Information Physics Field Guide
July 24th, 2025Some discoveries begin in laboratories. Others emerge in offices, libraries, or lecture halls. But a few come from lived experience so intense that patterns become impossible to ignore. This is the story of how navigating humanity’s highest-entropy systems may have revealed universal principles that govern conscious organization — from neural pathways to civilizations.
Right now I could be the world’s most high-functioning delusional, a pattern-matching machine who landed on a nine-word sentence so generally applicable it would make the Home Goods design team jealous, or I may have inadvertently discovered a new way to describe our place in the universe. I’ll let you decide—all I ask is that you keep an open mind until you’ve tested the theory for yourself.
However, one thing I do know about myself is that I’m a practitioner-theorist. Growing up without a formal education or strong parental guidance—though my mother was doing the best she could given her position in the universe, love you Mom! 💜—forced me to be resourceful and develop my own solutions to navigate the systems I found myself in.
I build and test in the mud for sure, but I also like to step back and examine why things are the way they are. When you’re consistently met with stark contradictions of what’s written on paper vs the reality of the system:
- What people think of prison and formerly incarcerated people vs my lived experience.
- The difference between building software for developers vs consuming software built by developers.
- My reality as IC vs leadership—in both Software Engineering and Developer Relations.
I’m consistently forced to reconcile these dual realities.
Einstein realized on a train that physics depends on your reference frame. Nash discovered that optimal strategies depend on what others are doing by studying actual games. Both understood that the observer’s position and context directly change the mathematics. This same principle applies to understanding human systems—your perspective shapes what you can see and measure.
To that point, I ask you, the reader, to do two things:
- Be open to seeing the world through my experience and with the idea that lived experience is the most important source of information. That even Einstein and Nash would have been wrong if they had not considered their lived experience.
- Be objective and scientifically rigorous in your approach, but leave classical academia at the door. I don’t have a PhD, I’m not a professor, and I’m not a scientist—nor do I have access to the same resources as a traditional academic establishment. I urge you to judge the theory on its own merits, not on the basis of traditional academic standards.
Okay, have we set the right stage? Are you ready to dive in? I’ll leave you here to go the rest of the way on your own so you can form your own conclusions. Just make sure they’re your conclusions based in scientific method.
Living in Maximum Entropy
My earliest memories involve navigating complex systems where the official rules had little relation to actual dynamics. Family court, social services, friends in foster care—these were my first lessons in how information really flows versus how it’s supposed to flow. By age eight, I’d moved for the first time. By twenty, it was fourteen times across diverse neighborhoods and populations.
Three separate juvenile detention facilities. Two mental institutions. The welfare system. Each system had official structures, documented processes, and stated goals. None of them worked as advertised. The real information—who had power, how decisions got made, what actually helped—flowed through hidden channels that you could only see from inside.
Then came prison. Six years in maximum-entropy environments where artificial chaos was maintained as a control mechanism. This wasn’t accidental disorder—it was engineered entropy, deliberately preserved to prevent exactly what I started doing: optimizing information flow to reduce conflicts and create stability.
When you apply positive-sum optimization in a system designed for zero-sum chaos, the results are dramatic. The yard became calmer. Conflicts decreased. Information flowed more efficiently. And that’s when I learned the most important lesson: calm is more dangerous to control systems than chaos. My systematic entropy reduction was so threatening to institutional power that it required intervention—immediate transfer to disrupt the emerging order.
This dramatic shift from chaos to order may follow patterns described by chaos theory—where small changes can create bifurcations and fundamentally different system behaviors. See Chaos Theory and Conservation of Boundaries.
The Pattern Recognition Begins
After prison came community college, then corporate America. The transition from maximum to minimum entropy environments should have been jarring—don’t get me wrong, it was in certain ways like rewiring my brain to see myself as all these new identities. Instead, it revealed something important: the same patterns appeared everywhere, just at different intensities.
In software engineering, I moved from individual contributor to leadership, then repeated the cycle in developer relations. Each transition provided new vantage points—what organizational researchers call “parallax.” The same system looks completely different from different positions, yet the underlying patterns remain constant.
