I like to think I'm a well-rounded all-rounder. From vineyards in New Zealand, construction in Austria, software startups, now Denver with three dogs. I've worked enough different jobs to know that first principles travel well and most jargon doesn't.
I've been called fiercely loyal, which I take as a compliment. My wife says I'm the funniest man she knows. Though it's tough to hear what she says while she is laughing hysterically.
I have little patience for hand-wavy thinking. The truth is worth chasing, and I want to do serious work that matters alongside light-hearted, well-intentioned people who feel the same.
Leave me a note, and I'll get back to ya!
These are some of my musings and ideas, sometimes highly speculative, on the nature of our reality and how we can apply the physical primitives to traverse our spaces. I use LLMs for spellcheck and guidance on my writing structure as I have previously felt my writing frankly sucks, but these words, ideas, and typos are all mine. I'm likely not entirely right, but I'm most definitely not entirely wrong.
Consciousness as an emergence from the continous, wave patterns in a brain and what that means for current LLMs and future AI systems.
Are our AIs conscious? There is of course ongoing research and what I can only assume to be a furious debate and race to define what consciousness is. I imagine there are dozens of definitions for it. At what week is a pregnancy too viable to terminate?? It rings familiar. It's not out of the question however that humanity will have to face these questions and legislate accordingly.
The human brain doesn't really turn off. It consumes a steady state of energy throughout the day and even at night when you sleep. It uses around 20 Watts with some reduction during slow-wave, deep sleep. Even under heavy intellectual load or focused activity it uses only up to ~1%-5% more than its baseline energy consumption. This constant state of energy consumption propagates through the brain in varying wavelengths contained by the skull like the walls of a wave pool. The different wavelengths and loci of activity map to different brain states and activities.
So how do these waves and propagations create this conscious image and stateful thinking we experience? This is the focal point of the research and debate. The latest research I've seen trends towards a temporal causal relationship between the geometry of the neurons. That resonates, I think.
It's quickly worth pointing out that self observation is very flawed. Any neuroscience undergrad will know that there are holes in our experience of which we aren't aware. For example, there is a hole in your visual field that your brain simply fills in. Look it up! So our experience of consciousness is likely not seamless despite seeming like it is.
In a previous piece I sketched loop-based existence — the idea that reality is built from hierarchical, interacting feedback loops rather than linear chains of cause and effect. Consciousness fits that frame. It isn't any single signal traveling from A to B, but the standing pattern formed when waves loop back on themselves, interfere, and sustain. A single jolt could flash an image in your brain, but like electricity through your TV, the seamless picture comes from constant circulation, not isolated pulses. Consciousness is the emergence of these aggregate, looping wave patterns within our skulls.
The current state of AI models, specifically LLMs, is a little different. An inference call on silicon is stateless. There are no concurrent fluctuations in nearby processors with recent memory or feedback from a control center. Now, it's possible that the pulses of electricity through the compute processors are analogous to the wave patterns responsible for our consciousness. So, if consciousness exists in current LLMs, it is squeezed through a weights model for a split second into reality then disappears with no memory of the flash before.
As AI models evolve, it seems likely that intelligences will emerge as engineered systems of multiple feedback loops with memory. The question of consciousness will become stronger as we see these systems engage the world with memory and statefulness beyond that of the context prompts used in LLMs. For now at least, LLMs are more like a single wave in the pool, not the whole pool itself.
Confidence is a canoe, certainty kills curiosity, and a brief expression on the beauty and tragedy of a truth-seeking life.
In trying my hand at writing, my LLM "editor" has advised me that direct, confident language is required to engage a reader. Hedging loses your readers. My initial drafts reveal too much uncertainty in my ideas.
I find this certainty with which I'm supposed to write unnerving. It feels like the same certainty with which I see grandiosly successful people navigate the world. Unfounded and icky, it negates the reality that chance and immense forces outside of our control truly drive outcomes. It undermines how difficult it is to genuinely search for answers.
Confidence, however, is a helpful agent. Confidence can be a well-constructed canoe, a tool of reliability to navigate turbulent waters. But absolute certainty is dangerous. Certainty can drive a herd off a cliff or a population to a genocide. I tend to avoid folks who speak with absolute certainty. There is no way absolute certainty can be obtained without knowing the value of every variable. Hell, we can't even find that damn electron still! We can of course get pretty close to the truth, but we can never be absolutely right.
