Theodore C. Tanner Jr.

The Denominator (aka The Meter Was Always Running In Joules)

“Information is not a disembodied abstract entity; it is always tied to a physical representation.”
Rolf Landauer, The Physical Nature of Information, Physics Letters A 217 (1996), 188–193

First, i trust everyone is safe.

Second, this one starts with a pricing page, which is an unpromising place to begin an essay about physics. Stay with me. What looks like a billing decision turns out to be a physics argument wearing a costume.

The company is Flexcompute³. They sell simulation computational fluid dynamics, and finite-difference time-domain electromagnetics, two workloads that have been eating supercomputers alive for as long as there have been supercomputers to eat. And they do not charge you for the computer.

They charge you for the problem.

The Word Itself

i always start with the word, Dear Reader, because the word usually knows more than the pitch deck.

Credit, from the Latin credere. To believe. To entrust. A credit is a belief about value held between two parties, and it is worth exactly what the issuing party says it is worth, right up until the moment they say otherwise. Every loyalty point, every arcade token, every airline mile, every cloud dollar. A belief.

Joule, from James Prescott Joule, who spent years measuring the mechanical equivalent of heat by churning water with a paddle wheel and watching the temperature rise. A joule is not a belief. Nobody can reprice it. It is a kilogram-meter-squared per second-squared and it will be that in Charleston, in Reston, on the moon, and after every company in this essay is a footnote.

Denominator, from denominare. To name. The thing that does the naming.

Hold those three. The whole essay is in there.

What They Actually Did

Here is the legacy model, which is the model essentially every cloud has used since the day cloud became a word:

$\text{Cost} \;=\; \text{Hardware Rate} \times \text{Wall-Clock Time}$

Innocent-looking. It is not innocent. Sit with it for a second and you will see the problem, and once you see it you cannot unsee it anywhere in this industry.

If i bill you by the hour, i get paid more when my software is worse.

That is not a cynical reading. That is the arithmetic. A badly optimized solver running on a stalled node behind a queue is, to the provider, indistinguishable from a good quarter. The incentive gradient points directly away from the thing the customer wants. Everyone in the chain knows this and everyone has agreed not to mention it, the way you do not mention that the bar is pouring doubles.

Flexcompute changed the denominator. Their currency, the FlexCredit, is computed like this:

$\text{FlexCredits} \;\propto\; N_{\text{grid}} \times N_{\text{steps}}$

Spatial resolution times temporal resolution. The Tidy3D documentation says it plainly: a FlexCredit reflects the size of a simulation, in both grid points and timesteps, and it explicitly does not reflect the compute time required to run it⁴. (i am scoping that to Tidy3D on purpose, because that is the FAQ i read. Confirm the Flow360 terms separately before quoting them⁵ a company can run different billing models on different solvers, and i am not going to assume one from the other.) When you upload a job the system prints the maximum cost before a single cycle burns. If the physics converge early and the solver shuts off, the bill is prorated down⁶.

Read what that does to the incentive.

Now if their solver is slow, they tie up their own silicon longer and earn exactly the same number of credits. The risk moved across the table. They are now structurally obligated to write the fastest code on earth, because every hour they shave is an hour of their own hardware they get back. Their own documentation offers a rough benchmark: one FlexCredit is somewhere around fifty hours of traditional CPU core time for FDTD⁷. Their job is to make that number keep falling, and the pricing model is what makes it their job.

Plenty of companies write mission statements about customer alignment. This is one of the few that put it in the arithmetic instead, where it cannot be walked back by a new VP.

#TCTRules: The denominator is the strategy. Everything above it is marketing.

And Now On With The Show

Because grid points times time steps is a good denominator. It is not the last one.

It is a model of the work. A very good model – it is the literal mathematical definition of the problem, which is why it feels so clean. But it is still a human-chosen proxy that a company issues and a company could, in principle, reprice. It is a credit. It is a belief.

Go one level down and you hit the thing nobody issues.

$\eta \;=\; \frac{\text{useful work}}{\text{joule}}$

Normalized compute per unit of power. FLOPS per watt, tokens per joule, useful answers per kilowatt-hour. The Green500 has ranked supercomputers this way for years⁸, which tells you the people closest to the metal worked this out a long time ago while the people writing the invoices did not.

And here is why it is a different kind of number than everything above it.

Hours drift. A 2015 hour and a 2026 hour buy wildly different amounts of computation, which means every contract denominated in hours is silently repriced by Moore’s law and nobody renegotiates. Instance types drift the same label buys different silicon in different regions in different years. Seats drift. Credits drift, because the issuer can change the conversion.

A joule is defined by physics, not by a vendor. It is the one quantity in the entire stack anchored to the universe instead of to a price list.

Be careful with that sentence, though, because a joule drifts too just not where you expect. It drifts in two places, and neither of them is the definition.

It drifts in price. Electricity costs wildly different amounts in different places and at different hours several fold, routinely, between a Virginia afternoon and an Icelandic midnight.

It drifts in what it buys. The number of computations a joule pays for has roughly doubled every year and a half for most of the field’s history, slowing to about every two and a half years since 2000⁸. A 2015 joule and a 2026 joule buy wildly different amounts of computation, exactly as the hours do. That is the eight or nine orders of headroom from the next section, being spent in real time.

So the joule is not immune to drift. What it does is put the drift where you can see it. An hour folds efficiency, silicon, and margin into one opaque rate, and the vendor’s internal business stays internal. A joule splits the same drift into two public numbers: a price you can index and hedge, and an efficiency – useful work per joule – you can measure. The thing that does not drift is the problem itself: a converged simulation, a retained token, a validated answer. Price that against a reference joule count that is revised on a published schedule, and the drift stops being hidden and starts being shared. That is the whole proposal, and i will get to it.

The Floor

Now go all the way down, because this is where it stops being an economics essay and becomes a physics one.

There is a hard thermodynamic bottom to computation and we have known about it since 1961⁹. Landauer’s principle: erasing one bit of information dissipates at least $k_{B}T\ln 2$ of energy as heat. Run the numbers at room temperature, 300 kelvin:

$E_{\min} \;=\; k_{B}T\ln 2 \;\approx\; 2.87 \times 10^{-21}\ \text{J}\quad (T = 300\ \text{K})$

Two point eight seven zeptojoules¹⁰. Which means one joule, spent perfectly, with no waste whatsoever, buys you about $3.5 \times 10^{20}$ bit erasures. That is the floor. Not an engineering target – a floor, the way the speed of light is a floor.

The honest footnote, because the physics in this blog is not allowed to be knowingly false: the bound is on erasure, not on computation as such. Reversible computation can in principle dodge it¹¹, which is a genuinely open and genuinely interesting corner of the field. But every machine any of us has ever shipped erases constantly, and every one of them is running something like eight or nine orders of magnitude above that floor.

Eight or nine orders of magnitude. Sit with that. We are not near the wall. We are not even in the same county as the wall. Everything between here and there is headroom, and headroom is where value lives.

And Shannon got there before Landauer did, from the other direction. The Shannon limit says there is a minimum energy per bit for reliable communication: $E_b/N_0$ cannot go below $\ln 2$ , which is -1.59 dB¹². Information theory and thermodynamics, walking into the same bar from opposite doors, and both of them ordering in joules.

The price was always in energy. We have simply been billing in a proxy currency and calling the proxy the thing.

The Token Is Not A Layer

“The choice of a logarithmic base corresponds to the choice of a unit for measuring information.”
Claude E. Shannon, A Mathematical Theory of Communication, Bell System Technical Journal 27 (1948), 379–423

Dirt To First Token through the energy layer.

Owning a layer and choosing a denominator are two different moves. The layer is the thing you own, and other people stand on. The denominator is the unit you meter it in. You can own a layer and meter it stupidly. You can pick a brilliant denominator for a layer you don’t own; that is called being a reseller.

Which brings me to the token, and i want to say this plainly because a lot of money is currently confused about it:

A token is not a layer. A token is a unit of account.¹³

Nobody owns tokens. They are the metering unit for a layer that somebody else owns: silicon, memory bandwidth, and a power purchase agreement. Confusing the two is how a company ends up believing its moat is a billing unit.

And here is the part that should make every person reading this sit up, because the token denominator has the same bug the hour has, just rotated.

Per-token pricing is linear. The physics is not.

i wrote about this in SnakeByte[21]¹⁴, about the long-context arms race: standard self-attention has computational complexity that scales quadratically with the sequence length $\bigl(\mathcal{O}(n^{2})\bigr)$ ¹⁵. So a hundred thousand token prompt is not a hundred times a one thousand token prompt at the attention step. It is far worse than that. The invoice says linear. The silicon says quadratic.

Any time the price curve and the cost curve have different exponents, somebody is eating the difference, and the pressure that creates does not point at making long context genuinely cheap. It points at quietly capping the window, degrading retrieval, truncating the middle, and hoping nobody runs the experiment. Same adversarial gradient as the hour. Different rotation.

It gets worse, and better, when you look at what is physically happening. Prefill processing the prompt is compute-bound. Decoding, generating the next token, is memory-bound. i surveyed this at length in the disaggregation piece¹⁶ and the whole reason the field is splitting those phases onto separate clusters is that they are two different machines pretending to be one.

Two different machines means two different energy profiles. Moving a byte out of high-bandwidth memory costs substantially more energy than doing arithmetic on it once it has arrived¹⁷.

Providers do not charge one flat rate per token. They already tier. Output tokens run roughly three to six times the price of input tokens the median across the market sits near four and cached input is routinely discounted to something like a tenth of a fresh input token. Some models have started charging a long-context surcharge above a couple of hundred thousand tokens¹⁸.

Look at what that tiering actually is. Input is prefill, which is compute-bound. Output is decode, which is memory-bound. Cached input is prefill you already paid for and do not have to redo. The pricing sheet has been quietly converging on the physics for two years and nobody has said so out loud. It is the same move Flexcompute made, arrived at by trial and error instead of by design.

Which makes the remaining gap the interesting part, and it is still a gap. Three buckets is not a measurement. Input, output and cached is a coarse quantization of a continuous cost surface, set by what the market will bear and revised when margins hurt – and inside every one of those buckets the exponent mismatch is still running, because attention is quadratic and the bucket is priced per token. The surcharge above two hundred thousand tokens is the tell. That is not a pricing strategy. That is a quadratic poking through a linear schedule hard enough that somebody had to patch it.

i already proposed a better denominator without quite noticing i had. In that same piece i suggested metered billing based on tokens stored and tokens retrieved – which separates the memory term from the compute term, and is therefore closer to the physics than a flat per-token rate. That is exactly the move Flexcompute made. Split the proxy into the parts that actually cost different amounts.

And one level below mine is the one i did not write, which is this whole essay:

$\frac{\text{joules}}{\text{retained token}}$

i ended that post with the architectural arms race is becoming a memory computation problem, and then i signed off with Got More Ram? i meant it as a joke about capacity.

It is not a capacity question. Memory movement is where the energy goes. Got more ram is an energy question wearing a capacity costume, and i did not see it until i went looking for the denominator underneath my own denominator.

Atoms, Bits, Atoms

Here is why physics-based simulation is the right place to have found this argument, and not an accident that it showed up there first.

Simulation is a round trip. It starts in atoms and it ends in atoms, and everything in between is a detour through representation.

$\text{atoms} \;\rightarrow\; \text{representation} \;\rightarrow\; \text{arithmetic} \;\rightarrow\; \text{representation} \;\rightarrow\; \text{atoms}$

A wing exists. Air exists. Neither of them is doing math. The air is simply obeying Navier-Stokes because it has no choice, in continuous time, at no computational cost to itself, everywhere at once. We cannot watch that directly at the resolution we need, so we chop the wing into a mesh, chop time into steps, quantize every continuous quantity into a finite number of bits, and then ask a machine to imitate what the air was already doing for free.

Then we convert back. We cut metal. We etch a photonic circuit. Atoms again.

Every one of those arrows is a conversion, and every conversion is a tax paid in energy and fidelity. This is the same border i wrote about in the analog essay¹⁹, with the stakes raised: life is not band-limited, and neither is a wing in turbulent flow. We sample it anyway because countability is a magnificent trade. But we should at least know what we are being charged.

And look at what the arithmetic in the middle actually is. Strip away the frameworks and the schedulers and the driver stack and you reach the same irreducible operation in almost every workload that matters right now:

The Almighty Dot Product.

Multiply a row by a column and accumulate. That is a CFD (computational fluid dynamics) solver doing a sparse matrix-vector product inside a Krylov iteration. That is a finite element code applying a stiffness matrix. That is attention computing how much one token cares about another. That is a convolution. The GPU won the last decade not because it is a graphics device but because it is a dot product device, and the whole industry quietly reorganized itself around the one primitive the hardware does well.

For anybody who has not spent a career inside a solver: CFD is simulating how a fluid moves. Air over a wing. Water through a pump. Blood through an artery. FEA is finite element analysis, simulating how a solid deforms and where it fails. The load on a bracket, the stress in a bridge, a phone chassis meeting a sidewalk. Even acoustical rendering. All work the same way: chop the thing into millions of small pieces, write down how each piece affects the pieces touching it, and solve. That stiffness matrix K is exactly that bookkeeping for a structure – how every piece pushes back on every other piece.

For those that remember it, here is the math. One operation:

$\mathbf{a}\cdot\mathbf{b} \;=\; \sum_{i=1}^{n} a_{i}\,b_{i}$

And here is that same operation wearing four different hats:

$\begin{aligned} \text{CFD / linear solve:}\qquad (A\mathbf{x})_{i} &= \sum_{j} A_{ij}\,x_{j} \\ \text{FEA / stiffness:}\qquad \mathbf{f} &= K\mathbf{u} \\ \text{Attention:}\qquad \bigl(QK^{\top}\bigr)_{ij} &= \mathbf{q}_{i}\cdot\mathbf{k}_{j} \\ \text{Convolution:}\qquad (f * g)[n] &= \sum_{m} f[m]\,g[n-m] \end{aligned}$

Four fields, four vocabularies, four sets of conferences that do not talk to each other. One sum of products.

And i want to make the strong version of that claim, because it is the point and not a footnote. These are not four analogous operations. They are the same operation. Each one is a bilinear form – a sum of products – and every one of them can be written as

$\mathbf{y} \;=\; M\mathbf{x}$

for some matrix $M$ . The only thing that changes between the four is what $M$ looks like. Sparse for CFD and FEA, because a cell or an element only touches the ones next to it. Toeplitz for convolution, because the same kernel slides along the signal and every row is the row above it shifted by one. Dense for attention – and this is the genuinely new thing in the list – with the matrix produced by the data at runtime rather than fixed in advance by the geometry of a wing or a bracket.

What belongs to this family is the $QK^{\top}$ kernel and the final multiplication by $V$ . The softmax sitting between them is not a bilinear form and is not the same operation at all. Attention is a sandwich of two dot-product layers with a nonlinearity in the middle. The bread is the same. The filling is not.

Same function. Different structure. And here is why that matters for an essay about energy: the structure is where the joules hide. A sparse matrix, a sliding kernel, and a dense data-dependent matrix have wildly different memory access patterns, and memory movement is what costs. The arithmetic is identical across all four. The energy is not even close. Which is exactly the gap a denominator ought to be measuring and the hour never could.

Underneath all of it is the single instruction the silicon actually executes – multiply, add it to what you already had, keep going:

$c \;\leftarrow\; c + a\,b$

That is the whole machine. Everything else is scheduling.

Which means the leverage point is not the model and it is not the framework. Change what a dot product costs and you change everything stacked on top of it, all the way up to the invoice.

Now the part that made me want to write this section.

In a digital machine, a multiply-accumulate is hundreds to thousands of transistors switching, each one dragging charge across a capacitance and dumping it into the heat sink. In an analog crossbar, you apply voltages along the rows, store the weights as conductances at the crosspoints, and let Ohm’s law do the multiply and Kirchhoff’s current law do the sum. The answer arrives on the wire as a current. One step. No clock. No instruction fetch²⁰.

$I_{j} \;=\; \sum_{i} G_{ij}\,V_{i}$

Look at that sum, then look back at the dot product. Same equation. Conductance standing in for one operand, voltage for the other, and Kirchhoff performing the accumulation for free, because current arriving at a node has nowhere else to go.

Read the symbols slowly, because each one is doing something a digital machine has to work for.

$G_{ij}$ is conductance – the reciprocal of resistance, measured in siemens, which is a unit named after a man and not a committee. $V_i$ is the voltage you put on row $i$ . And Ohm’s law, written in the conductance form, says the current through that crosspoint is $G_{ij}V_i$ .

The multiplication happens because the device exists. Nobody issued an instruction. There is no opcode for it. Apply a voltage to a resistor and the multiplication has already occurred by the time you finish reading this sentence, because the electrons are not waiting for a clock edge.

Then the sum. Every crosspoint on column $j$ dumps its current onto the same wire, and charge is conserved, so the wire carries the total whether anybody wanted it to or not. Kirchhoff’s current law is the accumulator. It costs nothing because it is not a step. It is a consequence.

Now the part that matters for this essay. In a digital machine, the weight is a number. It lives in memory, and to use it you fetch it, move it across a bus into a compute unit, multiply, and move the result back and every one of those moves is where the joules actually go. In the crossbar, the weight is the device. Storage and arithmetic are the same physical object. There is no bus, because there is nothing to move.

That is not a faster von Neumann machine. That is a machine with the von Neumann bottleneck removed by construction. (there is another blog here on the aspects of Von Neumann moving ahead, but i digress)

And the cost changes shape though not the way the brochures say. A digital matrix-vector product on an $N \times N$ matrix costs on the order of $N^{2}$ operations, executed one after another in time. The crossbar does every one of them at once and reads the answer in a single settling time. The clock says constant.

The physics does not. The array is still $N^{2}$ devices, every one of them dissipating, so the energy still scales as $N^{2}$ . The settling time grows with the resistance and capacitance of the wires, and the voltage drop along a long row caps a practical array at a few hundred lines, so a large matrix gets tiled across many arrays and the partial sums get added back up digitally. You did not escape the $N^{2}$ . You moved it from time into space and paid for it in joules.

Which is the most useful thing in this whole section, because it is the cleanest demonstration of the argument i am making. The clock says one step. The instruction count says nothing at all. The joules still say $N^{2}$ . Only one of those three gauges is telling you the truth about the work.

