing a game—echoing early environments like OpenAI Gym—then we are constantly updating our own “software” through self-belief, self-development, and self-talk. In other words, a form of self-play.
A similar framework comes from Courtney Alexis Cho:
The subconscious stores self-talk as “reality data” → looped input → looped outcome.
She puts it simply:
“Reality is the PDF. Your identity is the file. If you don’t like what’s being printed, you don’t throw away the printer—you change the document.”
In this framing:
What Cho is describing maps surprisingly well to reinforcement learning (RL).
RL—especially in its modern form with Reinforcement Learning with Human Feedback (RLHF)—is about optimizing behavior through repeated signals and feedback loops.
Now apply that to ourselves:
Reframed:
What we repeat, our system optimizes for.
Across these philosophies, the mechanism is consistent:
This isn’t about wishful thinking—it’s about changing our information architecture.
Overriding negative loops isn’t “delusion.”
It’s updating the system.
There’s an important distinction here:
And yet, we live in a world governed by systems, math, and proofs.
So in a strange way:
Our beliefs become the “proof” we submit to our own system.
Both Bruce Lee and Courtney Cho are pointing to the same core idea:
We are not just reacting to reality—we are participating in shaping it.
If life behaves like a game, then:
To level up, we don’t just try harder.
We:
Because in the end, like any learning system—
we become what we repeatedly optimize for.
“From AI to the Poker Table”
Many people swear by this technique — one of my favorite poker players to watch is Randy Lew (known as @nananako on X/Twitter) who plays 24 poker tables online simultaneously! This is pattern matching (for context, managing 6 codebases and 3–7 foundation models at a time is a lot for me personally — and definitely a lot more cognitive overload in context-switching than most can handle).
Personally, hearing Andrej Karpathy reference — during the No Prior podcast — the founder of OpenClaw, Peter Steinberger, managing a setup of 10 simultaneous terminals resonated with me: it’s similar to running 6 codebases and 3–7 foundation models in parallel to build out an app and a website replicated across various stacks. // Check out the great podcast with Peter Steinberger and Lex Friedman btw.
Through AI and our use of pattern matching, we have been able to simplify and optimize our software engineering (app development and programming) workloads by offloading cognitive load to a machine — the same way we can distill GTO instincts for a human down to a few key actions, enabling them to monitor and judge gameplay across a number of simultaneously running games.
The deeper point: GTO isn’t just poker strategy. It’s a decision-making framework for life.
We didn’t only teach AI how to play games.
We used games to understand intelligence itself.
The question is: what are you optimizing for?
I’m building a LifeOS to make that visible — a system for tracking how you actually play your days, not only what you plan.
If you want early access, sign up to join the beta below.
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About Erin
Erin Jerri Malonzo Pañgilinan is a software engineer and computational designer. She is the lead author of the O’Reilly Media book Creating Augmented and Virtual Realities: Theory and Practice for Next-Generation Spatial Computing, which debuted as the #1 book in Amazon’s Game Programming and has been translated into Chinese and Korean with distribution in more than 42 countries.
She was previously a fellow in the University of San Francisco (USF) Data Institute’s Deep Learning Program (2017–2018) and the inaugural Data Ethics cohort (2020) through fast.ai.
She is currently working on new books and software applications exploring the intersection of AI, spatial computing/XR, and web3.
Erin earned her BA from the University of California, Berkeley and is a proud Silicon Valley native born and raised.
Outside of her professional work, Erin has spent over 16 years as a dedicated community organizer and advocate. She co-founded 2 non-profit organizations, FASTER - Filipinx American in STEAM - Science Technology Engineering Arts and Mathematics, ARVR Academy focused on women and underrepresented communities in immersive technology, and has served on the board of Silicon Valley Iceskating Association (SVISA).
In her spare time, Erin enjoys poker, video games, baking, watching anime, performing spoken word poetry, and engaging in the elements of hip hop.