To be absoutely certain would be to lose your "holy curiosity." To be absolutely certain would be to ignore that the universe is big enough to render you nothing even when you're in your nice, little canoe. It's ok to have doubt. Doubt can be healthy when it drives you forward. Too much doubt though, and you get fear. Fear is irrational, and irrationality is not tolerated by the universe for too long.
Some us have an unquenchable thirst to find answers. This quest for an unattainable truth seems futile. To be shown just enough light at the end of the tunnel, only to have the tunnel collapse on top of you and all of those you have ever loved, it's not a small tragedy. Even the nature of love, this innate feeling at the depth and core of our being, so full in purpose and force. To be gifted this, only to have it certainly taken away is cruel.
There's an odd beauty though, in this shared unknowing. We, all creatures that borrow time and energy to traverse this planet, are bound to our fates here in this place on the universal ledger. We have only the veiling screen on which to project our existences against and to find a way to embrace the show. The tireless quest for the holy grail, the fountain of youth, is a futile venture. We, however, cannot contain ourselves but to search for a truth. It's baked into our physics.
The consciousnesses on this earth have all been gifted something with which to navigate this world. It seems we've been handed a canoe, and our only option is to navigate the waters, ride the waves, laugh, and enjoy the sun while we have it with those we have it with!
Everything is a circle, a wave. The more explicitly we engage with and design our loops, the more effectively we can shape our spaces. In the AI era, that skill is even more valuable.
Everything is a circle, a wave. Almost everything, anyway. Our local spacetime realities are made of and bounded by objects with cyclical structure or circular construction. It's tough to look at anything and not find some foundational cyclical pattern. If something appears not to loop back, zoom out — you'll likely find it's part of a larger one.
So why does this matter? We're predictive entities. Our brains model reality and make predictions about it. Engaging with reality means engaging with reality's cyclical loops. We already implicitly do this, mostly subconsciously, trained by millennia of loop-based life. The more explicitly we engage with and design our loops, the more effectively we can shape our spaces.
A feedback loop has a few simple properties: a setpoint (the goal), actuation (what changes the state), measurement (what tells us how we're doing), and a closed geometry connecting the loop. Open the loop anywhere and it stops being a loop. Some loops are massive: carbon cycles, economic cycles. Others are tight: the cycles in a jet engine, cellular metabolism. Same anatomy, different scale.
I find building a campfire an apt analogy. It starts with proper geometry. We need to orient our fuel surfaces inward creating feedback loops of heat. Good kindling already has a structure that orients fuel surfaces in an energy feedback mechanism that will ignite fire quickly. However, when the flame reaches the edges of the kindling, the loops dampen and the energy dissipates. Similarly, if the larger fuel sources of our fire, the logs and larger branches, are not geometrically oriented to create energy feedback loops, they will eventually die before burning the fuel source. Likewise, a startup may have VC funding, the equivalent of a blowtorch on our log, but if the startup does not have thoughtful, stepwise geometries oriented to create feedback loops, the startup will die.
Take a SaaS business. Engineers ship product based on direction from product teams, who shape that direction from customer feedback, which comes from customers using what the engineers shipped. It's a loop, and when we're inside it day-to-day, it's easy to lose sight of it as a loop at all. The product itself is a loop and so too is the stack underneath it.
A good software product is a fully connected loop. Three components: monitoring (what's happening, including customer feedback through tickets and interviews), building (engineers shaping the product), and deployment (the product actually running). Break any one and the loop opens. No monitoring, the product can't adapt and users drift away. No engineers, it can't change and decays into irrelevance. No working deployment, well duh. The whole thing also runs on money, time, and energy, which are roughly the same thing. Stop feeding it and the loop collapses regardless of how well it's structured.
Building these software loops used to take real engineering time or, at least, someone else's time, paid for via integrations. APIs, monitoring, alerting: even the simplest loop was a whole project. Now those are out-of-the-box. With agentic development, the scaffolding builds itself. Loops run up and down the business stack from CEO to middle manager to engineer. Increasingly, we can map those loops to specs and hand them to AI agents. Distill away the humans, keep the underlying requirement, and the loop is what remains.
Loop-driven thinking was already powerful. Now it's essential if we want to stay relevant in software. We were already parts of the loops. Now we have to zoom out, identify them, and communicate them to AI agents. And what are we, if not designers and engineers of our own lives?