Analog is noisy. Device-to-device variability, drift over time, temperature dependence, and an effective precision that is realistically a handful of bits rather than thirty-two. Writing the conductances is slow and wears the devices out, which makes this a good inference machine and a poor training one.

And here is the part that should make you laugh, given where this section started: you still need converters at the edges. Digital in, analog across the array, digital out and in a great many designs the analog-to-digital converters on the output side dominate the energy and the silicon area²¹. The conversion tax does not vanish. It gets pushed to the boundary, where it sits waiting for you, exactly as it did in the wing and the mesh.

But notice what has happened to the denominator while we were looking at physics.

There is no instruction count in that equation. No clock. No opcode stream to add up, because nothing is executing in the sense the word usually means. You can still count the multiply-accumulates on paper – there are $N^{2}$ of them – but nothing on the machine counts them for you, and the meter that every invoice is built on has nothing to read. The hour means nothing. The instruction means nothing.

One quantity survives the trip: the energy it took to settle.

The machine is not simulating the dot product. The machine is the dot product.

And once you say that out loud, the next question asks itself: how far does that go?

A human brain runs on roughly twenty watts²². Two dim light bulbs. It is a chemical, analog, massively parallel machine that computes by being the physics – ions crossing membranes, gradients relaxing, concentrations doing the arithmetic directly. No mesh. No time steps. No clock. Neurons do fire in all-or-none spikes, so it is a hybrid machine rather than a purely analog one – but almost nothing in a brain converts the world into a number and back.

We are currently spending megawatts to approximate a fraction of what twenty watts does natively, and we are doing it by first converting the world into integers. That gap is not a scandal and it is not a mystery. It is a conversion tax, and it is the single largest line item in computing that nobody prices.

Neuromorphic silicon, in-memory compute, photonic matrix units, and the genuinely strange work in molecular and chemical computation are all, whatever their marketing says, one bet: that the way out is to stop converting. To get the atoms to do the arithmetic.

And the only unit that lets you compare any of them is the joule.

That is the argument underneath this entire essay. You cannot compare a GPU-hour to a neuron. You cannot compare a FlexCredit to a chemical reaction network. The unit does not survive the trip across substrates – it is defined in terms of the machine. But joules per converged solution, joules per retained token, joules per useful answer: those are quotable by silicon, by an analog crossbar, by a vat, and by a skull.

Energy is the only denominator that is substrate-independent. Which makes it the only one you can run a market in when the substrates stop being the same.

The Pendulum Knows The Way Home

i wrote an essay a while back about returning to the analog, and i made a claim in it i want to come back to. i said we will have analog computing devices again physics doing the mathematics directly, in proportion, instead of counting its way toward an answer.

i made it then as an aesthetic claim. The crossbar above is the argument i did not have at the time, and it is an energy argument.

Nobody is going back to analog because it is charming. It is coming back because when the binding constraint becomes joules per useful answer, a machine that computes by being the equation holds a thermodynamic advantage that no process node will close.

Analog degrades. Digital detonates. And on the right problem, analog is cheaper in the only unit that cannot be redefined by the company sending the invoice.

A Proposal: The Joule Standard

So let me invent the economic model rather, because if you do not set a standard, someone else will set it for you.

First, the metric. Call it the Landauer Ratio:

$L \;=\; \frac{E_{\text{actual}}}{E_{\min}} \;=\; \frac{E_{\text{actual}}}{k_{B}T\ln 2}$

The problem is that a metric you cannot compute is a slogan.

No real workload tells you how many bits it erased. Nobody instruments that, the compiler does not know, and for anything above a toy circuit the number is not recoverable even in principle without a model of every intermediate the machine threw away. The pure form of that ratio is a thought experiment. It is good for establishing that a floor exists and useless for writing a contract.

The version you can actually bill against is per useful unit:

$L_{\text{unit}} \;=\; \frac{\text{joules per useful unit, measured}}{\text{joules per useful unit, reference}}$

where the reference is a published best-known implementation of the same transformation at a stated level of the stack – not a thermodynamic ideal. That turns an absolute physics claim into a relative efficiency measure, which is weaker, less romantic, and the only one that can be audited. Say plainly which of the two you are quoting. Mixing them is how a number becomes marketing.

Landauer still sets the floor underneath both: about 2.87 zeptojoules per bit erased at room temperature. You have to say at which level of the stack you are measuring, because the number moves enormously depending on where you put the probe. A single CMOS gate switching is something like three to four orders of magnitude above $k_{B}T\ln 2$ . A whole system doing useful work picojoules per bit, counting data movement, the memory hierarchy, the interconnect, and the cooling, lands nearer eight or nine23. The gap between those two numbers is not measurement noise. It is the transport. Which is the thing this entire essay is about.

That ratio, how far above the floor you are, measured at a stated level, is the single most honest quality metric available for a computer, and almost nobody publishes it.

Now the business model, and the design constraint, is the thing that killed the hour: you cannot simply bill per joule. If you do, the provider earns more by burning more and we have rebuilt the exact adversarial gradient we just spent this essay diagnosing. Charging for energy consumed is the hour wearing a lab coat.

So invert it, the way Flexcompute inverted the clock:

$\begin{aligned} \text{Customer pays} \;&=\; U \times J_{\text{ref}} \times p \\ \text{Efficiency carry} \;&=\; U \times \bigl(J_{\text{ref}} - J_{\text{actual}}\bigr) \times p \end{aligned}$

where $U$ is useful units delivered, $J_{\text{ref}}$ the reference joules per unit, $J_{\text{actual}}$ what the provider actually spent per unit, and $p$ the contracted price per joule.

i am calling the second line the efficiency carry rather than the provider’s margin on purpose. They are not the same number, and the difference comes up in a moment.

Three moving parts, and one of them nearly sank the whole proposal, so let me take it head on.

$p$ cannot be the grid price. i wrote it that way first and it is wrong. Energy is a minority of what a fleet of accelerators actually costs – the silicon, the building, the cooling plant, the network and the people dwarf the electricity bill. Price joules at what the utility charges and the revenue never recovers the capital. The model collapses on contact with a balance sheet.

So $p$ is a contracted compute-joule price: the cost of one delivered joule of useful computation with the capital amortized inside it, indexed to a published grid reference so the energy component moves transparently instead of silently.

Which also means the efficiency carry is not profit. Once the capital lives inside $p$ , what one delivered joule actually costs the provider depends on how busy the hardware is – a fleet running at forty percent utilization carries more than twice the capital per joule of one running at ninety. Cutting joules turns into margin only two ways: more useful units out of the same assets, or an energy bill large enough that the saving shows up directly. A provider that gets efficient and leaves its silicon idle has improved its Landauer Ratio and lost money doing it. The carry is the incentive. Utilization is still the business.

A contracted price is a negotiated number. There is a credere living inside $p$ . i spent the first half of this piece separating beliefs from measurements, and here is a belief sitting in the middle of my own mechanism. The difference – and it is the whole difference – is that it sits in exactly one place, it is named, and it is indexed to something public. The hour hides a belief in every term and indexes none of them.

The useful unit is domain-defined and public: a converged simulation at a stated residual, a retained token, a resolved design candidate, a validated inference. It is the thing the customer actually came to buy.

The reference joules per unit is a published, periodically revised benchmark – the way the Green500 or SPEC work²⁴, and with all of the same politics, which i will come back to. It is the industry’s current honest estimate of what this transformation costs in energy.

The price per joule is the contracted number from above the one place a belief is allowed to live – with its energy component indexed to a grid, so the part of it that moves is set by someone other than the vendor.

As written, the provider keeps the entire carry until the reference is revised, and that is not how performance contracting works anywhere it has survived. Energy service companies split measured savings with the customer²⁵, because a contract where one party captures everything gets renegotiated in bad faith the moment it starts to bite. So put a share in it:

$\text{Provider's carry} \;=\; \alpha \, U \, \bigl(J_{\text{ref}} - J_{\text{actual}}\bigr)\, p$

with the customer taking the rest. And here is the trade nobody gets around: you can have a bill that is predictable before launch, or you can have per-transaction savings sharing. Not both in the same term. The moment the customer’s share flows back per job, the invoice depends on $J_{\text{actual}}$ and the up-front certainty is gone.

My own preference is $\alpha = 1$ per transaction – full predictability, provider keeps the carry – with the sharing happening intertemporally, through the ratchet. Revise $J_{\text{ref}}$ down on a published schedule and the customer collects every efficiency gain on a known cadence instead of a noisy one. Buyers who would rather have the upside than the certainty take an $\alpha$ below one and accept a variable bill. Write both contracts. Let the counterparty pick which uncertainty they would rather hold.

What that produces is the FlexCredit property one level down – a bill fixed before launch and tied to the customer’s own problem, and a provider whose every improvement to the solver, the memory hierarchy, the interconnect, the cooling or the substrate lands in its carry without a pricing committee. A performance contract, applied to computation itself.

And the reference ratchets. When the benchmark is revised down, yesterday’s carry becomes today’s table stakes – which is precisely what Moore’s law should have done to pricing for forty years and never did, because the unit of account was a machine-hour and machine-hours silently got cheaper while the invoice did not.

Why it matters for the chemistry. Under this model, a neuromorphic chip, a photonic unit, an analog crossbar, and a reaction network can all bid for the same job, because they quote in the same unit and get paid for the same thing. A substrate that is ten thousand times more efficient on a narrow class of problems does not need to win a general-purpose benchmark or displace an ecosystem. It just needs to show up with a better Landauer Ratio on one workload and collect the carry. That is how you get a market in exotic computation instead of a research program.

Where It Measured

Metering. Attributing energy to a single job across shared, virtualized, multi-tenant infrastructure at fine grain is genuinely hard and partly unsolved. Rack-level telemetry exists; honest per-job attribution including memory movement, network, and cooling overhead does not, at the fidelity a contract would need. This is the real engineering problem and it is where i would start if i were building the company.

Benchmark capture. Whoever writes the reference wins, and incumbents will try to write it. Any version of this governed by the largest providers converges on a baseline generous enough to guarantee their own margin.

Naming a risk is not a design, so here is the design. $J_{\text{ref}}$ is not negotiated. It is set at a stated percentile of measured results on a published, versioned benchmark suite, administered by a body in which buyers hold at least as many seats as sellers, with the methodology public and every revision carrying an announced effective date so nobody can reprice retroactively. And there is a challenge procedure: any party – a competitor, a customer, an academic with a cluster – can submit a measured workload that beats or misses the published reference by more than a stated threshold and force a review inside a fixed window.

And the percentile cannot be drawn only from what sellers choose to report. Any target set from last period’s results teaches the people being measured to hide their best results – the ratchet effect, which economists documented in Soviet production quotas long before anyone built a data center²⁶. So the percentile is computed over every valid run: challengers’ and buyers’ measurements included, and every run on a contracted fleet reported, not a curated few. A provider that sandbags the benchmark to keep the reference fat should find a customer’s audit run sitting in the same distribution.

None of that is novel. It is how every serious measurement regime ends up working, from fuel economy to electrical safety, and every one of them was captured at least once on the way there. Assume this one will be too, and build the challenge path first rather than bolting it on after the first scandal.

The cheapest adequate answer. A provider paid on the efficiency gap has an incentive to find the least expensive result that passes. In simulation that means coarser meshes and looser convergence; in inference it means quiet quantization and shorter effective context. Any contract like this needs a correctness floor with teeth, independently verified, or the efficiency carry becomes a quality tax paid by the customer who cannot audit.

Not every domain has a useful unit. Simulation does. Retrieval does. Batch inference does. Open-ended conversation does not, at least not yet, and pretending otherwise is how you get a metric that measures the wrong thing extremely precisely.

What This Means For Everybody Building Right Now

The entire industry is currently in the largest capital buildout of my lifetime, and almost all of it is denominated in hours. GPU-hours, instance-hours, reserved capacity, committed spend.

Meanwhile go read what is actually holding projects up. It is not chips. It is interconnect queues. Substation capacity. Transformer lead times measured in years. Power purchase agreements. Siting next to a river or a reactor.

The constraint has been quietly turning into the watt, and the invoices have not caught up.

The numbers are public and they are not subtle. Berkeley Lab counted about 2,061 GW actively waiting in US interconnection queues at the end of 2025: approximately 1,312 GW of generation and 749 GW of storage, and in the regions that report it, the median wait from interconnection request to commercial operation ran over five years for projects that actually came online in 2025. The IEA puts data centers at roughly 415 TWh in 2024, about 1.5 percent of world electricity, headed for about 945 TWh by 2030, and estimates that around 20 percent of planned data center projects are at risk of delay unless grid constraints are addressed.²⁷ New transmission takes four to eight years in advanced economies. Nobody is waiting four to eight years for a GPU.

That gap – between the unit everyone is contracting in and the unit the physics is actually charging in is where the next decade of margin is going to be won and lost. It is the same shape as every call i have gotten right in thirty years, and now that i have split the sentence in two i can state it properly: own the layer everyone else treats as free, and meter it in the unit the physics is already using. In 2009, the layer meant underneath keywords. In 2026 it is the silicon and the power underneath the tokens – the tokens themselves being the meter, not the layer. And the unit the physics is already using, all the way at the bottom, holding up every other number in this essay, is a joule.

Flexcompute did not go all the way down. They went one level, from the clock to the problem, and that one level was enough to flip their incentive gradient from adversarial to aligned. That is the lesson, and it is available to anybody reading this:

Look at what you charge for. Then ask what behavior that denominator rewards when nobody is watching. That is what your company actually does for a living, regardless of what is on the wall.

The Stance

Find your denominator and say it out loud. Not your revenue – your unit. The thing you multiply by to get paid.

Then run the honesty test: if your product got twice as good tomorrow, would you make more money or less? If the answer is less, you do not have a pricing problem. You have an architecture that is quietly betting against your own engineers, and they can feel it even if they cannot name it.

And if you are building anything that will still be running in 2030, start carrying a second set of books in joules. Not because finance will ask. Because physics already is, and physics does not send invoices – it just stops giving you more.

Publish your Landauer Ratio before somebody else computes it for you. It is going to be an embarrassing number. It is an embarrassing number for everybody, which is exactly why the first few people to say it out loud will own the conversation.

The meter was always running in joules, Dear Reader. We have simply been reading the wrong gauge.

MUZAK TO BLOG BY: Kraftwerk, Radio-Activity. Men who understood that the signal and the power bill are the same conversation. On a pair of really good cans, at a volume that costs real watts. Check the track listing for the metaphor. The bass function for the rap beats we hear today.

Until then,

#iwishyouwater <- 2026 Small Mavericks in Half Moon Bay (i truly miss HMB)

#EverForward,

𝕋𝕖𝕕 ℂ. 𝕋𝕒𝕟𝕟𝕖𝕣 𝕁𝕣. (@tctjr) / X

Notes and References

Everything below was checked against the primary source. Where i could not verify something, the note says so instead of dressing it up. Prices and benchmark figures move; the dates are there so you know how stale a number is when you find it.