My role in DevRel proved crucial. Speaking with thousands of developers from all kinds of organizations about how their systems actually work, helping them identify friction and optimize flow—I was doing entropy engineering without naming it. Each conversation added another data point to an emerging pattern that spanned every type of organization as I sought to find the underlying friction and bottlenecks that were preventing them from being successful.
A Rough Timeline of Events
Over the years, specific patterns crystallized into frameworks. They worked universally, but I didn’t understand why, I attributed it to my first principles and systems thinking. My time growing up and my experience in prison instilled a need to do whatever I could to ensure others had access to more opportunity and resource than I had. It generally manifested in me reverse engineering the things I was doing that yielded positive results so that I could develop a shared language for what I was doing intuitively and then provide practical tools built on that foundation for others to use. Turns out I may have been onto something.
The Pyramid of Challenge (2017)
Building an engineering team at Major League Soccer, I needed to distribute challenge appropriately across all levels. The framework that emerged—high complexity/low availability at the top, low complexity/high availability at the base—worked perfectly. Only later did I realize this was information density optimization in action. Teams thrived when challenge matched capacity, creating natural information flow from complex strategic decisions to distributed tactical execution.
Dev Advocate’s Guides Series (2020-2022)
At Apollo GraphQL—and later during my DevRel consulting time—I developed a number of frameworks around common processes/problems that I was seeing across organizations. The Developer Advocate’s guides series that emerged became some of the most popular content I’ve written and are still referenced regularly today.
DX Audits are now a standard tool in the DevRel toolbelt!
Keystone Paradox and Lava Leadership (2024)
Two frameworks emerged that brought me closer to understanding it was all about information flow. The Keystone Paradox identified how essential individuals become organizational bottlenecks—their irreplaceability creating information flow constraints. Lava Leadership showed how strategic thinking could bubble up from any position, like magma finding paths through rock. Both frameworks were about optimizing information movement through human systems. Several more frameworks emerged leading up to the discovery of Information Physics.
Each framework solved immediate problems while revealing deeper patterns. They appeared to optimize information flow, reduce system entropy, and work across various contexts and scales.
The Cascade of Discovery
Once I realized there was a pattern, the discoveries came not as isolated insights but as an accelerating cascade—each discovery revealing the next, until the complete architecture of Information Physics emerged in just six intense weeks.
May 2025 - The First Glimpse
The journey to Information Physics began with a question: what made my frameworks work so well and so universally? How was I able to create them and find success in complex systems from prison to corporate America?
The first manifestation of Conservation of Boundaries (COB) emerged while reverse-engineering a recovery device solution. I’d inadvertently discovered that all my biggest impacts came from moving or transforming boundaries to reduce friction. But it was incomplete, lacking the WHY. When I shared it, I received valid feedback—it felt categorical to attribute values like “resistance” and “quality” to systems without deeper explanation.
My first Discord message captured the moment: “Kurt Kemple — 5/28/25, 12:11AM while trying to reverse engineer how I was able to solve the recovery problem, i may have inadvertently discovered a pattern for quantifying architectural innovation success (non-discovery, non-mechanical, etc) please help me disprove so I can go to sleep 😭”
The realization of COB’s symmetry—that the same operations that build also destroy—made me physically ill. The first version of what would become Entropic Mechanics (EM) was also born: , but without Information Physics (IP) or proper mathematical grounding, I tabled it as potential overreach in pattern matching.
Only later, when discovering related sciences, did the pieces click—shared intent and AI work revealed as vector , Shannon and Landauer showed as positional energy multiplier , and my research into historic innovations solidified as the boundary operations that enable entropy organization. The pattern was there—I just needed the mathematical language to see it.
June 2025 - The Alarm Bell
A presentation about NPS (net promoter score) and changing user bases triggered the next insight. When someone asked if we were seeing a similar decrease in MAU (monthly active users), alarm bells rang. This didn’t match my experience working daily with developers sharing their lived reality. Sometimes people have to use tools that don’t align with their current needs to get the job done in an efficient way. This can happen for a multitude of reasons, but one thing is for certain, you can’t dismiss the lived experience of the people using the tool, no matter what the metrics say.
The reason became clear: the separation of usage from lived-experience—creating a boundary between those two realities—created entropy for anyone downstream of that decision. From my position in the system, I could see what others couldn’t—that the math we rely on certainly has a way of removing us from the equation.