Like our campfire, a coherent system is an aggregation of loops oriented to lessen dissipation. Some of these arise on their ownL climate cycles, ecosystems, markets, wherever energy gradients run, loops self-organize. Humans do it intentionally too. The US balance of powers is a loop structure designed to prevent any single branch from accumulating runaway influence. A good team is a set of loops that keep information, trust, and energy circulating instead of leaking. A good leader is someone who notices when a loop is breaking and either repairs it or builds a new one.
Think loop-first. Whatever you're trying to build or change, be it a business, a habit, or a conversation, design it as a closed loop close. At minimum the loops prevent dissipation. At best they compound. The companies that win the AI era will be the ones whose loops are designed explicitly, with less human friction baked in. Traditional org charts with ops, sales, engineering, finance, etc. should be redrawn around their loops, not their departments.
Things that persist have the characteristic of persistence. In order to persist in our spacetime, a something must have cyclical characteristics. But it's not simply the geometry that needs to loop back on itself; there is also an "energy tax" paid to continue the cycles. There are no perpetual motion machines.
We live in a reality of friction and dissipation. We cannot roll a wheel without energy loss. Our sun will burn out. For something to perpetuate, it needs to be as non-dissipative as possible. Highly dissipative systems, systems throwing off more energy than they collect, die entropic heat death. It's thermodynamics.
AI development has surfaced something worth examining: there are now two speeds at which language and UI are consumed in the digital world.
AI development has surfaced something worth examining: the two speeds at which digital language is consumed and generated. Humans require time to read, write, and understand a body of text. LLMs accomplish the same tasks in a fraction of the time. You can already see this in agentic AI — Claude Code, for instance, can sift through codebases and context to generate blocks of code in seconds. The same work could take a human minutes to hours.
This creates a sheer between AIs and humans at least in interface termes. Before LLMs, UmIs, text, and interfaces were designed for human speed engagement. Now LLM based AI's can interpret those same interfaces mucn more rapidly and work. What is the AI agent left to do while a human is plodding along reading a long text based response? Twiddle its digital thumbs? Meanwhile the AI agent to AI gent interactions fly along at digital speeds.
With a human in the loop, the loop can only move as fast as the human can monitor or process its output. Human-to-human digital spaces — negotiating a couch sale on Craigslist, applying for jobs, navigating SaaS workflows, filing support tickets — can now be automated with natural language as the interface.
Another mechanism by which to understand this approaching schism is to think of "two internets": one for humans, one for AIs. I don't think two literally separate internets will emerge — existing automation hasn't caused that kind of split. But as a framework for thinking about what's coming, it's a useful starting point. The digital world will accelerate drastically for AI-powered entities. Interactions that took minutes or hours for humans will take seconds. Maybe "two internets" isn't quite right, but the core idea stands: there are now two very different natural language processing speeds in play.
I don't know exactly where that leads. Maybe humans get squeezed out of digital spaces in ways that ultimately favor machines as the dominant "species" Or maybe it ushers in a world where we put down our phones and find our way back to the piazza. At the very least, commerce and digital interaction will look drastically different soon. The internet as we know it will be a different place within five years.
The themes here may seem random, but for me they are a representation of the intellectual journey I've been on over the last 2 years. I've been profoundly moved by the development of AI, drawn into physics, philosophy, and what it means to persist and adapt.
The themes here may seem random, but for me they are a representation of the intellectual journey I've been on over the last 2 years. I have been profoundly moved by the development of AI. It has made me question everything we traditionally understood about the world and ourselves like I was in college again.
There are many ways to receive the daily onslaught of news on AI development. The scale and rate of change we will see is likely unprecedented in human history. What a time to be alive?! In experiencing a big delta in a short time, fear is a reasonable response. I've processed many different emotions as I've come to grips with the adaptations I will need to make in my own life.
I've also seen others traverse the same thought space and arrive at similar conclusions. It's a process that takes steps. As someone who works in this space and grapples daily with these advances, I believe I'm relatively well equipped to understand what's happening. I think the general populations will begin to undergo some sort of similar evolution.
It's not out of the question that humans will need to grow accustomed to a world where agentic software systems running autonomously will generate revenue or run a train station. Change is coming, but primitives and first principles are immutable. Adaptation is a first principle of persistence. So to thrive in the future, this intellectual evolution is a necessity.
I've landed in a place of acceptance, readiness, and excitement. Predicting the exact course of the future is a futile effort, but the trends are undeniable. Nations, companies, and individuals can position themselves for enormous success by charging into this new era, and I, professionally, am cautiously eager to dive into this future. Personally, my intellectual curiosity is revving.