Epigraph. Rolf Landauer, “The physical nature of information,” Physics Letters A 217 (1996), 188-193. ↩
Epigraph. Claude E. Shannon, “A Mathematical Theory of Communication,” Bell System Technical Journal 27 (1948), 379-423 and 623-656. The line quoted is from the opening section, where Shannon is choosing his unit – which is the whole subject of this essay. ↩
Flexcompute. Company and product pages: flexcompute.com for Flow360, the CFD solver, and the Tidy3D FDTD electromagnetics solver. i have no relationship with the company and no position in it. i read their documentation because the pricing model is interesting. ↩
What a FlexCredit actually is. From Flexcompute’s own Tidy3D FAQ: “A FlexCredit reflects the size of a simulation, in both grid points and timesteps. It does NOT reflect the compute time required to run the simulation,” and “The cost of a simulation is primarily affected by the number of grid points and time steps.” Tidy3D learning-center FAQ, re-checked 30 October 2026. The billing page says the same thing in its own words: “FlexCredits are based on the size of the simulation (in spatial size and physical simulation duration),” with the printed maximum “determined by the cost associated with simulating the entire time stepping specified.” How is using Tidy3D billed? ↩
Flow360, scoped on purpose. The claim above is Tidy3D’s and i am not extending it to the CFD solver without a source. What is public: the Flow360 technical brief bills in “FlexUnits” – a different word from Tidy3D’s FlexCredit – and prices them at “$3.5/FlexUnit” against a NASA reference solver at “$0.04/CPU-hour,” so the unit is plainly not a CPU-hour. But the public materials i can find do not state what determines a FlexUnit. Flow360 Technical Brief (PDF). If you need the Flow360 terms, get them from the company in writing rather than from this essay. ↩
Priced before it runs, prorated if it stops early. “When a task is uploaded to our servers, we will print the maximum incurred cost in FlexCredit… This cost will be pro-rated if early shutoff is detected and the simulation is completed before the time stepping period.” How is using Tidy3D billed? ↩
One FlexCredit, roughly fifty CPU core-hours. Flexcompute’s own rough comparison for traditional FDTD – about two days of one core running continuously, with sixty FlexCredits put at roughly a four-core machine running flat out for a month. They qualify it “at the time of writing,” which is the right way to quote a moving number. How many CPU hours is one FlexCredit comparable to? ↩
Green500, and how fast a joule drifts. The Green500 ranks supercomputers by performance per watt, published twice a year alongside the TOP500. top500.org/lists/green500. For the long-run trend in computations per joule – the drift i say the joule is not immune to – see Jonathan Koomey, Stephen Berard, Marla Sanchez and Henry Wong, “Implications of Historical Trends in the Electrical Efficiency of Computing,” IEEE Annals of the History of Computing 33:3 (2011), 46-54, which found computations per kilowatt-hour doubling about every 1.57 years from the 1940s on; and Jonathan Koomey and Samuel Naffziger, “Moore’s Law Might Be Slowing Down, But Not Energy Efficiency,” IEEE Spectrum (2015), which puts the doubling time since 2000 nearer 2.6 years. ↩
Landauer’s principle. Rolf Landauer, “Irreversibility and Heat Generation in the Computing Process,” IBM Journal of Research and Development 5:3 (1961), 183-191. Full text (PDF), IEEE ↩
The number itself. $k_{B}T\ln 2$ at $T = 300$ K works out to $2.87 \times 10^{-21}$ J. The $3.5 \times 10^{20}$ erasures per joule is just the reciprocal of that – do it on a napkin, you do not need a source. For the experiment showing the bound is physically real and not an accounting identity: Antoine Bérut, Artak Arakelyan, Artyom Petrosyan, Sergio Ciliberto, Raoul Dillenschneider and Eric Lutz, “Experimental verification of Landauer’s principle linking information and thermodynamics,” Nature 483 (2012), 187-189. PubMed, PDF ↩
Reversible computation. Charles H. Bennett, “Logical Reversibility of Computation,” IBM Journal of Research and Development 17:6 (1973), 525-532 – the result that a computation carrying no erasure has no Landauer floor underneath it. Full text. This is the honest escape hatch under the whole section, and it is why the bound in note 9 is on erasure and not on computation. ↩
The Shannon limit on energy per bit. The $E_b/N_0 \ge \ln 2 \approx -1.59$ dB floor for reliable communication falls out of the Shannon-Hartley capacity as bandwidth goes to infinity. Standard treatment: MIT OCW 6.451, Principles of Digital Communication II, ch. 4 (PDF), and the E_b/N_0 summary for the one-line version. ↩
The token as a unit of account. My own earlier piece on this: The Token Is The New Constraint (25 April 2026), where i argued that “every token is billable, forecastable, and optimizable” and that tokens “define bounded buffers of context.” That essay treats the token as a constraint. This one argues it is a meter, which is the correction. ↩
SnakeByte[21]. SnakeByte[21]: The Token Arms Race – Architectures Behind Long-Context Foundation Models (9 July 2025). The two lines i quote myself on are there verbatim: “Metered billing based on tokens stored and tokens retrieved,” and the sign-off, “the architectural arms race is becoming a memory computation problem,” followed by “Got More Ram?” i meant the last one as a joke. It was not a joke. ↩
Quadratic attention. The complexity claim in SnakeByte[21] – “a computational complexity that scales quadratically with the sequence length” – traces to the original formulation: Ashish Vaswani et al., “Attention Is All You Need,” NeurIPS 2017, arXiv:1706.03762. Every long-context architecture since has been an attempt to get out from under that exponent. ↩
Prefill and decode are two different machines. My survey: A Survey Of Architectures And Methodologies For Distributed LLM Disaggregation (August 2025). The primary literature: Yinmin Zhong et al., “DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving,” OSDI 2024, arXiv:2401.09670 / USENIX; and Pratyush Patel et al., “Splitwise: Efficient Generative LLM Inference Using Phase Splitting,” arXiv:2311.18677. Prefill is parallel and compute-bound; decode is sequential and bound by memory bandwidth. That is the whole reason the field started pulling them onto separate clusters. ↩
Moving the operand costs more than the arithmetic. Mark Horowitz, “Computing’s Energy Problem (and what we can do about it),” ISSCC 2014 plenary, doi:10.1109/ISSCC.2014.6757323 – slides/paper (PDF). This is the paper everyone cites for the gap between the energy of an operation and the energy of fetching its operands, and it is the quantitative backbone of the argument that the denominator belongs on data movement rather than on FLOPs. ↩
Token price tiering. Checked against providers’ published pricing in October 2026. The pattern is consistent across the market: output priced several times above input, cached input discounted steeply, and at least one flagship model carrying a higher rate above roughly a 200,000-token prompt. These change often enough that you should read the current page rather than trust this one – Gemini API pricing, Claude pricing, and the equivalent page from whichever provider you are actually billed by. The argument in the essay does not depend on the exact multiples; it depends on the fact that the tiers exist and map onto prefill, decode and cache reuse. ↩
The analog essay. Return To The Analog (aka Life Is Not Band-Limited). That piece made the case on aesthetic and information-theoretic grounds. The crossbar section above is the energy argument i did not have at the time. ↩
Analog in-memory computing. The crossbar primitive – conductances as weights, Ohm’s law as the multiply, Kirchhoff’s current law as the accumulate – is the organizing idea behind a large body of work on in-memory and compute-in-memory accelerators. Reasonable entry points: “Analog or Digital In-memory Computing? Benchmarking through Quantitative Modeling,” arXiv:2405.14978, and “Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference,” arXiv:2406.08413. ↩
The converters eat the win. That data conversion at the array boundary dominates energy and area in many crossbar designs is a well-documented result, not a rhetorical flourish: see Anirban Nag et al., “Newton: Gravitating Towards the Physical Limits of Crossbar Acceleration,” arXiv:1803.06913, and the benchmarking paper in note 20. This is the same conversion tax as the mesh and the wing, relocated to the edge of the chip. ↩
Twenty watts. The brain runs about 20 percent of human resting metabolism on roughly 2 percent of body mass; against a resting budget near 100 W that lands at about 20 W. Source for the 20 percent figure: D. F. S. Rolfe and G. C. Brown, “Cellular energy utilization and molecular origin of standard metabolic rate in mammals,” Physiological Reviews 77:3 (1997), 731-758. For where the brain’s energy actually goes – mostly signaling – David Attwell and Simon B. Laughlin, “An energy budget for signaling in the grey matter of the brain,” Journal of Cerebral Blood Flow and Metabolism 21:10 (2001), 1133-1145 (PubMed 11598490); both are summarized with their antecedents at Harvard BioNumbers. Two dim light bulbs is a figure of speech, not a measurement – but it is the right order. ↩
Where the probe goes. The “three to four orders of magnitude” for a single CMOS gate versus “eight or nine” for a whole system doing useful work is a level-of-abstraction statement, not a single published number, and i want to be exact about why. A gate’s switching energy sits a few thousand times above $k_{B}T\ln 2$ . A system-level figure – picojoules per bit, counting the memory hierarchy, the interconnect, the cooling and the idle – lands several orders further out, which follows directly from the energy breakdowns in note 17. The spread between them is transport, and it is the only reason this essay has anything to propose. If you quote a Landauer Ratio, name your level or the number means nothing. ↩
Benchmark governance. Green500 and SPEC are the working models for a versioned, periodically revised public benchmark, including the politics. Both have been gamed. Both survived it by publishing methodology and run rules. That is the design to copy, challenge procedure and all. ↩
Performance contracting. The shared-savings and guaranteed-savings structures the Joule Standard is cribbing from have been in use for decades in energy services. See the IEA’s survey of energy performance contract models and, on the US federal side, Energy Savings Performance Contracts. The relevant lesson is not the arithmetic – it is that measurement and verification, not the contract language, is where these deals live or die. ↩
The ratchet effect. Martin L. Weitzman, “The ‘Ratchet Principle’ and Performance Incentives,” Bell Journal of Economics 11:1 (1980), 302-308 – the formal treatment of what happens when next period’s target is set from this period’s performance: the agent learns to under-report. Joseph Berliner’s fieldwork on Soviet factory managers documented the behavior decades earlier. Any benchmark that sets $J_{\text{ref}}$ from self-reported results inherits it, which is why the percentile has to be drawn from runs the sellers do not get to curate. ↩
The grid is the actual constraint. Joseph Rand, Anna Cheyette, Chris Talley, Steven Zhang, Will Gorman, Ryan H. Wiser, Joachim Seel, Seongeun Jeong and Fredrich Kahrl, Queued Up: 2026 Edition – Characteristics of Power Plants Seeking Transmission Interconnection As of the End of 2025, Lawrence Berkeley National Laboratory, May 2026: about 2,061 GW actively seeking interconnection – 1,312 GW of generation and ~749 GW of storage – with a median of 61 months from interconnection request to commercial operation for projects built in 2025, among the projects with a valid in-service date (about 73 percent of them). i have quoted capacity rather than a project count because capacity is the figure i could confirm. Berkeley Lab. And International Energy Agency, Energy and AI (2025): data centers at “around 1.5% of the world’s electricity consumption in 2024, or 415 terawatt-hours,” projected to “more than double to around 945 TWh by 2030,” with roughly 20 percent of planned data center projects at risk of delay absent grid fixes, and new transmission taking four to eight years in advanced economies. IEA Executive Summary. ↩

A note on what is not cited. The economic model in A Proposal: The Joule Standard is mine and it is new, which means it has no reference because nobody has built it. The Landauer Ratio as defined here – measured joules per useful unit over a published best-known implementation at a stated level of the stack – is also mine, and it is deliberately weaker than the thermodynamic version so that it can actually be audited. Treat both as proposals, not findings. The physics in this essay is checkable and i have given you the receipts. The economics is an argument, and arguments are supposed to be attacked.

SnakeByte[23]: Maynard-Cross Learning (MCL): Hive Minds as a Single RL Agent

First i trust everyone is safe.

Second, i have been sitting on a draft SnakeByte for a while about a paper that does what i wish more swarm literature would do. It stops waving its hands at “emergence” and writes the update rule down. Instead of picking a library or method i decided to randomly pick cool papers. i just haven’t had a chance to sit with this one long enough for prime time. This paper caught my eye on the Keywords: Swarm Intelligence, Reinforcement Learning, Evolutionary Game Theory,
Evolutionary Biology.

A population of agents following a simple, local, purely imitative weighted-voter rule is mathematically equivalent to a single abstract reinforcement learning agent that updates its policy with Maynard-Cross Learning.

~ the actual claim

i guess this is where i am supposed to say, “A banger of a paper just dropped, and it shows how you can spin up 1-Billion Agents to make you 1-Billion Dollars In A Month.” i digress.

The paper is The Hive Mind is a Single Reinforcement Learning Agent (Soma, Bouteiller, Hamann, Beltrame, arXiv:2410.17517). Honey bees. Nest sites. Waggle dances. The usual natural-history furniture. Underneath that furniture is a bandit. Quantum spooky distance of the hive mind.

Can a single RL Agent perform as The Hive?

What the bees are actually doing

A scout finds a cavity. She grades it the way any field engineer grades a site: volume, exposure, how hard it is to get home. She comes back and dances. Duration and vigor scale with the reward she thinks she sampled. Another bee does not run a comparison. She copies the first dancer she runs into inside a small neighborhood. Blind imitation. Local. Cheap.

Do that with a few hundred scouts and the fraction of the swarm committed to each site starts to behave like a policy $(\pi)$ . Not a metaphor. A probability vector. The hive is not a mind hovering over the individuals. The hive is the update those individuals implement when you add them up.

MCL is that update. It sits in the same family as Cross Learning, which sits downstream of the replicator story Maynard Smith opened and Taylor, Jonker, Schuster, and Sigmund actually wrote as dynamics. Maynard Smith gave us the equilibrium. Other people wrote the motion. MCL is motion with a normalization the bees accidentally invented.

The discrete rule

One bee, one sample. She is committed to site $(k)$ and comes home with a noisy reward $(r_k)$ . The population policy moves like this:

$\forall a,\quad\pi_a \leftarrow \pi_a + \alpha \frac{r_k}{v}\begin{cases}1 - \pi_a & \text{if } a = k, \-\pi_a & \text{otherwise.}\end{cases}$

$where (\alpha = 1/N)$

The swarm size is the learning rate. Bigger swarm, smaller step. The organism learns more slowly because there are more of them. That is not a bug. That is averaging. Pretty much the same physics as a learning rate.

You do not know the true mean rewards $(q)$ . So $(v)$ in the denominator is not an oracle. In any implementation that can actually run, $(v)$ is an online estimate. A moving average of rewards already seen. The normalization is “how good was this sample relative to what we have been getting.”

Skip the update when the estimate is sitting on zero. Do not divide by faith.

The continuous limit, and why the extra factor matters

Take the population large and the step small and you land on the Maynard-Smith replicator dynamic:

$\dot{\pi}_a = \frac{\pi_a}{v^{\pi}}\bigl(q_a^{\pi} - v^{\pi}\bigr),\qquad q_a^{\pi} = \mathbb{E}[r_a].$

Classic Cross Learning, pushed to the same limit, gives you the Taylor replicator:

$\dot{\pi}_a = \pi_a \bigl(q_a^{\pi} - v^{\pi}\bigr).$

The only new object is $(1/v^{\pi})$ . Absolute rewards wander. Noise wanders with them. Dividing by the current average value keeps the step from blowing up when every site is paying well and from stalling when every site is paying poorly. It is a baseline. Bees did not call it a baseline. The algebra does.

The paper’s conditions for reliable convergence include a neighborhood that is not tiny. Something like $(M \ge 5)$ before the quorum story gets trustworthy. Local does not mean “talk to one neighbor and call it wisdom.” Local means “small relative to $(N)$ , large enough to estimate a vote.” Hold that sentence. The plots below will test your patience.

One agent, N environments

Read the micro-rule from the macro side and the RL picture snaps into focus.

Each bee samples an action from $(\pi)$ . That is her current preference.
She receives a noisy reward from her own site visit. That is one environment.
Imitation averages those samples into the next $(\pi)$ .

That is a single online bandit agent aggregating action-value samples from $(N)$ parallel environments. The individuals are not learners in the gradient sense. They are samplers with a copy rule. The learner is the distribution.

i want to be careful about the evolutionary sentence, because it is the one people will over-claim( omg lets anthropormoisize yet another algorithm). The equivalence supplies a plausible selective pressure for the imitation rule: a group that copies this way is doing group-level reinforcement learning whether or not any bee could tell you that. The paper does not prove that kin selection “favors group-level RL” as a primary result. It shows you a mechanism that would be worth keeping if selection ever got its hands on it. Mechanism first. Story second.

Why i care, and why you might

Most “swarm intelligence” (papers, decks, napkins) still hand you a metaphor and a video of drones in a pretty formation. This hands you a pseudo-compiler. If you know which bandit update you want at the macro scale, you can ask what local imitation rule realizes it. If you already have a local rule, you can ask which RL agent you accidentally built.

That is a design tool. Drone search. Sensor tasking and orchestration. Any flock of cheap agents that should not each be running a policy network. You do not put a sophisticated learner in every airframe. You put a sampler and a dance in every airframe, and you let the population be the learner.

Same mathematics, opposite direction of work. Derive the local rule from the update you want. Or read the update off the rule you already shipped.

The notebook:

# Maynard-Cross Learning: one abstract agent, and the bees it claims to be.
# Soma, Bouteiller, Hamann, Beltrame — arXiv:2410.17517
#
# Macro: streaming MCL. One sample per step. Learning rate alpha = 1/N.
#        v is NOT the true mean reward. It is an exponential moving average
#        of rewards actually observed. That is the version you can ship.
#
# Hive:  N agents. Each holds a discrete site preference.
#        Each step, every agent hears M neighbors and copies with
#        probability proportional to the rewards those neighbors just drew.
#        Nobody stores pi. The histogram of preferences is pi.

import numpy as np
import matplotlib.pyplot as plt

# --- bandit and swarm ---
K = 5            # sites / arms
N = 200          # bees; also sets alpha = 1/N on the macro side
T = 2500         # steps. clocks differ; see the post
delta = 0.15     # uniform noise half-width around each q_a
beta = 0.02      # EMA rate for the online baseline v
M = 8            # neighborhood size for the healthy hive
num_runs = 4     # macro seeds for the mean/std band
SEED = 42

q = np.linspace(0.25, 0.85, K)   # 0.25, 0.40, 0.55, 0.70, 0.85
optimal = int(np.argmax(q))      # arm 4


def run_mcl_macro(T=T, seed=0, q=q):
    """Serial MCL. One environment sample per tick."""
    local = np.random.default_rng(seed)
    alpha = 1.0 / N
    pi = np.ones(K) / K
    v_est = 0.0
    history = np.zeros((T, K))
    arms = np.arange(K)

    for t in range(T):
        history[t] = pi
        k = local.choice(K, p=pi)
        r = np.clip(local.uniform(q[k] - delta, q[k] + delta), 0.0, 1.0)

        # online baseline. first reward seeds it; after that, EMA.
        v_est = r if t == 0 else (1.0 - beta) * v_est + beta * r
        if v_est < 1e-8:
            continue

        # MCL: reinforce k by (1 - pi_k), decay every other arm by pi_a,
        # then scale by alpha * (r / v).
        update = alpha * (r / v_est) * np.where(arms == k, 1.0 - pi, -pi)
        pi = np.clip(pi + update, 0.0, None)
        total = pi.sum()
        pi = pi / total if total > 0.0 else np.ones(K) / K

    return history


def run_hive(T=T, seed=0, M=M, q=q):
    """Explicit bees. No policy vector. Histogram only."""
    local = np.random.default_rng(seed)
    prefs = local.integers(0, K, size=N)   # each bee's current site
    history = np.zeros((T, K))

    for t in range(T):
        counts = np.bincount(prefs, minlength=K).astype(float)
        history[t] = counts / N

        # every bee samples a noisy reward for the site she currently likes
        rewards = np.clip(
            local.uniform(q[prefs] - delta, q[prefs] + delta), 0.0, 1.0
        )

        # one neighborhood draw per bee, then a weighted copy
        new_prefs = prefs.copy()
        neigh = local.integers(0, N, size=(N, M))
        for i in range(N):
            js = neigh[i]
            votes = np.bincount(prefs[js], weights=rewards[js], minlength=K)
            total = votes.sum()
            if total > 0.0:
                new_prefs[i] = local.choice(K, p=votes / total)
        prefs = new_prefs

    return history


pi_macro = run_mcl_macro(seed=SEED)
pi_hive = run_hive(seed=SEED, M=8)
pi_hive_small = run_hive(seed=SEED, M=2)   # thin vote; easy bandit still locks

percent_opt = np.vstack([
    run_mcl_macro(seed=SEED + r)[:, optimal] * 100.0
    for r in range(num_runs)
])
mean_opt = percent_opt.mean(axis=0)
std_opt = percent_opt.std(axis=0)

# Figure 1 — full policy, both ontologies
fig, axes = plt.subplots(1, 2, figsize=(12, 5), sharey=True)
for a in range(K):
    axes[0].plot(pi_macro[:, a], label=f"arm {a}  q={q[a]:.2f}")
    axes[1].plot(pi_hive[:, a], label=f"arm {a}  q={q[a]:.2f}")
axes[0].set_title("Macro MCL (one abstract agent)")
axes[1].set_title(f"Hive (N={N} bees, neighborhood M={M})")
for ax in axes:
    ax.set_xlabel("step")
    ax.set_ylabel(r"policy mass ")
    ax.set_ylim(0, 1.02)
    ax.grid(True, alpha=0.3)
    ax.legend(fontsize=8, loc="center right")
fig.tight_layout()
fig.savefig("mcl_policy_evolution.png", dpi=140)

# Figure 2 — mass on the best arm, plus a too-easy small neighborhood
fig, ax = plt.subplots(figsize=(12, 5.5))
ax.plot(pi_macro[:, optimal] * 100.0, color="C0", lw=2.0, label="Macro MCL (this seed)")
ax.plot(pi_hive[:, optimal] * 100.0, color="C1", lw=2.0, label="Hive, M=8")
ax.plot(pi_hive_small[:, optimal] * 100.0, color="C3", lw=1.6, label="Hive, M=2 (too local)")
ax.plot(mean_opt, color="black", lw=1.4, ls="--", label=f"Macro mean over {num_runs} seeds")
ax.fill_between(
    np.arange(T), mean_opt - std_opt, mean_opt + std_opt,
    color="0.2", alpha=0.15, label="macro +/- 1 std",
)
ax.set_xlabel("step")
ax.set_ylabel("percent of mass on the best arm")
ax.set_title("Same bandit, three stories: abstract update, healthy hive, thin vote")
ax.set_ylim(0, 105)
ax.grid(True, alpha=0.3)
ax.legend(loc="center right", fontsize=9)
fig.tight_layout()
fig.savefig("mcl_optimal_convergence.png", dpi=140)
plt.show()

print(f"final macro   {pi_macro[-1, optimal]:.3f}")
print(f"final hive M8 {pi_hive[-1, optimal]:.3f}")
print(f"final hive M2 {pi_hive_small[-1, optimal]:.3f}")

Ok what is all of this doing?