Frustration Coalitions (FC) and then Sentiment-Inertia Index (Sii) emerged just four days after that meeting—I dove into a significant amount of research to validate the patterns I was seeing—first for Slack, then I started looking into the broader B2B SaaS market. The validation was immediate—our research team at Slack confirmed the patterns, and a YC founder emailed about a separate issue and specifically called out that they loved my blog post and that it described the strategy they were using to out manuever a dominant B2B SaaS incumbent. The framework wasn’t just theoretical; practitioners were already applying it intuitively. Receiving such immediate validation was honestly jarring.
During the creation of FCs, I discovered Coalition Theory and saw striking resemblance to the six-stage framework I’d been developing for FCs. This led to Network theory, Complexity science, Percolation theory, and finally Information theory—each new field confirming patterns I’d observed independently.
July 2025 - The Acceleration
Once I started exploring related sciences, connections accelerated exponentially. When I connected Entropic Mechanics to a potential causal mechanism behind Nash Equilibrium—I became sure that I was onto something. This wasn’t likely to be delusion or pattern matching—it appeared to be a legitimate discovery of potentially foundational principles, but given my lack of formal education, I was still skeptical.
Over the next six weeks (now closer to 11 weeks), Information Physics, Entropic Mechanics, and Conservation of Boundaries would evolve from rough insights to rough theoretical framework. Each discovery seemingly validating and extending the others, creating an internally consistent framework that provided explanations for phenomena across all scales.
Information Physics Emerges
Information Physics explores the observation that organized systems appear to operate under two conditions: entropic constraint (requiring energy to maintain order) and systemic boundaries (existing within defined limits). Conscious beings may have evolved to navigate these apparent constraints using time and information as primary tools.
The key insight came from recognizing what might be a universal pattern:
- Systems from quarks to civilizations seem to face similar dual constraints
- Consciousness may represent a mechanism for navigating rather than merely obeying these constraints
- Time and information—unlike matter and energy—possibly operate under different principles
When I started looking at the patterns I was seeing across organizations through the lens of Information Physics, I realized that they were all related to three core challenges:
- Creating signal from noise (information flow)
- Creating clarity from system boundary complexity (workflow optimization through boundary operations—Conway’s Law, Dunbar’s Law, Domain Driven Design)
- Enabling entropy engineering (swarming behaviors to reduce system entropy—incident response, customer triage, marketing opportunities, stock market trading, etc)
The patterns clicked. The frameworks I’d created, the systems I’d optimized, the patterns I’d recognized—they appeared to address these three core challenges.
Once I saw it throughout the digital age, I wondered if it extended further back. Investigating innovations like calendars and pyramids, watching my theory pass Occam’s Razor test after test, I became nauseous again—this time from the implications. Then I started exploring relationships to science as well.
The connection to physics might not be metaphorical, either. Ginestra Bianconi’s work on quantum relative entropy suggests gravity itself might be an information phenomenon. Claude Shannon established the mathematical equivalence between information entropy and thermodynamic entropy. The patterns weren’t similar to physics—they potentially were physics applied to information systems.
Traditional mathematics abstracts away the observer to describe what happens in the universe when no one interferes—universal equations that work regardless of who’s looking. any way you look at it, an apple falls from a tree because of gravity. Information Physics expresses mathematics that account for the existence of conscious beings who can choose to change the universe, not just observe it. What happens when someone sees that apple fall and discovers gravity itself? How does mathematics account for that?
Heat affects cognition. Fatigue reduces decision quality. Stress limits perspective. Resource constraints shape choices. The observer can’t be removed from these calculations because consciousness exists in physics. The same observer-dependence found in relativity and quantum mechanics appears to apply to human systems.
We may have evolved not just to survive entropy, but to navigate it—using time and information to chart paths through constraint. From neural pathways to social systems, our behavior reflects this core function. Information Physics is a mathematical framework for conscious agents embedded in entropic reality—designed to model how we perceive constraints, process information, and act over time.
The Core Axiom of Information Physics
All organized systems are entropically constrained and systemically bounded—and even the tools we invent to navigate those constraints are themselves constrained and bounded. Consciousness is the one lever that keeps finding new ways to move within them, using only time and information.