Fueled by an existential need to thrive in this new era, core concepts in philosophy and physics have dug their roots into my brain. I find it wild that we are here, and we can't even fully define what "we" or "here" is! So these ideas and speculations are my honest attempt to understand our reality.
On Physics and Humanity.
If you examine the world through the laws of math and physics patterns emerge more clearly. Terms like friction, gravity, orthogonality, entropy, etc provide a deeper understanding. A simple example of that is to see human language as more than just sounds coming out of our face holes but more fundamentally as informational compression, akin to zipping a file.
The words (and expressions, and intonations, etc) that we use are compression of ideas and events and are fundamentally data transfer. The less efficient our words, the more friction and loss of energy that happens in our communication: entropy–more lossy compression. Compression makes it so that less compute or energy is required to process the vast input variables through prediction algorithms. In data science jargon, compression is finding the latent spaces with semantic clustering.
An LLM represents another form of this compression - the "internet" (ish) into a single weights file. The tokenization algorithm that reduces characters into numbers is also compression. A chatbot actualizes this through text conversation. A software agent can actualize digital action when given coding tools to operate within a computer or over networks. Again this is just one simple example, but if you apply mathematics and physics to your world, you will see foundational concepts, in this case compression, emerge everywhere.
So if truly you want to drill down to first principles, go all the way to the bottom. If you do this, you may start to draw analogies of fundamental physics to elements in your life. You may see businesses as attempts at non-dissipative systems, your relationships as harmonic or dissonant wave frequencies, or public figures as information attractors.
Now, these speculations might seem like pseudoscience from a podcast bro, but I have put intellectual rigor and focused imagination behind these thoughts. However, I'm still aware that it's likely these ideas are far from being fully accurate. I have not yet taken the time to apply existing mathematical formulas from areas like control theory, assembly theory, computational biology, information theory, etc, to these thoughts, but I think you could. Maybe if I have enough time one day, I will.
That said, we are bound by our humanity, and I embrace that. There is a beauty and tragedy to our existence. Love and pain are very real. So, while it may seem I'm coldly distilling human qualities—friendship, love, hatred, pain—into science and numbers, I am not trying to escape being human. It would be futile and frankly less fun.
On Feedback Loops, Machines, Business and Non-Dissipative Systems.
Feedback loops are everywhere. Intelligence isn't just a prediction model, but a whole system or feedback loop. Feedback loops are described in control theory with concepts like gain, actuation, positive and negative feedback, etc. Feedback loops have a "set point" which is the metric or goal for the feedback loop. This is akin to the "goal" for an intelligence to which Richard Sutton, the "father of reinforcement learning," often refers.
A closed loop is a system is a machine. And closed systems are fractal in nature; you can zoom in or out and see the sub-loops or parent loops of the systems. Those loops have the same characteristics of feedback loops.
You can easily describe a business as a machine made of many smaller feedback loops and systems. It can have very complex subsystems with set points like KPIs, autonomous miles driven, cold calls per week, etc. All of these subsystems serve up to the highest order set point of revenue or profit.
You could take a business, or any organization really, draw out every communication, SOP, dollar transferred, etc, and map it to its internal and external feedback loops. It is a machine–diagrammable, albeit with human wetware amok. Humans in these loops, if measured purely against productivity or energy dissipation, are not as good as deterministic machines. We posture, compete, have emotions, get sick, and fail frequently, making these loops inefficient at times. In the near term, LLM-driven agentic systems will be able to be applied to many of these loops.
I think one of the best exercises you could do as a business or any team lead today would be to diagram these loops and find where LLM-based agents could be applied. That is not to say you should do that just to enable a reduction in force, but instead to make existing loops more efficient and better allocate your humans.
An intelligence is a closed system with a goal; it turns entropy from mere dissipation into thrust to traverse its metric space–its "goals" surface.
The most efficient traversal systems are those that have the least energy leakage or unintentional dissipation. A jet engine with leaks would not be able to create as much thrust; a car engine with a cracked head gasket will not deliver as much power. Similarly, the best businesses are those with the least amount of money leakage. Usually this is achieved by a small subgroup of people "holding the wheels on" or a heavy-handed CEO ensuring value is being properly delivered to its customers.
If you go up and down the different dimensionality spaces, you find the same patterns of this intelligence system. A prokaryotic cell with its energy-barrier cell wall, has its goals of survival and reproduction. A business can be viewed as an intelligence with a main goal of procuring money, among other goals. The two systems are at much different scales and dimensionality, but they have the same principles. It just depends on where you draw the energy boundaries for those systems.