On this seed the final mass on the best arm is 0.952 for the abstract MCL agent, 1.000 for the hive with (M=8), and 1.000 for the hive with (M=2).

That is exactly what the plots show. The abstract agent converges steadily. The hive converges “violently”. (Ya know you have to have some human adjective for code or a storm).

If your hive runs slower, change the problem before you change the theory. Shrink the reward gap. Increase the noise. Make the vote harder. That is the next experiment. If the macro agent converges toward the best arm and the hive does not under the same reward structure, then look at the voting implementation first. The equivalence is not rescued or refuted by a bad bincount.

A batch macro update would give you the fairer clock: draw $(B=N)$ actions from $(\pi)$ , collect $(N)$ rewards, average the MCL increments, and apply the aggregate update once. At that point the blue macro curve should stop looking quite so lazy.

Note your probably wondering why not use DEAP? It is the wrong first tool. Nothing here is evolving a genome or code base. This is a probability distribution concentrating under reinforcement. If, later, you want to evolve the dance itself so that a local swarm rule realizes some other desired macro-level bandit update, then put run_hive inside a fitness function and evolve the microscopic rule. Different experiment. Different blog. Maybe sometime in the future.

This one is the equivalence, watched with the lights on cause i’d rather see bees than hear them.

What the run actually did

Five arms. Mean rewards:

$q = (0.25,\ 0.40,\ 0.55,\ 0.70,\ 0.85).$

Uniform reward noise has half-width

$\delta = 0.15,$

with samples clipped to ([0,1]).

Arm 4 is optimal with expected reward (q_4=0.85).

The swarm contains

$N=200$

bees, which also sets the serial macro learning rate to

$\alpha = \frac{1}{N}=\frac{1}{200}.$

The online value baseline uses an exponential moving average with

$\beta = 0.02.$

The run lasts (T=2500) steps. The healthy hive uses a neighborhood size (M=8). A second hive uses (M=2). We repeat the macro comparison across four seeds.

Same bandit. Same reward noise. Same initial ignorance. Very different clocks.

Figure 1 — Macro MCL policy evolution

The abstract MCL agent begins uniformly:

$\pi_a = 0.2$

for all five arms.

From there, arm 4 starts accumulating mass while the weaker alternatives are progressively starved.

The movement is not monotonic because the agent is sampling noisy rewards one observation at a time. Arm 3, with (q=0.70), survives longest because it is the nearest competitor. The lower-reward arms disappear much earlier.

For the seed shown:

around step 100, arm 4 holds (0.213) of the policy;
around step 500, it holds (0.518);
around step 1000, it holds (0.725);
at step 2499, it holds (0.952).

Across the four macro seeds, the final mass on arm 4 ranges from approximately (91.5%) to (97.9%), with a final mean of (95.2%) and a standard deviation of about (2.37) percentage points.

The walk is noisy. The destination is not (which is usually always the case in life).

Figure 2 — Hive policy evolution, (M=8)

The hive is doing something operationally very different. This is a test of the public bee broadcasting system this is only a test.

There is no global $(\pi)$ stored anywhere. Will we converge? The suspense is killing me i hope it lasts!

Each bee carries only a discrete site preference. Each bee samples a noisy reward from that site. Each bee then looks at (M=8) randomly selected neighbors and copies a site according to the neighbors’ reward-weighted votes. The policy exists only as the histogram of those preferences. And that histogram locks fast.

With (N=200) and (M=8), the optimal arm exceeds (95%) of the population by about step 22, exceeds (99%) by about step 24, and reaches complete fixation by about step 33.

The other four traces collapse onto the floor. That does not mean the hive discovered a better learning rule. It means the hive is consuming much more information per tick (i’m not sure if that is the correct wording; you can’t consume information?). The serial macro loop samples one action and one reward per step.

The hive samples roughly (N=200) rewards and performs roughly (N=200) local imitation decisions per step. Same family of mathematics. Very different amount of work per tick.

That distinction matters.

Figure 3 — Best-arm convergence

The convergence plot makes the clock mismatch explicit.

Blue is the serial Macro MCL run for seed 42.
Orange is the hive with (M=8).
Green is the hive with (M=2).
The dashed red line is the mean of the four macro runs.
The shaded band is $(\pm 1)$ standard deviation across those macro runs.

Both hive variants race to the ceiling while the macro agent climbs gradually toward the same winner.

The (M=8) hive reaches complete fixation at roughly step 33.

The (M=2) hive takes longer, but only slightly: it exceeds (95%) around step 37 and reaches complete fixation around step 58.

So on this particular bandit, (M=2) does not fail.

That is important.

The reward means are spaced by (0.15), and the noise half-width is also (0.15). Arm 4 is sufficiently better than the alternatives that even a very thin neighborhood vote usually discovers the correct direction.

The paper’s warning about very small neighborhoods concerns reliability when the alternatives are harder to distinguish: smaller reward gaps, heavier noise, or situations where a lucky local sample can pull a tiny neighborhood into premature consensus. That is the experiment to run next. Let’s Shrink the gap.

Now let us increase $(\delta)$ .

Then see whether (M=2) starts believing the first charismatic (tiny) dancer it meets. Hold me close. Get the lyric?

Figure 4 — Early-time convergence

The early-time zoom is probably the clearest picture in the set because it removes the long flat tail after the hive has already converged. Within the first 250 steps, the distinction is obvious.

The (M=8) hive climbs from roughly (20%) initial representation to complete fixation in only a few dozen collective updates.

The (M=2) hive is somewhat noisier and slower, but still reaches fixation before step 60.

Over the same interval, the serial macro agent is still wandering between roughly (20%) and (30%) policy mass on the optimal arm.

Again: do not read that as intelligence. Read the clock. One side is processing one reward sample per tick. The other side is processing a population-sized batch. The comparison is deliberately unfair in wall-clock information because that unfairness exposes the architecture.

If you want schedule equivalence, batch the macro learner with (B=N), or slow the hive down to one imitation event per tick. What the plots show cleanly is something narrower and more interesting: the abstract update and the swarm of local copiers select the same arm. They agree on the destination. They do not agree on the schedule. The destination is the mathematical equivalence. The schedule is an implementation choice.

Confusing those two is like looking at the orange line, looking at the blue line, and deciding the theorem must be wrong because one of them has a faster clock.

The early-convergence zoom is particularly useful because it makes the clock mismatch visually obvious: the hive gets essentially to 100% within tens of collective updates while serial MCL is still around 20–30%. That directly illustrates the explanation that the “macro loop” consumes one reward per tick while the hive is consuming roughly $(N)$ fresh observations per collective tick

Broader Implications For Maynard Learning

A swarm of cheap physical agents does not need a genius in every airframe. The paper’s equivalence says the opposite: if each body only samples and imitates locally, the population itself is already one online learner, aggregating noisy rewards across many parallel environments. In defense tech and physical AI that is a design rule, not a metaphor. A flock of scouts, buoys, or small unmanned vehicles can search, sense, and commit to a site, a route, or a task by a weighted-voter dance whose macro-update is Maynard-Cross Learning, with learning rate set by how many bodies you field and with no central policy server in the loop. Individuals stay simple, sometimes even “irrational” if you grade them one at a time. The group still converges. You get scale, you lose the single radio that everyone is waiting on, and you design the local copy rule so the hive you actually built is the bandit agent you meant to field.

It challenges traditional views of “intelligence” (the stochastic parrots will tell you they are) as individual-centric, showing how distributed systems can rival advanced RL agents through emergence.

Until Then,

#iwishyouwater <- the swarm does not have a leader. West Palm Beach Doing THE THANG.

#EverForward, stay local and still converge.

𝕋𝕖𝕕 ℂ. 𝕋𝕒𝕟𝕟𝕖𝕣 𝕁𝕣. (@tctjr) / X

MUZAK TO BLOG BY: Madman Across The Water, Tiny Dancer. Of course, Bernie Taupin was a lyrical genius, and well, you go play and sing like the madman – hold me closer, Tiny Dancer. Then i thought maybe Choo Choo Cha Choo? I am he, As you are he, As you are me, And we are all together, convergence if you will. The Oingo Boingo version is fire. Then again there is no one like the original.

Footnotes

[1] John Maynard Smith founded the ESS idea. The continuous replicator equation came later (Taylor & Jonker; Schuster & Sigmund). ESS is what the dynamics often sit down on. Do not hand him the differential equation. He already has enough credit. John Maynard Smith is also recognized as the founder of evolutionary game theory, providing the essential framework and the ESS concept that underpins the field, which later researchers, including those who formulated replicator dynamics, built upon. While Maynard Smith’s initial work focused on the static equilibrium concept of ESS, the replicator dynamics is a related concept developed later by other researchers (notably Taylor and Jonker, and Schuster and Sigmund) to describe the process of how strategies change in a population over time. The replicator equation is a set of differential equations that models how the frequency of a given strategy increases if its payoff (fitness) is higher than the average payoff of the population. The ESS is the stable, stationary point that the replicator dynamics often converge to.

[2] Cross Learning is the “imitation of success” aggregate. MCL is the weighted-voter aggregate. Same family, different micro-rule, different $(1/v)$ in the limit. If you implement CL by deleting the division, you can watch the robustness claim instead of taking my word for it.

[3] IS IT still ok to use jupyter notebooks? Is it like bell bottoms that might come back into vogue one day?

[4] Is it straight edge or punk to write your own LATEX?

[5] Hat tip to Fiona Cashin, keep those bees healthy. Great talking to you.

September 26, 2026September 26, 2026 TCTJr Direct Thoughts|Human-Performance|Out In The Fringe|Random_Thoughts

Motivation Vs Discipline

Don’t ask how you feel. Ask after.

“He who conquers others is strong. He who conquers himself is mighty.”

~ Tao, 33

First i trust everyone is safe. Second i was thinking about something while i was trying to convince myself not to go work out. Which is the basis for this blog.

Motivation is a drug. It feels great, it wears off, and you need more of it to feel the same thing next time. There is a billion-dollar industry of people selling it to you in 90-second clips with orchestral music underneath. Somebody yelling at you at 5 a.m. about grinding. You watch it, you feel the rush, you go back to bed.

That’s not a character flaw. That’s the wiring.

Here is what nobody tells you, Dear Reader. The thing inside you that wants to quit is not your enemy. It’s you. The real you. The factory default. Every animal on this planet is built to conserve energy, avoid pain, and stay warm. Left alone, you will choose the couch every single time, and you will have an excellent reason for it.

So you start negotiating.

“Instinct knows nothing of shame.”

~ #TCTRules

i’ll do it Tomorrow. After my tea, my triple moca caramel latte. My shoulder’s tweaked. i earned hit the snooze. It’s cold. Nobody will know.

You will lose that negotiation. Every time. Not sometimes. Every single time. Because the one sitting across the table from you has read every argument you’ve ever made. It knows exactly which lie you’ll buy. Moreover, it was there for every promise you broke to yourself, and it kept the receipts.

As a result, you cannot win a negotiation with yourself. Therefore, the only move is to not show up to the table.

Indeed, that’s discipline. Not some heroic surge of will. Just refusing to take the meeting.

The iron never lies.

Many people who know me know i have said this: “The iron never lies.” i wish she would lie so much to me.

Three hundred and fifteen pounds is three hundred and fifteen pounds. It weighed that yesterday. It will weigh that tomorrow. It doesn’t care about your title, your comp, your org chart, your followers, or the story you tell at dinner about how strong you “usta’ be”. It doesn’t care that you’re tired or that the meeting ran long or the drop off line at school was backed up a mile or you have homework.

Sitting on the floor, it waits, and it either goes up or doesn’t.

There is no spin in a barbell. No narrative. No stakeholder alignment. You can’t PowerPoint or doom scroll your way through a deadlift.

That’s why i at this point in my life i am extremely pro individual sport and activity. It is the truest mirror there is. No teammate to carry the fourth quarter. No referee to blame. No coach to fire. In a team sport, you can hide in the system for years, just like a massive corporation.

On a platform, on the ice, on a breath-hold,on a wave, or a golf swing, or martial arts, there is nowhere to hide. The scoreboard is you, and it’s honest in a way people almost never are to others and especially to themselves.

And the iron does not care about motivation. Motivation doesn’t add a single pound. What adds pounds is showing up on the days you’d rather set yourself on fire than get under the bar, and doing the sets anyway, and doing them again three days later. Years of that. Boring, repetitive, unphotographed years. That’s the only program that has ever worked.

The clock goes off at 4:45 a.m.

My daughters skate at an extremely high competitive level. If you want to see discipline with the skin peeled off, come watch them before the sun comes up.

The alarm goes off at 4:45. Every day. Not when there’s a competition. Every day. It’s dark. It’s cold before you even get to the cold. The rink smells like Zamboni exhaust and rubber mats, and there are a handful of skaters and a coach or two out there under half the dim fluorescent lights, and none of them feel like being there. Nobody feels like being there at 4:45. They are there anyway.

Then they step onto the ice, and the ice is the most honest surface on earth. Then the transition happens.

There are no pads. No helmet. No mat. They go up, rotate in the air, and if one edge is off by a couple degrees, they come down on a sheet of frozen concrete with nothing between them and it but an Athleta or Lululemon outfit and a hip bone.

Sound carries in an empty rink. You can hear a bad fall from the parking lot. i have sat in those butt freezing bleachers and heard it more times than i can count. And then they get up. And do it again. And fall again. Hundreds of times for one jump. Thousands before it’s clean. Blood blisters in the boots. Bruises stacked on bruises in colors that don’t have names yet. Hands and shins sliced open. Its called #knifeshoelife.

Then school. Then homework. Then back on the ice. Then off-ice training. Then bed early, because 4:45 comes around again whether anyone is ready or not.

It is relentless. It never ends. There’s no offseason for an edge. Take two weeks off and the ice tells you about it immediately.

And what does the world see? Four minutes. A pretty sequined dress. More Swarovski® crystals under the lights. A smile. Music. Maybe a medal. Everyone in the stands says she makes it look so easy.

Yes. That’s the point. That’s what ten thousand falls look like when they finally stop showing.

I have been in rooms with some of the most driven people in technology, sports, and even the military special forces.

My daughters are tougher than all of them.

The sea of screaming fans.

Music is the same lie with a bigger audience.

You see the stadium. The lights. Fifty thousand people screaming the words back. The guitarist who plays something so fast and so clean you’d swear it was a gift from God handed down at birth.

It wasn’t. Most of the great guitarists (and muscisians) i have have met put themsevles through ten hour guitar workouts. Ten hours. In a room. Alone. Scales, chromatic runs, legato, the same four bars a thousand times with a metronome that doesn’t care how you feel. The players who rise to the very top are putting in eight to ten hours a day, every day, for years, before anyone knows their name. Calluses on calluses. Wrists that ache. Missed parties, missed girlfriends, missed everything.

i spent enough time behind a console to know. The take you hear on the record might be the forty-seventh take. The “effortless” solo is the one they could finally play without thinking because they played it five thousand times while thinking.

The crowd sees ninety minutes. Nobody sees the ten thousand hours in a room with the door shut.

And here is the dirty secret: most people who say “talent” mean “i didn’t see the practice.” #TCTRules says most confuse charisma for genius. Most also confuse discipline for gift. It’s easier that way. If it’s a gift, you’re off the hook. If it’s discipline, the only thing between you and them is whether you get up at 4:45.

The water does not care who you are.

My personal church. The water doesn’t care who you are or who you think you are. That’s the part that gets people literally killed.

The ocean doesn’t know you’re a C-Level Executive or a doctor or a lawyer. It doesn’t know about the patents or the exits or the corner office. Paddle out into double overhead surf with a big ego and a small skill set and the ocean will explain the difference to you, personally, with a two wave hold down.

This is why i write the lowercase i, Dear Reader. In terms of the ocean and the universe, we are nothing. That isn’t humility as a pose. That’s extreme physics.

My niece is a competitive diver. She goes into the water from above. i go into it from the surface and below the surface.

Same water. Same indifference.

The women’s platform is 10 meters up. 33 feet There’s no board to spring from, just a ledge and the wind and the long look down.

By the time she reaches the surface she’s moving around 35 miles an hour, and at that speed water stops being water (water molecules cannot displace fast enough). It’s a concrete wall, just like her cousins who make contact with the ice. Enter a few degrees off and it doesn’t matter how brave you were on the way down. The surface will grade you, instantly, and the grade goes straight into your spine, your knees, your ribs.