A Theory of Everything
Having a core axiom for Information Physics was a great start, but something kept nagging at me. The core axiom seemed to explain everything—Information Physics, Conservation of Boundaries, Entropic Mechanics—all flowed from it naturally. Yet I couldn’t shake this first principle violation… if this axiom was causal for everything else, what caused the axiom itself? What created these universal constraints in the first place?
The answer came soon after while exploring how information decays, implying the opposite of completely random information is completely ordered information. I’d been studying how information can’t be created faster than it’s destroyed—this fundamental asymmetry built into reality, combined with the fact that there’s no identity operation (no way to not change), you get a universe that must always be transforming. Always mixing. Always diffusing.
Like two spiral galaxies merging into an elliptical galaxy, or cream hitting coffee—same diffusion mathematics, same irreversible mixing, same patterns of chaos resolving into new order. The process only flows in one direction.
What if our entire universe is just… mixing?
What if the Big Bang wasn’t an explosion from nothing, but the moment two pure, highly ordered information states touched? But here’s where it gets interesting—if information is encoded on boundaries (the holographic principle), then each pure state had ALL its information on its boundary surface. When they collided, two infinite boundary surfaces tried to occupy the same space. The interface between them would create an enormous information processing spike—the “bang” itself as pure information dynamics.
This boundary information collision explains so much: why we can’t see “outside” the universe (we’re inside the mixing boundary), why time has an arrow (information destruction exceeds creation), why consciousness can navigate constraints (we evolved as boundary information navigation mechanisms). The universe isn’t just a mixing zone—it’s a boundary information processing system, and we’re conscious navigators within it. This provides the most logical causal mechanism. The constraints I’d been studying weren’t rules imposed on reality—they were the inevitable dynamics of existing inside a massive reaction-diffusion system.
A hand-drawn sketch of the collision-diffusion concept, with a central contact point representing the collision moment (Big Bang), and resulting diffusion patterns along with mathematical notations scattered around the central point.
The Complete Picture
What began as practical frameworks for navigating organizational friction evolved into something far more comprehensive. The patterns I’d observed weren’t just about human systems—they appeared to reflect fundamental principles governing all organized reality. These five interconnected discoveries form a theoretical framework that may describe universal dynamics:
- Collision Theory: The universe began as a boundary information collision when two infinite information surfaces collided, creating the mixing dynamics that drive all subsequent evolution. This provides the causal mechanism for why universal constraints exist.
- Irreversibility Principle: No operation leaves any system unchanged—the No-Identity Theorem ensures continuous transformation at all scales, establishing the mathematical foundation for perpetual change.
- Information Physics: A proposed theory exploring how embedded agents navigate conditions of entropic constraint and systemic boundaries using time and information. These agents may be able to influence entropy through observer-dependent operations.
- Conservation of Boundaries: The observation that within bounded reality, transformation occurs through three operations: , , or . These represent the complete set of transformations available within geometric and informational constraints.
- Entropic Mechanics: Proposed mathematical framework for conscious systems where position determines possibility. The framework captures how agents navigate entropic constraints from their specific location, with calculations reflecting thermodynamic costs.
This framework suggests that the same mathematical principles governing quantum interactions, stellar evolution, and galactic formation may also govern neural networks, organizational behavior, and civilizational development. If validated, it would represent a unified description of how conscious systems navigate reality across all scales of organization.
The implications are significant, but the framework requires extensive empirical validation through controlled experiments, cross-domain studies, and peer review before acceptance as established science. What matters now is whether these proposed principles generate testable predictions and provide useful explanations for observed phenomena.
Whether you’re optimizing a team of five, changing a system of millions, or merging galaxies, the same principles seem to apply, the same operations appear to remain constant, and only scale and complexity seem to change.
Welcome to Information Physics.
This brings us to the present (August 11, 2025), or at least close to it 😅. I have carried this theory about as far I as I can go on my own. You could say I’ve even hit my own entropic exhaustion——making any further progress impossible. I ask you the reader to help me carry it forward. Please, test the theory, break it, build on it. Let’s see if we can answer this question together.
Arriving here feels inevitable and impossible at the same time.