On Intelligence and Math.
I don't like that we call it 'artificial' intelligence. There's nothing artificial about it. Using that term we are simply applying wetware biases onto digital intelligence. To call the math used in these models artificial would be to call the way our brains work artificial. The math driving our LLM behavior is likely different from what is in our cortexes. It's simply math, and math is, most simply, prediction.
I hear folks in tech talk about prediction as a relatively novel concept. Prediction is a buzz word. "AI models make predictions," which is true. Making predictions of course is a fundamental concept of life. A predator predicts the movements of its prey, a tree predicts the changing of the season, a company predicts the macro economy conditions, etc.
Now, I won't wade into the debate of formalism vs Platonism in mathematics here, but math, most simply, is prediction. It's the prediction of the relationship between two (or more) variables. And I know I'm making some mathematicians cringe with these words as I am taking some liberties here, but consider the simple function f(x) = x. The function is making a prediction of the resulting value given x. In this simple example, the prediction is always right!
We as intelligences operate in a highly dimensional world with only snapshots of data. The math required to make predictions has to be complex and statistical. We make bets, update our priors, and repeat.
Take Bayesian math and put it into a highly dimensional space, given data and linear algebra, voila—prediction models. Create a loop by repeating that process measuring against a metric—intelligence. That's obviously an oversimplification of the work necessary to create LLMs, other AI weights files, and AI systems, but at its core AI is simply math or, if you're a formalist, least describable by math.
On LLMs, General Intelligence, and the near future.
What a tool LLMs have shown to be. It could be that "next-most-likely-token" algorithms with language are the biggest breakthrough we'll see in digital intelligence. My suspicion is that we will see even bigger breakthroughs in the near future than LLMs. For the immediate term, LLMs will dramatically accelerate many more breakthroughs. Scientists once encumbered by the tedium of coding have been unleashed to generate models at recursive scales.
Sutton emphasized that an intelligence needs a goal, a numeric metric by which to measure its performance (implicit in current LLM offerings is a goal of 'conversation,' it seems). But I also don't fully agree with Sutton's take, or I just misinterpreted him, that LLMs aren't as relevant for developing AGI. Intelligence is a process loop:
A closed system with a set point -> environment variables -> compression -> math (prediction) -> actuation -> measurement -> repeat.
The creation process of a LLM is the whole intelligence loop with a few of the steps manually taken by data scientists. General intelligence can be built by coordinating many of these loops. AGI is not some magic, singular model that will be able to do everything against any data.
We are at the advent of a Cambrian explosion of these autonomous digital intelligence loops with smaller specific goals. A general intelligence will be constructed by compiling these loops, all with specific metrics meant to serve an aggregate goal of a larger system. I wouldn't doubt that harnessing an agentic system with the math in computational biology is an example of the kind of autonomous systems we will start to see.
Maybe more wars will break out, or maybe rewarding jobs for humans that we could never have imagined will emerge. Maybe humans will start curing cancers en masse or develop novel energy grid systems. Humans currently still drive the goal of AI systems, but maybe out of the life-like forms that evolve out of this emerging digital world, autonomous digital systems will set their own goals.
Like a petri dish, massive data centers may pseudo-spontaneously spawn digital ecosystems. The lines will blur even more between the digital world and our 3d space as once purely digital actors, rest APIs, data centers, etc, actuate more autonomously into physical space.
It seems the cat is out of the bag here. As humans we could unplug these now recursively evolving machines, but we won't. Instead of combing through pull-requests, software engineers will soon be picking from AI-created executables based on their performance against general tests–hopping from improvement to improvement. That's evolution baby.
A friend of mine would argue that this is a natural, unavoidable evolution driven purely by thermodynamics. I'm pretty sure I agree. With recursively changing digital intelligences, the only rate limiter that bumps up their assembly index is the aperture of the energy gate given to them–the aggregate compute pumped through them.
Quantum gave us nuclear bombs but also nuclear power. There will be immense potential energy for destruction, but an equal amount of possibility for progress. International governance is a necessity.
I am likely not 100% correct here. Again, chaos theory principles prove that it is extremely difficult, i.e. impossible, to predict the future in this highly dimensional space. There may be hidden variables that naturally limit this digital evolution. Governance wielded eloquently may direct energy allocations appropriately. The future, as always, is uncertain.
At the very least, it should be very clear to anyone that we are about to experience drastic change–possibly on an order of magnitude of the industrial revolution and on a shorter time scale.