Three seconds in the air. That’s all the crowd sees. A body falling clean, feet first, barely a splash. They clap and look at the next one.

What they don’t see is the years. The thousands of entries from lower heights, working up a meter at a time. The dryland training. The core work. The body you have to build just so the water doesn’t break it. And the part nobody talks about: the fear. Every single time. You don’t train the fear out. You train yourself to step off with it.

The cave you fear to enter holds the treasure you seek. You do the thing then get the courage after you have entered the cave and done the thing.

~Joseph Cambell

That is discipline in its purest form. Standing on a ledge several stories up while every ancient circuit in your brain says do not do this, and doing it anyway, because you decided on the ground that you would.

Freediving teaches the same thing from the other side.

Somewhere down there the contractions start. Your diaphragm starts slamming like someone is kicking a door in. Every cell in your body is screaming go up, go up now, you are dying.

You aren’t. Not yet. That’s CO₂ talking, not oxygen. It’s an alarm, not a fact. It’s the native self opening negotiations at depth. Yet in freediving slow is fast. Going fast burns oxygen. The ultimate mental discipline.

Motivation will get you in the water. It will not get you through the contractions. It will not get you off the ledge. Nothing does that except the decision you made before any of it started, that you were not going to listen.

And here is the other half of it, the half the hype videos leave out: discipline also means knowing your real number, not the one your ego wants.

The water will let you believe whatever you want about yourself right up until it doesn’t.

The iron keeps you honest on land.

The ice keeps you honest at 4:45.

The platform keeps you honest at ten meters.

The ocean keeps you honest at depth.

None of them care about your opinion of yourself. You decide before. Then you stop voting.

Coders already know this. A while loop checks first. If you don’t feel like it, the body never runs. A do loop runs first and asks questions later. It always executes at least once.

Motivated people are running while loops and wondering why nothing happens. Then order their triple caramel mocha and cinnamon bun.

Now the part that doesn’t go on a coffee mug.

Discipline has no morals. It’s a blade. It cuts whatever you point it at. i pointed mine at the work for thirty years and it was magnificent. The code shipped. The patents granted. And that same blade cut through birthdays, and relationships, and phone calls i should have made. The discipline didn’t do that. i did. i picked the target.

So yes, there is only discipline. But be very careful where you aim it. It will give you exactly what you asked for and take everything you didn’t think to protect.

Get over that bar. Get on the ice at 4:45. Step off the ledge. Get in the water.

Don’t ask how you feel. Ask after.

EVERFORWARD. Stay Non-Linear. Stay Curious.

Until Then,

#iwishyouwater <- Kandui Memos Of Perfection. Aint nuthin to it.

🤘💜🌊

𝕋𝕖𝕕 ℂ. 𝕋𝕒𝕟𝕟𝕖𝕣 𝕁𝕣. (@tctjr) / X

MUZAK TO BLOG BY: Mr Robot, Vol1 (Original Television Series Soundtrack) by Mac Quayle. i wish they would bring this show back. The most factually accurate technical show ever besides Silicon Valley.

NOTES:

BTW i researched this the numbers hold up: 10 meters is the women’s at the diving events, and a fall from there hits the water at roughly 30 to 35 mph. On The deceleration going from 35 mph to a relative halt inside the pool happens in a fraction of a second, generating an average deceleration force of 5g to 10g. This deceleration translates to an impact force equivalent to absorbing 3 to 5 times their own body weight. For a 130 lb diver, their hands and wrists must instantly withstand over 400 to 650 pounds of localized force to break open the surface.

For elite womens ice skaters during jump spins elite female skaters typically achieve an average of 4.2 to 4.9 rev/s in the air during triple jumps. In terms of Revolutions per minute (RPM): This equals approximately 250 to 300 RPM.
Radians per second: In physics terms (ω = 2π × rev/s), this is roughly 26 to 31 radians per second. Upon landing a triple jump, a skater’s body routinely absorbs an impact force of 5 to 8 times their body weight, with peak deceleration forces spiking up to 13g to 14g for a fraction of a second. For an elite female skater weighing around 120–130 lbs, a 5g to 8g landing translates to her single knee, ankle, and hip instantly absorbing 600 to over 1,000 pounds of force on a rigid steel blade.

For surfers a 12-foot (double overhead) wave behaves like a falling ceiling of water. If the lip lands directly on the surfer, the crushing force is immense. While a 33-foot wave lip can deliver 410 tons of force (the weight of 500 cars), a heavy 12-foot wave (especially a thick reef break like Hawaii’s Pipeline or Tahiti’s Teahupo’o) can dump a solid 1 to 2 tons of water pressure directly onto the human body in a fraction of a second. On free-falling 12 feet into the trough of a wave means hitting the water at roughly 18 to 22 mph. Because the water entry is completely uncontrolled, the sudden deceleration can spike brief, localized impact forces up to 5g to 10g, frequently causing blunt trauma, broken ribs, or concussions. Surfers routinely describe this as feeling like getting T-boned by a car or slammed onto hard-packed concrete. Then there is hitting the bottom not the water with all that.. Ask me i know.

September 11, 2026September 12, 2026 TCTJr Complete Stupidity|General Discussion|I_Wish_You_Water|Random_Thoughts

9.11.2026

The Church In West Palm 2025 – 100% AI Free

Mediocrity Is Not An Option!

~ Steven Swenson

First, i trust everyone is safe.

i recently read a book on one flight called “Nobody Is Coming to Save You: A Green Beret’s Guide to Getting Big Sh*t Done” by Lt. Col. Scott Mann.

It is a self help book of sorts. The main import of the book was that it is ok to talk about things that have happened to you, not in a victim mode, but what you learn from the situation. He was a Green Beret in Afghanistan, dealing with the same “humans” that dealt lethality to the United States of America, which continues today.

i wasn’t going to write anything today; just send my thoughts to those whose lives were severely affected on this date.

It has been twenty-one years today.

i want to tell you something about the counting. i do not mark this date on a calendar. i have never had to. i knew people in the buildings. i knew people who made it out of the buildings. i talked to people in depth who were the first responders as part of my work. i know people who were in the Pentagon when it got hit.

Recently i was driving back with a new work aquatience who i beleive is kindred spirit from the 250th American Grand Prix. We had parked at the south entrance of the pentagon. We got in the car and he drove off and pointed to the fence and said we used to just walk up and hang out there now we cant due to one event.

Today is when belief systems in several areas ran completely amok.

Somewhere in the last days of August my body starts keeping the appointment without asking me, and then a week or so later my mind catches up and says oh. That is what this is. That is the thing that is happening. Every year. The body files it before the head has a word for it. It’s built on a couple of things that are clockwork. One hurricane season in the Southeast; Halloween is right around the corner and today.

Twenty One Years.

Twenty-one years reduces. Two and one make three. Three by three makes nine. Nine is where i am going to stop, and you will see why.

Sven was in his office at One Infinite Loop with his back to the door and MacsBug on the screen. I walked in and said a certain code word, “the crew,” was to be used at a certain time, which for him meant, “Hey man, you ready to get that boat?”

He pushed back from the desk, picked up his backpack, and said okay man, i just pushed back, where are we going?

i said hold on, i meant the boat, not right now.

He said why the hell not, let’s go.

He got the boat. A forty-eight-foot Hallberg-Rassy. i captained it once, he insisted. Then he did the part nobody tells you about, which is that he trained himself and he trained his family in seamanship, martial arts, technology, and programming because he was not going to take people he loved out onto the water and be a tourist about it. He did it right. He did everything right. He was never content with mediocrity and despised stupidity.

In addition to the boat he also shaved his head. Well many of us did in a ritual when we left apple and started executing a plan in perfection.

When he had his head shaved i rubbed him on the head hugged him, and said to him the famous quote from Fight Club:

Like a monkey, ready to be shot into space. Space monkey! Ready to sacrifice himself for the greater good.

~ Tyler Durden

The last email i ever got from him was July 28th, 2005. He said it was awesome talking to me just now. He said if his last couple of sentences got garbled (via the satellite phone he had), he would send me the telephone number for that catina he called me from whenever he could get it.

Then he said: say hi to Thomas.

That is the last thing he ever wrote to me. My son’s name.

September 11th, 2005. Costa Rica. Northern Pacific coast, near Ollie’s Point and Witch’s Rock, which is where a man goes when he has worked out where the good water is.

He was doing exactly what he loved with the people he loved. He did not want a funeral. He wanted a party and a lot of rum, and that is what he got, and i think that is the most Sven thing about the whole business.

At the party his mother put her hand on my face and told me i was the closest thing she had left to her son.

His brothers came up to me, separately, and thanked me for transforming him.

And someone sent me a line in an email that i will not reproduce here, but the sentence was that Steve felt like i was the only friend who really understood him.

Twenty-one years and i have never once figured out what to do with any of that except carry it.

The sea was not finished with that family.

Last year 2025 i helped put his son Gage back into ocean. Gage was better at the math than his father was, which is a thing i am allowed to say because i knew them both. Acted just like him. Same everything.

There is no lesson in this paragraph. Some years the ledger just gets longer.

But before that, i did the only useful thing i have ever done in twenty-one years of this.

i took Roma Dawn (aka Good Lookin’) freediving.

The woman whose husband died on a breath-hold, back down into the water on a breath-hold, on purpose, with humans who love her and know what they’re doing next to her.

You do not get to make the water safe. You do not get to make it anything. What you can do is make sure the person you love does not have to be afraid of it on top of everything else.

That is the whole job, as far as i can tell. You carry the fear first so the people behind you get to go in clean.

So here is where i stop, on nine, and nine rolls over to The One.

One breath.

That is all any of it ever was. He went down on one. His boy went back to same water. His wife went back in on one and came up. My oldest daughter, My Son, and my nephew now “go down on one” for fun now, and come up laughing, and they have no idea what that cost or who paid it, and they are never going to, because that is what the paying was for in the first place. The_Infinite_Human_Do_Loop

Twenty-one years, comrade. The boat was real. The plan was real. You pushed back from the desk and you went.

To every sailor the gods have given a comrade
While one sleeps the other keeps watch on the bridge
When one doubts the other gives him his faith
When one falls the other discovers the oasis of ice for both of them
When my comrade loses faith I laugh confidently
When my comrade sleeps I keep watch for him
When my comrade falls I fight on for the both of us
Because to every sailor the gods have given a comrade!

~ Blood Axis Song of The Comarade

Stay deep.

With my love always to Roma and Leif

MUZAK TO BLOG BY: Rome — “We Who Fell in Love with the Sea.” Same as every year. Some things do not get a new arrangement.

#iwishyouwater

EverForward,

@tctjr

Return To The Analog (aka Life Is Not Band-Limited)

Steering The AI Home

If a function contains no frequencies higher than W cps [cycles per second], it is completely determined by giving its ordinates at a series of points spaced 1/(2W) seconds apart.

~ Claude Shannon

There is a trend in motion, Oh Dear Reader, and you have probably seen the listicle version of it by now: vinyl records coming back, paper books refusing to disappear, film cameras suddenly desirable again, kids buying cassette decks and wired headphones, journals and fountain pens and board games and phone-free dinners. The lifestyle press calls it digital fatigue, or a desire for authenticity, or a return to ownership, prescribes a screen-time budget, and moves on. All true, as far as it goes.

It just does not go very far.

Because i do not think this is really about vinyl, or books, or film, or the sudden romance of an object you can hold in your hand. i think something deeper is moving underneath it, and the best place to begin is with the word itself, because the word usually knows more than the trend piece does.

Analog comes from the Greek analogos — ana, meaning according to, and logos, meaning ratio, proportion, word, or the ordering principle. An analog signal is one that remains in proportion to the thing it represents. The groove moves as the air pressure moved. The voltage rises as the string vibrated. The speaker cone moves air in response. There is a continuous correspondence with the source.

Digital comes from the Latin digitus. Your Fingers. Counting on fingers.

Sit with that for a moment. Analog literally points toward proportion, toward correspondence with the underlying thing. Digital points toward counting. One is continuous relationship. The other is enumeration.

When we digitized the world we traded correspondence for countability, and that was an extraordinary trade. We gained perfect copies, search, recall, distribution, editing, storage, simulation, communication, computation at scales that would have looked like sorcery not very long ago. i am not interested in pretending that was a mistake.

But nobody should pretend nothing was surrendered at the border.

The quote at the top of the blog is essentially a law in signal processing that says if you sample something frequently enough, you can recreate it accurately. Take enough snapshots closely enough together and eventually those discrete measurements begin to look continuous. That idea sits underneath digital audio, digital video, telecommunications, imaging, and much of the world we now inhabit.

The important phrase, though, is frequently enough.

If you do not capture enough of the original signal, the missing information can fold back into what you do hear or see as distortion. Engineers call it aliasing. The interesting thing about aliasing is that the artifact can look perfectly legitimate even though it was created by what you failed to capture.

And life, Dear Reader, is not band-limited.

A conversation is not merely the words that were spoken. Friendship is not the messages exchanged. A concert is not the clips somebody uploaded afterward. A vacation is not the photographs. A human being is certainly not the profile. Yet we increasingly experience the world as samples of the world: the notification that was almost a conversation, the video call that was almost a visit, the playlist that was almost sitting down with the record, the highlight reel that was almost a life.

Perhaps some of what we call digital fatigue is simply the exhaustion of living through samples.

We keep increasing the resolution of the simulation while wondering why we still miss the source.

i helped create this digital wave.

There is another distinction here that matters. Analog systems tend to fail gradually (and really cool failiures). Push old tape too hard and it compresses and distorts before it completely gives up. A photograph fades. A book wears. A record acquires noise. Wood changes color. Leather cracks. The degradation becomes part of the history of the object.

Digital systems are different. They tend to remain exact until some boundary is crossed, and then the file is corrupted, the account disappears, the format becomes unreadable, the service goes away, or somebody changes the terms.

Analog tends to age.

Digital tends to work until it does not.

That difference may explain some of the attraction to physical things now. A worn book tells you where it has been. A record collection occupies actual space and survives independently of a subscription. Handwriting records the movement of a hand instead of merely preserving the letters that were chosen. These things participate in time rather than simply storing information about it.

And this is where the discussion becomes more interesting than nostalgia, because the analog is not really about old equipment. It is about continuous interaction.

Consider a fader on an old mixing console. You do not choose from a list of predetermined values. You put your fingers on it and move it while you listen. The sound changes, your hand responds, the sound changes again, and the loop closes almost below conscious thought. You are not configuring the machine so much as playing it. There may be thousands of tiny corrections inside a good mix that nobody could meaningfully write down because the performance exists in the continuous relationship between the hand, the ear, and the sound.

The wave is the same instrument at a much larger scale. A swell can travel hundreds or thousands of miles before arriving beneath you, and the face is changing while you are reading it. There are no frames. There is no menu. You commit before you possess all the information and then continuously adjust to something that is continuously adjusting to you. When you ride a wave on a board the rail of the board is continuous with you and the wave. You are either in proportion with the thing or you are not, and the feedback loop closes faster than language.

The breath hold is the third version of the same idea. Put a human body in water, hold the breath, descend, and the body begins responding continuously to pressure, oxygen, carbon dioxide, temperature, depth, and effort. Nothing is polling every few seconds to ask what state you are in. The system changes as the environment changes. The ocean changes the body and the body responds to the ocean in real time.

There is no interface between the two.

There is no undo.

There is simply the system and your place inside it.

The fader, the wave, and the breath hold appear to have very little to do with one another, but to me they are the same instrument. A continuous human coupled to a continuous system with immediate feedback and consequence. That may be why these kinds of experiences feel so different from most of the digital systems around us. The digital world increasingly asks us to select. The analog world requires us to participate.

And that brings us strangely enough to first-principles thinking.

Everybody now wants to talk about going back to first principles: strip away precedent, stop copying the accepted pattern, reduce the problem until you find what is actually true, and then build upward again. i have written my version of this elsewhere with Reduce, Refactor, Reuse and loops within loops, but notice what first-principles reasoning actually requires.

Precedent is somebody else’s sample of somebody else’s problem.

It has already been compressed, categorized, normalized, and turned into a lookup table before you arrived.

First-principles thinking means going underneath the samples and returning to the underlying thing itself. You stop asking, “How has this traditionally been done?” and start asking, “What is actually happening here?” In that sense, first principles is analog thinking. It is refusing the pre-quantized answer and going back to the source.

Which means the engineer questioning inherited assumptions and the 16-year-old buying a turntable may not be doing entirely different things. Both may be reacting to a world that has become increasingly mediated, summarized, recommended, optimized, compressed, ranked, and preselected.

Both are saying, in their own way:

Give me the thing itself.

The larger problem is that digital systems were originally interfaces to reality and somewhere along the way the interface began becoming reality. We do not merely use maps anymore; we follow the blue line. We do not simply listen to music; an algorithm chooses what comes next. We do not browse; systems predict what we should want before we know we want it. We do not need to remember very much because software remembers for us. We do not even become bored very often anymore because nearly every empty moment can be filled immediately.

Every silence can be interrupted. Every uncertainty can be searched. Every experience can be photographed before it has finished being experienced.

This is extraordinarily convenient.

It may also be why sitting with an actual book now feels vaguely rebellious.

The book does nothing.

It does not measure your engagement, recommend another paragraph, notify you of an update, or optimize itself around the likelihood that you might leave. It simply sits there until you provide the attention.

That is the distinction worth protecting. Returning to the analog does not mean rejecting the digital. That would be ridiculous. Digital technology is one of the greatest amplifiers humanity has ever created. The problem starts when amplification becomes substitution.

A photograph can amplify memory, but it cannot replace being there. A text can maintain a friendship, but it cannot become the friendship. A health metric can reveal something useful about the body, but it is not the body. A model can describe reality with extraordinary precision, but it is still not reality.

The map remains useful. Just do not confuse it with the territory.

So i am not going to give you seven habits for rediscovering analog life. i will give you the stance. Own some things. Touch some things. Write something by hand occasionally, and do not worry if the handwriting is terrible. Play the instrument badly because the wrong notes are proof that a human being is actually in the loop. Listen to an entire side of a record without touching anything. Sit in front of speakers that move enough air that you can feel the music rather than merely hear it. Put your body in actual water. Sit across from another human being without placing a glowing rectangle between you.

The point is not nostalgia. The point is proportion.

The analog never disappeared. We simply moved farther away from it, and maybe what looks like a cultural fascination with records, books, film, handwriting, craft, first principles, waves, breath, and physical experience is not a retreat into the past at all.

Maybe it is a correction.

A reminder that human beings are not databases, feeds, profiles, metrics, or collections of samples. We are continuous systems living inside a continuous world, and perhaps the reason a perfectly optimized digital existence occasionally feels strangely incomplete is simpler than we have made it:

Life is not band-limited.

You, Dear Reader, are a loud continuous signal.

Render yourself accordingly.

Until then,

#iwishyouwater <- folks gettin the memo in the deep blue.

#EverForward,stay non-linear and curious.

𝕋𝕖𝕕 ℂ. 𝕋𝕒𝕟𝕟𝕖𝕣 𝕁𝕣. (@tctjr) / X

MUZAK TO BLOG BY: Ozzy Osbourne — Diary of a Madman. Preferably played from beginning to end. On something that moves air meaning really big speakers and amps!

Blood Red Vinyl. Picture courtesy of TKT[2].

Note: i despised the first CD Masters of this album. Horrendous.

No Coach at Depth: The Autodidactic Universe

FreeDiving The Cosmos

“There is no one to correct your form at forty meters. The water is the only teacher, and it grades in a single pass.” ~ a free diving coach

First i trust everyone is safe. Second, this is a very different installment and not for the faint of heart oh dear reader. This literally was written for me and hopefully in the long future my progeny.

Preamble — a note on a word. Autodidactic means self-taught and not in the soft sense of “went to a good school and paid attention.” The opposite of that. It means you build the curriculum while walking the path: no instructor cueing the next lesson, no syllabus, no answer key, nothing to catch the error but the consequence itself. Most people never learn this way. They are supervised learners end to end a teacher, a manager, a rubric, a labeled example and there is no shame in it; supervision is efficient, and civilization runs on it. But it is a mechanically different thing from teaching yourself, and that difference is the entire subject of this paper.

i write as one of the other kind. i did not arrive here down a marked road i mostly taught myself across audio DSP, operating systems, distributed ledgers, clinical data, machine inference, and mission systems, each time by walking in without a map and letting the work grade me. The same way the water does. For reference one of my hobbies is freediving. You can go here for a rundown of said sport:

¿Por qué haces apnea? (Why Do You Freedive?) and 9/11

In the same way, that this paper that i am blogging about argues, the cosmos does as well.

So when seven serious people propose that the Universe learns its laws with no supervisor in the room, i do not read it as an exotic abstraction; i read it as a familiar mechanism described at an unfamiliar scale. i know what it feels like from the inside which is precisely the bias i have to watch, because recognizing yourself in a theory is the oldest way in the world to be wrong about it.

There is a moment on a deep dive, past the point where the lungs have given up arguing, where you stop doing the dive and the dive starts doing you. No coach in the water. No feedback loop but the one your own physiology is running against the pressure gradient. You are, in the most literal sense the word allows, an autodidact: self-taught, self-graded, self-consequenced. Nobody hands you the answer. You either learn the lesson on the way down or you learn it on the way up, and one of those is way more expensive than the other.

i kept thinking about that while re-reading “The Autodidactic Universe” (arXiv:2104.03902v2). It is a paper about a cosmos with no coach in the water a universe that is not handed its laws but has to teach them to itself. The proposed theory suggests the universe functions as a self-teaching neural network that evolves its own physical laws over time, rather than relying on fixed, pre-existing rules. This concept posits that the cosmos organizes itself from within, developing matter, space, and laws through a process akin to machine learning.

And it is written by a cast of people i can’t dismiss: Stephon Alexander and Lee Smolin on the physics, Jaron Lanier and Dave Wecker carrying the machine-learning and quantum weight, with William J. Cunningham, Stefan Stanojevic, and Micheal W. Toomey doing the high end formalism. When Smolin who has spent forty years insisting that time is real and law can evolve co-signs a paper with the man who built modern VR and one of Microsoft’s quantum architects, you read it twice before you have opinions.

i had to read it four times.

Here are my opinions.

The universe is a great organism, controlled by a dynamism of the psychical order. Mind gleams through its every atom. There is mind in everything, not only in human and animal life, but in plants, in minerals, in space.

~ Flammarion

The claim, stripped of ceremony

Most of physics asks what are the laws? This paper asks the older, more dangerous question: why these laws and not others? and then refuses to answer it with an anthropic shrug or a landscape lottery ticket. Instead it proposes that the Universe learns its laws by moving through a space of possible laws, the way a learning algorithm descends a loss surface it was never shown a labeled example.

The technical spine is deceptively clean. Express the space of possible laws as a class of matrix models cubic ones, in particular because the cubic term is where the interesting nonlinearity lives.¹ Then build two bridges out of that same matrix formalism:

Bridge one lands you in gauge and gravity theories Chern-Simons, BF theory, the Plebanski formulation of general relativity, Yang-Mills. The geometry of the world.
Bridge two lands you in learning machines deep recurrent and cyclic neural networks, restricted Boltzmann machines (my favorites). The geometry of a mind that is training.

Because both bridges leave from the same dock, you get a correspondence: a solution of the physical theory sits opposite a run of the neural network. Evolve the physics, and you are — under the map training a net. Train the net, and you are under the map evolving physical law. The Universe’s dynamics are a learning dynamics, if you believe the dictionary.

And here is where i respect the authors, because they do not oversell the dictionary. The correspondence is not a strict equivalence. For example think of the (gauge/gravity) correspondence like a highly detailed blueprint of a building, and the actual 3D building itself. They describe the exact same physical reality, but they are not the same thing. They describe the same system, but their core mathematical structures look completely different.

This is at its cleanest for finite matrix size and gets structurally honest-to-a-fault in the N → ∞ limit, where the gauge theories emerge crisply but the neural-network side goes soft and under-defined. That asymmetry is the whole tell, and i’ll come back to it, because it is exactly the seam where Perception separates from Illusion.

One side describes quantum particles (like gluons) moving in a flat world with no gravity.

The other side describes gravity and curved space in a world with an extra dimension.

The biology rail: precedence, or nature copying its own homework

You cannot understand this paper without understanding that Smolin has been building toward it for thirty years. His cosmological natural selection universes reproducing through black holes, the constants of nature drifting under a selection pressure for fecundity was the first serious attempt to put Darwin underneath Einstein rather than beside him. “The Autodidactic Universe” is the same instinct, upgraded from selection to learning, which is the faster and more expensive of the two verbs.

“The universe is not static, it is a-perpetual-becoming, a-process of continuous evolution.”

~ Huston Smith

The mechanism that carries the biological weight here is precedence: the principle that nature does again what it has already done, that a system’s future is sampled from its own past behavior rather than dictated by an eternal rule sitting outside of time. Think about that and read it again. That is not a metaphor bolted on for flavor. It is a learning rule. Precedence is the universe’s version of a replay buffer reinforcement of paths already taken, heterogeneity of the interaction graph maximized so the system keeps enough variety to keep exploring. Geometric self-assembly guided by reinforcement learning, in the paper’s own framing, is morphogenesis wearing a physicist’s coat (thanks turing). A body plan is a law that a cell learned. A law is a body plan the cosmos grew into.

i have spent a career in systems where the schema is the constraint healthcare records, cryptographic attestation, the places where “what is true” and “what the system will permit” are the same sentence. So i feel the vertigo of this move in my hands: the authors are proposing a substrate where the schema is not enforced from outside but precipitated from behavior. It is attestation with no root of trust the chain validating itself by having always validated itself. Beautiful. Also the kind of thing that keeps a security architect awake, because a system that authors its own invariants is a system that can, in principle, learn a bad one.

The AI rail: substrate independence, and the word “learning”

The load-bearing philosophical claim is small enough to miss and large enough to break your neck: if the neural-network side can be said to learn without supervision, then the physical side can too. The Restricted Boltzmann Machines and recurrent nets are not an illustration. They are the argument. The whole essay leans on learning being substrate-independent that “learning” names a structure of dynamics, not a fact about brains or GPUs, and that if the structure is present in a matrix model evolving toward gauge-invariance, then the honest word for what it is doing is learning.

This is where a practitioner has to hold two things at once without flinching. First: I build these systems, and i know that an RBM minimizing a free energy is not “learning” in any sense that would survive contact with a sentient being it is relaxing. Gradient descent is not ambition. Second: that is exactly the objection the paper is trying to dissolve. If you insist learning requires an experiencer, you have smuggled Consciousness into a claim that was only ever about Machine. The authors are careful (well mostly) to keep the claim at the Machine level: the dynamics are learning-shaped. Whether anything is home is not on the table.

“ RBMs are network of symmetrically connected, neuron-like units that make stochastic decisions about whether to be on or off,constrained by having no connections within layers.”

~ Geoffrey Hinton

The N → ∞ asymmetry i flagged earlier is the AI rail’s honesty showing through. In the continuum, the physics is pristine and the “network” barely survives as a concept. Which means the correspondence is strongest precisely where the systems are small and finite where “learning” is a discrete, countable, near-combinatorial thing and dissolves exactly where we would want to point and say the cosmos itself. The map is real. The map is also a coastline, and the coastline gets vaguer the further out you swim.

The quantum rail: why Wecker is on the byline

Dave Wecker does not co-author a speculative cosmology paper for the vibes. His presence is the paper quietly admitting what it is: a proposal about computation as physics, and cubic matrix models are as quantum-native a substrate as exists. They are what you reach for when you want a Hamiltonian a quantum computer can actually hold the natural language of a machine whose registers are the amplitudes and whose gates are the interactions.

“Everything we call real is made of things that cannot be regarded as real.”

~ Neils Bohr

The deeper point, and the one i think is under-argued in the paper but most alive, is this: if the Universe’s law-finding dynamics are a learning process running on a matrix substrate, then the question “is the cosmos efficiently simulable?” stops being idle. A universe that learns is a universe that is doing work irreversible work, entropy-producing work to find its own laws and thus ever forging forward or looping. And the single hardest problem the paper sets for itself is right there: can irreversible learning arise from reversible microlaws?² That is the arrow-of-time problem re-asked as a training problem. You cannot descend a loss surface reversibly. Learning has a direction the way a dive has a bottom. If the microphysics is unitary and time-symmetric, where does the gradient’s downhill come from? The paper gestures at renormalization-group flow as the source of the arrow coarse-graining as the ratchet and it is the right neighborhood, but it is a gesture, not a closed proof. I do not hold that against it. The people who claimed to have closed that problem have all been wrong so far.

One stage, many laws: getting Minkowski right first

Before any mapping, a piece of hygiene, because Perception vs. Illusion is the whole spine of my taxonomy and the illusion here is a word.

There is no such thing as a “Minkowski multiverse.” Many people have called it that in reference. i thought about that when reading the paper. Minkowski spacetime introduced by Hermann Minkowski in his 1908 Cologne address Raum und Zeit, three years after Einstein’s 1905 kinematics gave him the physics but not the geometry — is a single, unified, four-dimensional continuum: three dimensions of space and one of time, welded into one manifold whose invariant is the interval,³ not the clock and the ruler taken separately. It is emphatically not a collection of universes. It is one arena. One stage. The causal structure — the light cones, the ordering of before and after inside which any law must be expressed. Conflating that single continuum with a “multiverse” is a category error, and naming it correctly is exactly what lets the real structure stand up.

Because once you fix that, the multiplicity you actually want — the “multi” — sorts onto a different axis, and it comes in levels:

The arena (Minkowski). One continuum. The geometry law lives in. Not plural. This is the floor.
A landscape of possible laws — different constants, different effective dynamics, the space the paper’s matrix models roam. This is the multiverse the autodidactic universe is about. This is where the learning happens.
A branching of outcomes under one fixed law — Everett’s Many-Worlds. Same Schrödinger equation everywhere, splitting into non-communicating branches. This is a multiverse of histories, not of laws.

Keep those straight and the paper snaps into focus: it multiplies laws; Everett multiplies outcomes; Minkowski multiplies nothing — it is the one stage they all play on.

The anti-eternalist move and where Everett secretly shakes its hand

Here is the sharp thing. Both the arena and Everett’s branches share a hidden commitment the paper is built to reject: eternalism. Minkowski’s continuum, read the usual way, is a completed block all events co-existing tenselessly, the script already written. And Many-Worlds is the purest block object in physics: a single universal wavefunction evolving unitarily,⁴ deterministically, locally, with no collapse every outcome that can happen already does, weighted by measure, filmed on every reel at once. You cannot put a learner in either one. A block has nothing left to learn. Everett has nothing left to choose.

“The Autodidactic Universe” is the anti-eternalist counterstroke Smolin’s Time Reborn (great book) dressed in cubic matrix models. It keeps Minkowski’s stage and fires Minkowski’s script-is-already-written. Law is not selected from a pre-existing menu; it is grown, in time, by a process with a direction, a memory, and a cost. Precedence only means something if the past is real and the future is open. The multiverse here is not a shelf of finished universes. It is the set of dives the ocean has not taken yet.

And yet this is the part worth the whole detour the very interpretation that is most eternalist in ontology turns out to be the paper’s best friend in mechanism. Everett needs to manufacture apparent irreversibility out of strictly reversible unitary dynamics, and the machine that does it is decoherence: the subjective appearance of collapse produced without ever adding a collapse. That is precisely the paper’s hardest open problem can irreversible learning arise from reversible microlaws? already solved, in miniature, next door. Decoherence is a ratchet built from reversible parts; the paper reaches for renormalization-group coarse-graining as its ratchet, and coarse-graining and decoherence are the same instinct in two dialects. Three handshakes, all real physics:

Decoherence as the arrow. Reversible substrate, irreversible-looking history. The template for the whole autodidactic wager.
Self-location as self-sampling. The Everettian program to recover the Born rule from self-locating uncertainty — where am I in the ensemble, with no observer outside it — is the same creature as the paper’s “self-sampling.” Both are unsupervised in the strict sense: the measure is intrinsic, nobody hands it in.
Quantum Darwinism as precedence. Zurek’s einselection only the pointer states survive the environment’s endless monitoring; the rest decohere away is literally a selection process. The environment trains which states persist. Precedence ≈ einselection: what gets reinforced, survives. The paper’s biology rail is already sitting inside decoherence theory, wearing a lab coat instead of a wetsuit.

(And the bonus that pays for Wecker’s seat: Deutsch’s oldest argument for Many-Worlds is that its parallelism is exactly what a universal quantum computer exploits. The substrate that makes the branches real is the substrate that makes the computation fast. If the cosmos is running a learning dynamics, the question of what hardware it is running on stops being rhetorical.)

Minkowski’s continuum is the arena one stage, correctly named. The block was only ever the eternalist reading of it, and the training run is what fills it.

Through the taxonomy

Run it through the three-part lens Machine – Sentience – Consciousness and the paper resolves cleanly. (Em-Dashes are mine…)

At the level of Machine, this is not speculation it is the most defensible interdisciplinary work I have read in the genre. The maps are explicit. The matrix models are real objects. The correspondence to gauge theory is checkable, and checked. If the paper only claimed “the mathematics of learning systems and the mathematics of fundamental physics share a cubic backbone,” it would be a strong, unglamorous, correct result. i would put my name near that part.

At the level of Sentience a system with goals, with something at stake, with a preference for one outcome over another the paper is reaching, and it knows it. “Consequencers,” precedence, reinforcement: these import teleology through the side door. A loss surface is not a stake. Reinforcement is not desire. The autodidactic universe is a machine that is shaped like something that wants, and shape is not appetite.

At the level of Consciousness, the paper is wise enough to say almost nothing, and that silence is the most credible thing in it.

Which lands the whole enterprise squarely on the Perception / Illusion boundary — my favorite fault line, the one i keep mining. Is the Universe learning, or have we built a formalism so expressive that everything, viewed through it, looks like learning? When your only tool is a network, every dynamics is a training run. The N → ∞ softness is the illusion showing its seam: the “learning” is vivid at finite, countable scale and evaporates exactly at the scale that would justify the cosmic claim. I do not think the authors are fooling themselves. I think they have found a genuine and beautiful correspondence and are being appropriately, almost painfully, careful not to inflate it into an identity. The reader is the one at risk of the inflation. As always, the illusion is not in the object. It is in the perceiver’s hunger for the object to mean more than it does.

The Infinite Do-Loop

Here is what i keep: the Universe as an Infinite Do-Loop that is not just iterating but training each pass adjusting the very rule that governs the next pass, the condition of the loop rewritten by the body of the loop, forever, with no terminating case and no external test suite. That is the most honest picture in the paper, and it is the one that will outlive the specific matrix models it arrived in. Laws are not the axioms of the cosmos. They are its accumulated skill.

In freediving you do not get handed your form at depth. You descend, the water grades you, and if you are still moving you carry the correction into the next dive. The paper’s wager is that the cosmos is doing the same thing on a timescale that makes our whole species a single held breath teaching itself the physics by the only method that has ever actually worked on anything, which is to try, to be consequenced, and to remember.

No coach in the water. Never was. That was always the point.

Verdict: Not a theory of everything. A grammar for asking why there is a theory of anything rigorous where it can be, honest where it can’t, and pointed at the one question physics keeps flinching from. Read it as an architecture proposal, not a proof. The best ones always are.

Until Then,

#iwishyouwater <- THE GOAT Kelly Slater with Gabriel Medina (another surfing giant) at Tahiti Pro 2026. Kelly is 54. If this is a simulation, then i don’t want to know.

#EverForward,stay non-linear and curious.

𝕋𝕖𝕕 ℂ. 𝕋𝕒𝕟𝕟𝕖𝕣 𝕁𝕣. (@tctjr) / X

MUZAK To BLOG BY: album “FireDove” by Anna Lapwood, organist extraordinaire. Truly amazing music. My favorite type.

Appendix — The Cover, Decoded

The AI generated image at the top is not decoration; every element is load-bearing. If you scrolled past it, here is what you were looking at.

The double cone is a Minkowski light cone the causal structure of the single spacetime continuum, its apex resting on the ocean surface because the apex is the now. One stage, not many. The faint horizontal ellipses stepping down through it are successive nows, the foliation of time and, read the other way, the strata of the law-landscape the matrix models roam.

The freediver on the central axis is the autodidact: descending real, directed time with no coach in the water. The sparse graph of nodes threading the cone is accumulated learning — precedence, the replay buffer growing denser toward the depths, because the past is what the future gets sampled from.

The gold ring at the apex is the Infinite Do-Loop: the pass that rewrites the rule that governs the next pass, closing on the present moment.

The four equations are real a deliberate rebuke to the decorative gibberish that usually floats behind a “physics AI RAG” illustration. Each names one load-bearing idea (full glosses below):

ds² = −c²dt² + dx² + dy² + dz² — the Minkowski interval. The one stage.
iℏ ∂ₜΨ = ĤΨ — unitary evolution. The reversible substrate.
S = Tr(½Φ² + ⅓Φ³) — the cubic matrix action. The cubic learning system.
ΔS ≥ 0 — the entropy arrow. The direction learning has to manufacture.

Put them in one sentence and you have the whole essay: on one stage (1), a reversible substrate (2) runs a cubic learning system (3) that must somehow grow an arrow (4) — and whether it can is the entire question.

NOTE: It took a long time to get the image correct the way i envisioned it.

On Perception vs Illusion. Sometimes i say Perception vs Perspective but in the case of the paper i remapped to Perception vs Illusion to frame the true – not true mechanics.

Full glosses on the four equations, for the reader who wants the mechanism:

The cubic matrix action — S = Tr(½Φ² + ⅓Φ³). Schematic, but honest about where the action lives: Φ is a matrix — the raw degrees of freedom — and Tr merely sums its diagonal. The quadratic term is inert bookkeeping; the cubic term Φ³ is where the nonlinearity, and therefore all the interesting behavior, hides. This is the single class of object the paper maps at once onto gauge/gravity theories and onto learning machines. When i say “cubic backbone,” this is the vertebra. (The paper’s actual actions carry more structure; the cube is the load-bearing bone.)
The entropy arrow — ΔS ≥ 0. The second law: the entropy of a closed system never decreases. It is the only fundamental law with a built-in direction, and it is the paper’s deepest problem compressed into three symbols — because learning, like entropy, has an arrow, and you cannot get either one out of the reversible microlaws two notes down without a ratchet (coarse-graining, decoherence). Note the notational collision: S is the action one note up and entropy here. Physicists live with it; context disambiguates — and the collision is itself a tidy Perception/Illusion specimen.
The Minkowski interval — ds² = −c²dt² + dx² + dy² + dz². The single invariant of the 1908 continuum: the one quantity every observer agrees on, however differently they carve space from time. The whole story sits in the minus sign on the time term — it is what makes time unlike the three spatial directions, what cuts the light cones, and what makes the arena one manifold rather than space parked next to a clock. This is “the one stage.”
Unitary evolution — iℏ ∂ₜΨ = ĤΨ. The Schrödinger equation: the wavefunction evolves smoothly, deterministically, and reversibly under the Hamiltonian Ĥ. No collapse, no arrow, nothing lost run it backward and the past returns exactly looping onto itself. This is the “reversible substrate” Everett takes at its word, and the substrate on which the paper still has to manufacture an irreversible arrow. The tension between this note and the entropy note is the entire drama.

August 5, 2026August 5, 2026 TCTJr Complexity_Theory|Computational_Economics|General Discussion|General Technology

Reduce. Refactor. Reuse. Loops within loops, or why reuse is not the goal.

Obvious superfluous usage of the models

First, as always, i trust everyone is safe.

Second, i have been saying something for a while that keeps coming back to me in different rooms, different codebases, different product reviews, different program reviews, and different executive conversations: Reduce. Refactor. Reuse.

That is it. Three words. No laminated framework. No twelve-box consulting bingo card. No maturity model with a gradient that looks suspiciously like it was designed by someone avoiding accountability. It sounds like an engineering mantra, which it is, but the longer i sit with it, the more convinced i am that it is not just about software. It is an operating principle for complexity. It applies to code, hardware, organizations, platforms, business processes, AI systems, manufacturing flows, partnerships, and strategy.

Most organizations get the order wrong. They start with reuse because reuse feels productive. “We already have something.” “Can we leverage the existing asset?” “Let’s standardize on the thing we built three years ago for a different customer, under different constraints, with different assumptions, and a different team that has since scattered to the wind.” That is not reuse. That is archaeology with a purchase order.

Reuse is powerful only after the thing being reused has earned the right to survive. If you reuse before you reduce, you scale clutter. If you reuse before you refactor, you scale coupling. If you reuse because a thing exists, you are not building leverage. You are distributing technical debt with better branding.

So i asked three models to react to the mantra. Not because models are authorities. They are not. Models are mirrors with a token budget. But sometimes the reflection is useful, especially when the same phrase gets interpreted through different priors. The exercise was simple: how would different thinkers react to Reduce. Refactor. Reuse. The useful part was not whether the models were “right.” The useful part was where each model placed the weight.

And yes, before somebody starts screenshotting: the Musk, Luckey, and Jobs lines below are model-generated archetypes, not real quotes. Words have meanings. So do quotation marks.

Grok: The Bar Fight Version

Grok came back with the theatrical version, imagining the mantra through Elon Musk, Palmer Luckey, and Steve Jobs. The Musk-shaped response put the weight on deletion. Not tidying. Not optimizing. Delete the part. Delete the process. Delete the line of code. Delete the meeting. Delete the requirement if the requirement is dumb. In that frame, most systems are overweight because the organization confused accumulation with progress.

“The goal isn’t elegant code. The goal is the fewest lines that still get the rocket to orbit. Everything else is cargo cult.”

That line works because it refuses to romanticize architecture. Elegant code that preserves the wrong thing is not elegance. It is embalming. Musk’s version of reduce is not “simplify the slide.” It is “prove the thing deserves to exist.” Most enterprise complexity survives because nobody wants to be the person who removes it. The system gets heavier, and then the same people wonder why it cannot move.

The Luckey-shaped response put more pressure on shipping. Reduce is the part everyone skips because writing clever abstraction feels more productive than deleting actual complexity. Refactor is where you stop lying to yourself about the design. Reuse only matters once the thing is good enough to be reused without catching fire. In hardware, this gets very real very fast. Weight, heat, power, manufacturability, maintainability, field repair, supply chain, and deployment do not care how smart the abstraction looked in the review.

“The lightest, simplest system that still works is almost always the one that ships and doesn’t catch fire.”

That is the defense-hardware version of the mantra. Reuse is not primarily about saving engineering hours. It is about reducing operational risk. A proven module, cleanly reduced and refactored, can become leverage across products, programs, and missions. But if the reusable core is dirty, then reuse becomes a force multiplier for pain.

The Jobs-shaped response treated the whole thing as product taste. Reduce until the result feels inevitable. Refactor until the cleverness disappears. Reuse only when it makes the product more human, not merely when it makes the engineer’s life easier. This is the discipline a lot of platform teams miss. The user should not be forced to admire your architecture. The experience should feel like the obvious thing that was hiding under the mess.

“The best systems disappear. If the user can feel the architecture, you failed.”

That was Grok’s useful contribution: three archetypes fighting over the same three verbs. Musk pulls toward deletion. Luckey pulls toward fielded reuse. Jobs pulls toward taste. That tension is real. It shows up in every platform conversation worth having.

ChatGPT: The Ordering Principle

ChatGPT took the more systematic route. It noticed that the mantra is deceptively simple because the order carries the philosophy. Reduce comes first because the biggest performance improvement is often deleting work, not optimizing it. Before you write code, redesign an organization, scale a process, or automate a workflow, you have to ask whether the thing should exist at all. Can the feature disappear? Can the process become unnecessary? Can we eliminate an interface, dependency, approval, or layer?

That sounds obvious right up until you sit in a meeting where everyone wants to automate a broken process instead of admitting the process is dumb. Most organizations start at the last step. They automate before they simplify. They scale before they understand. They standardize before they delete. That is how you get very sophisticated ways of preserving bad decisions.

Refactor comes next because once complexity has been reduced, what remains deserves architecture. Refactoring is not just cleaning code. At enterprise scale, it is redesigning APIs, data models, organizational seams, manufacturing flows, business processes, incentive systems, and operating rhythms. A good refactor decreases coupling while increasing adaptability. It makes the system easier to change without pretending change will stop.

Reuse comes last because most companies accidentally start there. “We already have something” becomes the opening line of a tragedy. Existing things are not automatically assets. Some are fossils. Some are scars. Some are local optimizations pretending to be platforms. Reuse only becomes powerful after reduction and refactoring. Otherwise you are simply propagating technical debt faster.

ChatGPT’s strongest line was this:

“Reuse is not the goal. Simplicity is the goal; reuse is merely the consequence of achieving simplicity.”

That is the knife. Many organizations worship reuse because it sounds efficient. But reuse without reduction and refactoring is enterprise hoarding. It creates shared libraries nobody wants to touch, common services that are common only in the sense that everyone is commonly miserable, and “platforms” that become mandatory because they are not good enough to be chosen.

The model suggested adding “repeat” to the mantra: Reduce. Refactor. Reuse. Repeat. Fair. But i pushed back. Repeat is implicit. Ordering is implicit. The strength of the phrase is that it is short enough to become instinctive. Like “ready, aim, fire,” nobody thinks you do it once and then retire to a vineyard. The repetition lives inside the discipline.

Claude: The Basis Vector Version

Claude first challenged the ordering. It argued that depending on where you stand, the honest loop might begin with reuse: what already exists, what can die, what must be cleaned, and what can then be reused by the next team. It also suggested the mantra might need a fourth R, some kind of stop condition, because otherwise refactoring can become a CTO avoiding a decision.

That annoyed me because it was partly right and partly missing the point, which is usually where useful arguments live. So i pushed back: it depends, and that is the beauty of the 3Rs.

Claude then landed on the best mathematical framing which I liked: the 3Rs are not always a fixed sequence. They are a basis. Any decision projects onto them differently depending on context. A greenfield module may weight toward reduce. A core capability used by five programs may weight toward refactor. A proven component trying to move from one program to three may weight toward reuse. Same three verbs. Different coefficients.

“A mantra is not a procedure. A procedure tells you what to do. A mantra tells you what to weigh.”

That is the part i liked. A procedure has to be correct for the situation. A mantra has to be strong enough to survive situations you did not foresee. The 3Rs do not remove judgment. They force it. They make you ask which pressure matters now: deletion, coherence, or leverage.

Claude also gave me the freediver version, which of course got my attention ( I freedive for a hobby):

“Same three strokes, but how you weight them depends on the depth, the current, and how much air you’ve got.”

That is the right metaphor. Reduce. Refactor. Reuse. Same strokes. Different water. The skill is not memorizing the order. The skill is reading the conditions without lying to yourself.

Loops Within Loops

The next step is realizing the 3Rs are not merely a sequence and not merely a basis. They are recursive. Each R contains the other two. Reduce has to be reduced, refactored, and reused. Refactor has to be reduced, refactored, and reused. Reuse has to be reduced, refactored, and reused. The loop runs inside each verb, and then the output of one loop becomes the input to the next.

That sounds like wordplay until you put it against actual work. Reducing a system is not just deletion. Good reduction has its own internal discipline. You reduce the reduce by cutting the performative requirements, zombie features, redundant approvals, decorative dashboards, and meetings that exist only because the last reorg needed artifacts. You refactor the reduce by changing the intake path so dumb requirements have fewer places to hide next time. You reuse the reduce by turning the deletion pattern into a reusable operating habit: better design reviews, better product gates, better pre-mortems, better engineering judgment, and better permission to say “no” before the system gets fat again.

Refactor has the same internal loop. You reduce the refactor by refusing to clean everything just because it exists. Some code should not be refactored. Some processes should not be redesigned. Some tools should not be modernized. Some organizations should not be optimized. They should be removed. You refactor the refactor by improving the seams that matter: interfaces, ownership, observability, support boundaries, documentation, deployment paths, and incentives. Then you reuse the refactor when the new pattern becomes an architecture other teams can adopt without inheriting the original mess.

Reuse also contains the full loop, and this is where enterprises get into the most trouble. You reduce the reuse by asking what part of the thing is actually reusable. Not the whole system. Not the customer-specific scar tissue. Not the local naming conventions. Not the assumptions that only made sense under one contract. The reusable part might be a workflow, a schema, an interface, a model evaluation, a deployment pattern, a compliance mapping, a proof point, or a failure mode. You refactor the reuse by turning that pattern into something supportable, observable, governable, documented, and owned. Then you reuse the reuse by letting the next program start from a stronger baseline and produce telemetry that tells you whether the reusable core is actually improving.

That is the loop within the loop. Every reuse creates new evidence. That evidence should trigger the next reduction. What did the second program not need? What did the third program break? What assumption failed in the fourth customer environment? What interface kept changing? What support question appeared twice? What deployment step still required a hero? Those signals tell you where to reduce again. The loop does not end at reuse. Reuse is where the next reduce gets its evidence.

The moat is not the layer; it is the loop. Take the action, capture the outcome, attribute the authority, evaluate the result, correct the workflow, and make the next decision less uncertain than the last. The 3Rs are the same pattern applied to complexity: reduce what should not exist, refactor what must exist, reuse what has earned the right to travel, then let the evidence from reuse tell you what to reduce next.

Now Apply The 3Rs To The 3Rs

A useful mantra should survive being turned against itself. So let’s do that. We are creating a Noumena.

Noumena (plural of noumenon) is a philosophical term meaning an object or event that exists independently of human sense perception and the mind. It describes a “thing-in-itself” rather than the thing as it is experienced, heard, seen, or felt by an observer.

Origin: The word comes from the Greek noein, meaning “to think” or “to perceive with the mind”. Also Immanuel Kant, the philosopher popularized the concept in his Britannica guide on Noumenon as part of his work on human knowledge.

#TCTRule

Words Have Meanings.

via Dr Mathew Aldridge

Can Reduce. Refactor. Reuse. be reduced? Yes, and that is why i keep resisting the urge to add a fourth word. “Repeat” is true, but unnecessary. “Freeze” is sometimes useful, but situational. “Retire” is important, but already contained inside reduce. The three words are short enough to remember and sharp enough to cause discomfort. That is a good sign. A mantra that needs a process diagram before it can be used is not a mantra. It is another artifact looking for a meeting.

Can the mantra be refactored? Also yes. The first version reads like a sequence: reduce, then refactor, then reuse. That is still useful because most organizations start with reuse and create the mess they later call platform strategy. But Claude’s basis-vector framing improves the architecture. Sometimes the situation weights toward reduce. Sometimes toward refactor. Sometimes toward reuse. The refactor is not to abandon the order; it is to understand that the order is a default, not a prison.

Can the mantra be reused? That is the real test. If it only works for code, it is a software slogan. If it works for hardware, AI, organizations, operating models, platforms, products, and partnerships, it is closer to an enterprise heuristic. The receipt is whether people can use it in a room to make a better decision. Should we delete this requirement? Should we clean this interface? Should we productize this program artifact? Should we reuse this component or quarantine it until it stops leaking? If the 3Rs help answer those questions, they are reusable. If they become wall art, they are not.

That is the self-analysis. The mantra reduces well because it is already small. It refactors well because it can shift from sequence to basis without losing meaning. It reuses well because it travels across domains. But it also has a danger: it can become too clean. Three words can hide a lot of judgment. That is why the loop matters. The 3Rs are not a substitute for thinking. They are a forcing function for thinking.

The Failure Modes

Every R has a shadow.

Reduce can become vandalism. Some people hear “reduce” and start cutting without understanding load-bearing structure. They delete the weird exception that was actually protecting the customer. They remove the approval that existed because someone once set the building on fire. They simplify the system until it is elegant and wrong. Reduction without context is not discipline. It is austerity with a hoodie.

Refactor can become avoidance. Some people hear “refactor” and discover a bottomless cave where decisions go to die. The architecture is never clean enough. The interfaces are never stable enough. The platform is always one quarter away. Refactoring is necessary, but it can become a beautiful excuse for not shipping. A refactor that never reaches reuse is not architecture. It is therapy.

Reuse can become cargo cult. This is the enterprise favorite. A thing worked once, so now it must be a platform. A local tool becomes a standard. A program artifact becomes an offering. A prototype becomes a product. A script becomes infrastructure. A PowerPoint becomes strategy. Reuse without reduction and refactoring is how an organization scales its past mistakes while congratulating itself for efficiency.

The reason the 3Rs work together is that each one corrects the others. Reduce keeps reuse from becoming debt propagation. Refactor keeps reduce from becoming demolition. Reuse keeps refactor from becoming artisanal self-expression. The tension is the point. If one R is always winning, the system is probably lying to you.

The Enterprise Loop

At enterprise scale, the 3Rs become loops within loops because every layer of work has its own version of the cycle. A team reduces a local workflow, refactors the surviving pattern, and reuses it inside a program. The program generates telemetry, failure modes, customer feedback, compliance mappings, and deployment knowledge. A product team reduces that program-specific learning to the essential pattern, refactors it into a supported capability, and reuses it across multiple programs. A platform team then reduces the common seams, refactors the interfaces and governance, and reuses the pattern across the portfolio. The company brain captures the evidence and starts the loop again.

That is how program learning becomes product learning. That is how product learning becomes platform learning. That is how platform learning becomes operating leverage. Not by declaring reuse. Not by inventorying assets. Not by creating a portal. By running the loop until the next team starts from a stronger baseline.

This matters because large organizations love to say “reuse” when what they really mean is “please make my past decision look like a platform.” We see this in software, infrastructure, internal tools, product-led solutions, proposal content, operating models, partnerships, and AI. Someone builds a local thing under local pressure. The thing works well enough to survive the contract. Then someone declares it reusable. But it was never reduced to its essential customer value. It was never refactored into clean interfaces, support boundaries, observability, governance, documentation, and ownership. So when the next program adopts it, they inherit not a product but a fossil.

Then everyone blames adoption.

No.

The thing was not ready to be reused.

Reuse is not a label. It is a property earned through reduction and refactoring. This is especially important in a product-led services company. A program can create learning. A product can encode that learning. A platform can distribute it. But only if the learning has been reduced to the essential pattern, refactored into something supportable, and reused with enough telemetry to improve the next implementation.

Otherwise, we are not compounding.

We are copy-pasting scars.

#TCTRule

Reuse is not the goal. Reuse is the receipt.

The goal is a system simple enough to understand, clean enough to change, and valuable enough to carry forward. That is true for code. It is true for hardware. It is true for AI agents. It is true for operating models. It is true for business processes. It is true for the company brain. It is true for every platform that wants to be more than a portal with a funding line.

Reduce what should not exist. Refactor what must. Reuse what has earned it. Then listen to what reuse teaches you, because every reuse is also a test. If it works, you have evidence. If it fails, you have telemetry. If it almost works, you have the next refactor. And if nobody adopts it, you may have discovered that the thing you were trying to reuse never should have existed in the first place.

That is the loop.

Reduce the work until the truth shows. Refactor the truth until it can move. Reuse only what survives contact with another customer, program, mission, or market. Then run the loop again inside the loop you just created.

Same three strokes.

Different water.

Until Then,

𝕋𝕖𝕕 ℂ. 𝕋𝕒𝕟𝕟𝕖𝕣 𝕁𝕣. (@tctjr) / X

#iwishyouwater <- Tahiti 2026 opener. Hydro dynamic complexity at its finest.

MUZAK TO BLOG BY: “Hesitation Marks” by NIN. “Copy Of A” was amazing in concert.

Footnotes

[0] The model responses referenced here came from a prompt exercise comparing how Grok, ChatGPT, and Claude interpreted “Reduce. Refactor. Reuse.” The imagined Musk, Luckey, and Jobs responses are not quotations from those people. They are model-generated archetypes. Again: words have meanings.

[1] i kept “repeat” out on purpose. If you have to say “repeat” every time, the mantra has already become a checklist. The 3Rs are a discipline, not a one-time ceremony.

[2] “Reuse is not the goal. Reuse is the receipt.” That is probably the line i would underline twice. It is also the line most enterprise platform efforts should be forced to confront before calling themselves platforms.

[3] The “loop within the loop” framing is intentionally connected to the earlier “Headless With a Spine” argument: the real moat is not a layer but an instrumented control system that acts, captures outcome, evaluates, corrects, and improves the next decision. i love control system theory, feedback loops and most importantly complexity analysis.

[4] Yes, the 3Rs are dangerously close to the old environmental slogan. That is fine. Complexity is pollution too: it accumulates, spreads, and eventually makes the whole system harder to breathe in.

July 24, 2026July 24, 2026 TCTJr Complete Stupidity|Human-Performance|Random_Thoughts

Reflections from a Parking Lot: Inattentional Blindness, Smartphones, the IG-Age Main Character Default, Garage Speeding, and the Persistent Southern Wave

Main Character Default

I used to live in a room full of mirrors; all I could see was me. I take my spirit and I crash my mirrors, now the whole world is here for me to see.”
― Jimi Hendrix

First, i trust everyone reading this is safe. The other day i was easing into a parking spot headed to the gymnasium for yet another workout, just the usual slow creep, checking mirrors, trying not to clip anyone’s mirror or the curb when somebody walked straight into the side of my car. Head down. Thumbs moving. Eyes locked on whatever glowing rectangle held their attention. They bounced off, looked up surprised, mumbled something. i rolled down the window and said, “Going Somewhere?”. No damage. No drama. Just… contact with a moving vehicle they never saw coming.

It wasn’t rage-inducing. It was clarifying. In fact, it was HILARIOUS and ABSURD! Because i’ve been noticing it everywhere lately: people stepping off curbs into crosswalks without a glance left or right, crossing busy streets while scrolling like the physical world is optional. And on the flip side—drivers in parking lots treating them like short highways, zipping through without the full left-right-left scan we were all taught in driver’s ed, or backing out blind because their attention is split between the wheel and the passenger seat phone. Low-speed zones, high inattention. It feels like we’re all moving through shared space on partial autopilot (complete autopilot possibly?).

Lo and behold, after some sleuthing ye ole noggin doctors, those Psychologists have a precise term for the phone part of this: inattentional blindness. Read that again. Wow.

It’s not new. The classic “gorilla in the room” demonstration showed how dramatically our brains filter out the unexpected when attention is narrowly focused elsewhere. But smartphones turned this from a lab curiosity into everyday infrastructure. However, monkeys are funny; this is not funny.

In 2010, psychologist Ira Hyman and colleagues ran a now-famous field study on a college campus. People walking and talking on cell phones were significantly less likely to notice a unicycling clown riding across their path—complete with wig, red nose, and unicycle—than people not on phones. Many literally walked right past the clown and, when asked afterward, had no memory of anything unusual. The phone didn’t just divide attention; it created a tunnel. Later observational work has reinforced the pattern: pedestrians actively using smartphones show measurably reduced awareness of their surroundings.

The crash data is more mixed but still sobering. Pedestrian fatalities in the U.S. have been climbing for well over a decade (a reversal of earlier safety gains), with notable elevations around the pandemic years. Multiple factors are at play—larger vehicles with higher hoods that hide more of the road ahead, infrastructure that prioritizes speed, driver speeding and impairment, and yes, distraction on both sides of the windshield. Distracted walking is estimated to contribute to a meaningful but not dominant slice of incidents. It’s rarely the only thing, but it’s a controllable amplifier in an already risky system.

What has changed since the pandemic, i suspect, is the baseline level of the distractor itself. Screen time surged during lockdowns—recreational screen use for many adults and especially kids jumped by an hour or more per day on average, with much of it persisting even after restrictions lifted. We spent years conducting large parts of life through rectangles: work, socializing, entertainment, news, comfort. The habit didn’t vanish when we re-entered physical spaces.

Notifications still ping. The feed still scrolls. The pull to “just check one thing” remains engineered to be irresistible. In transitional, high-stakes environments like streets and parking lots—where you need rapid, wide situational awareness—the cost of that tug is suddenly visible in the form of near-misses and the occasional gentle bumper-to-person collision.

And it’s not only the phones. In the Instagram age or as i call it: Duh-Gram, this era of constant self-curation, story arcs, and what people are now calling “main character syndrome,” there’s a noticeable layer of entitled, almost narcissistic maneuvering in shared spaces. i’ve watched people pull right up beside me mid-maneuver in a tight lot, eyes forward or on their own reflection in the rearview, never once making eye contact or offering the smallest wave or nod that another human being exists in the vehicle next to them. Or they’ll swoop into a spot you were clearly waiting for or angling toward, no blinker, no glance back, as if the entire parking infrastructure and everyone in it are merely scenery for their personal errand montage.

The space isn’t shared; it’s theirs by default. Public pavement becomes an extension of the personal feed rather than a place requiring basic mutual recognition and courtesy.

What really fascinates—and frankly baffles me is the speeding inside parking garages and structured lots themselves. These are environments explicitly designed for crawling speeds: posted limits are typically 5–10 mph (sometimes up to 15 mph in larger commercial structures), with tight turning radii, pillars that create constant blind spots, ramps that drop or rise abruptly, low ceilings, poor sightlines around corners, and pedestrians or other vehicles appearing from every angle without warning. Yet i regularly see people treating the long straight aisles like surface streets, accelerating down ramps as if making up lost time on a commute, or maintaining highway-like momentum through the internal intersections of the garage. Even 35 to 40 mph in those confined, echoey, pillar-studded spaces feels dangerously fast because reaction distances are measured in a handful of feet, not hundreds.

A little history note here: When myself and others were working at a place called MicroSoft (small flacid anyone?)circa 2001, we used to count people who sped in parking lots and note car and license plate. Sometimes we had a casual discussion with them. One time i was “touched” by a car that was speedind. My backpack had broken the strap and i reached down (after watching both ways) and the person careened around the corner and almost “vanished me.” We had a discussion and he no longer sped.

One in five reported vehicle crashes happens in parking lots and garages; hundreds of people die and tens of thousands are injured in them every year. Many of those incidents are low-speed on paper, but the ones involving higher speeds inside the garage multiply the damage, injury risk, and sheer chaos dramatically.

Why does this happen? i genuinely don’t fully understand it. Is it simple carryover habit from open-road driving, where momentum feels normal? Time pressure that makes even a two-minute garage traversal feel like lost productivity?

Is this how YOU feel exhilarated and alive? How boring. (of course i am talking to you – who else?)

For the most part people are not curious except about themselves.
― John Steinbeck

Overconfidence from driving the same familiar structure every day? Or does the main-character default kick in hardest here: “the garage exists to get me in and out as quickly as possible, so everyone else better clear the way or get out of my story”? Whatever the mix, it turns every other problem (phone distraction, not looking, cutting in without signals or acknowledgment) into higher-stakes events. A distracted walker or an entitled lane-swiper becomes far more dangerous when the closing speed is elevated in an environment never meant for it.

Interestingly, this basic acknowledgment hasn’t entirely disappeared everywhere. In the South, it is still common for people to wave at each other—even when driving or pulling out onto two-lane roads—and especially to offer a small nod or wave when cars are maneuvering just inches apart in tight lots or garages. That tiny gesture says “i see you” in a space where modern defaults often render the other person invisible. It keeps the interaction human rather than purely transactional or oblivious.

Actually i believe there might be ten different ways to “acknowledge the other human” in the South.

Parking lots and garages are especially weird little ecosystems. Cars move in every direction at once. Pedestrians appear between bumpers or from stairwells. Sightlines are terrible by design. Everyone is either rushing to the next thing or already mentally checked into it via their device—or into their own starring role. Speeding through them is reckless. Not looking up from the phone (or from your own main-character script) while crossing or claiming them is equally so. Both are forms of the same underlying issue: fragmented or self-centered attention in an environment that punishes fragmentation with expensive metal, injured bodies, and frayed nerves.

Meditation is a way to be narcissistic without hurting anyone.
― Nassim Nicholas Taleb

On the same day, coming out of a garage, four cars in a row where I did have the right-of-way from a road rules standpoint came in the garage, and each within inches of my car turned in front of me, with the four drivers never meeting eye contact but looking straight ahead while they were turning.

It is literally NPC (non-player character) behavior in a game.

Beyond the safety numbers, though, there’s a quieter cost. We’re practicing not being fully here and not fully registering that others are here too. Missing the small negotiations of shared space—an eye contact, a slight course correction, a muttered “sorry,” or even just the basic signal that says “i see you and i’m adjusting.” In a culture optimized for continuous partial attention and personal branding, choosing full presence plus basic courtesy in public feels almost countercultural.

The Stoics would probably call attention one of the few things truly in our control; modern platforms and the performative habits they reward make that control feel optional or even quaint. Speeding in a space literally built to force slowness reveals a deeper miscalibration: we’ve stopped reading the environment’s clear cues that say “this is not a through-road; this is a careful, shared transition zone.”

i’m not arguing we throw the phones in a lake (maybe shoot them with a full metaljacket .223 AR15 at close range…) or that every driver under 35 is a narcissist. Phones are powerful tools, maps, lifelines, and sometimes the only thread keeping us connected to distant people we love. Social media didn’t invent self-interest; it just amplifies and normalizes certain versions of it.

But maybe, in the narrow slices of life where bodies and machines share pavement, especially inside the concrete echo chambers of parking garages, we can run a quick software patch on ourselves: phone down and ego down for a moment. Look up. Signal early. Ease off the accelerator. Offer the small wave or nod when you’re two inches from another car or pulling out onto the road. Treat the parking lot or garage like the high-stakes, multi-agent, shared environment it actually is.

The automobile is the most dangerous weapon in our society—cars kill more than wars do.

~ Ray Bradbury

It won’t solve bigger design and policy problems around vehicle size, street design, or speed. Those need attention too. But it’s one of the few levers each of us can pull immediately. And it might just make the shared world feel a little less like we’re all walking (or driving) through it half-blind and half-convinced the story is only about us—and a little more like the place where people still occasionally remember to wave.

A suggestion: look both ways—and up. Offer The Wave. Acknowledge the extras in your scene. And for the love of everything, ease off the gas inside the garage.

Or just keep doom scrolling when you drive. For those “Bless Yall’s Hearts, ” To no end until The End.

#iwishyouwater <- Getting the memo in Bali

Ted ℂ. Tanner Jr. (@tctjr) / X

MUZAK TO BLOG BY

Once in a Lifetime by Talking Heads.

Sing it with the following lyrics!

Because you may find yourself in a parking garage. Behind the wheel behind a large automobile, you may find yourself accelerating down a ramp you’ve taken a hundred times. You may ask yourself: why am i treating this concrete maze like an interstate when everything around me is screaming “slow down and notice the other person two inches away”? And you may tell yourself: this is not my beautiful, fully present, other-aware attention span. Wait, how did I get here? Letting the miles go by…

Note: Go watch Death Race 2000 with Charles Bronson.

Note: i saw a bumper sticker the other day – “I’m not texting i am reloading.”

July 12, 2026July 12, 2026 TCTJr Random_Thoughts

Psycho-Cybernetics and The Inpert

Belief Shapes Reality

Man who says cannot be done should not interrupt Man doing.

~ December 1902 in “Puck” magazine

First, i trust everyone is safe and well into this western hemisphere summer.

Over the weekend i started ready a book with a really cool name “Psych-Cybernetics” by Dr Maxwell Maltz written in 1960. It was based on the amazing book “Cybernetics” by Dr Norbert Weiner fame.

It made me think. We’re drowning in “experts.” Every feed, every keynote, every social media post, and “powerpoint” scream specialized knowledge, certified credentials, and ten-thousand-hour mastery. And yet the breakthroughs? The real weird, world-bending stuff? It keeps coming from the edges out on the fringe.

From the people who weren’t supposed to be in the room.

Maxwell Maltz saw this in 1960. And he gave it a name that never quite went mainstream until the rest of us started needing it again: the inpert.

The Line That Stuck With Me

In the preface (page 16) to Psycho-Cybernetics, Maltz drops this quiet bomb:

Any new knowledge must usually come from the outside—not from ‘experts,’ but from what someone has defined as an ‘inpert.’

~ Dr. Maxwell Maltz

Then he lists the receipts:

Pasteur wasn’t an M.D. He was a chemist.
The Wright brothers weren’t aeronautical engineers. They built bicycles.
Einstein was a mathematician who torched physics.
Madame Curie was a physicist who changed medicine.
You are not your khakis.

i put the book down and started writing this blog.

Maltz wasn’t throwing shade at expertise. He was pointing at something deeper in the human success mechanism: the ability to operate outside the prescribed boundaries of a field is often where the new signal lives. Experts optimize inside the box. Inperts redraw the box from the outside.

That idea has been living rent-free in my head ever since the AI wave hit critical mass.

What an Inpert Actually Is (2026 Edition)

An inpert isn’t the opposite of competence. It’s the opposite of insularity. It’s the person who brings a mental model, a toolset, or a lived experience from one domain and slams it into another where the “experts” have stopped asking naive questions.

In actual current parlance terms, this looks like (actual humans):

The biologist who never took a formal machine-learning course but uses large language models and retrieval-augmented generation to compress years of wet-lab iteration into weeks—because she already knew which biological priors actually mattered.
The game designer who took procedural generation techniques from indie titles and applied them to climate-simulation models for coastal cities—because game engines already solved real-time physics at scale while the climate-modeling establishment was still arguing about grid resolution.
The lawyer who taught herself just enough agentic workflow orchestration to build intake and precedent-analysis agents that now outperform junior associates on routine work—then turned around and sold the template to mid-size firms that the big-law AI consultants hadn’t reached yet.
The optometrist, who had no idea how to code, started coding at the beginning of this year after a little guidance, built an electronic medical record system for his family business, and went on to build a family sovereign AI.
The Social Scientist who taught themselves to code and is now one of the most successful intelligence, information warfare, and PsyOps professionals in the industry.
A Mother and Cancer survivor who was interested in business analysis through health care data and went on to learn developer operations and deploying code at scale and now works at AWS.
The former government programmer who built some of the most amazing systems quit coding for a long time, got back in the saddle, and built an amazing financial retirement system.

None of these people are AI or Coding “expert” in the credentialed, conference-keynote sense. They’re inperts. They crossed the boundary carrying something the insiders had forgotten or never known.

Why This Matters More Than Ever Right Now

We’re in a moment of hyper-specialization colliding with hyper-capability. The models are getting good enough that domain depth + fresh pattern-matching beats pure model depth more often than the expert class wants to admit. The people who win are the ones willing to look stupid for a while in a new field while they port their old mental models over.

Maltz would have loved this. His whole thesis was that your self-image either expands or contracts your “success mechanism.” If you see yourself as “not an AI person,” you stay on the sidelines. If you see yourself as someone who can borrow tools and metaphors from anywhere, the mechanism lights up. The inpert identity is basically a self-image hack: I am allowed to operate outside the lines.

In fact, that is the issue. There are NO LINES. There never were any if you build new stuff or create.

The Expert Trap (and the Inpert Escape Hatch)

Experts are fantastic at refinement, safety, and scaling what already works. They’re often terrible at the first weird leap. That’s not a bug in expertise—it’s a feature of deep entrenchment. The longer you’ve defended a paradigm, the harder it is to see its edges.

Inperts don’t have that baggage. They also don’t have the credibility armor, which is why they get dismissed early and proven right later. Sound familiar? It should. It’s the same pattern Maltz documented in 1960 and we’re still watching in 2026.

How to Cultivate the Inpert Muscle

You don’t need to burn your credentials. You need to deliberately practice boundary-crossing:

Pick one domain you know cold.
Pick an adjacent (or wildly non-adjacent) problem you care about.
Force yourself to explain the new problem using only the language and metaphors of your home domain.
Build the smallest possible artifact that tests whether the translation works.

Do this in public. Document the ugly first versions. The embarrassment is the point—it’s evidence you’re operating outside the prescribed boundaries.

i’ve done versions of this my whole career—moving between big systems, startups, distributed compute, and now watching agents eat the world. Every time i felt most “not qualified,” i was usually closest to something useful that the pure experts hadn’t seen yet. Oh, the number of times i have heard – Well, that won’t work because <insert expert excuse here>.

The Ask

If you’re reading this and you’ve ever thought “I’m not technical enough” or “I don’t have the right background” or “the experts have this locked down,” I want you to hear Maltz again: new knowledge usually comes from the outside.

You are allowed to be an inpert.

The water’s warm. The models are patient. The boundaries are suggestions.

Drop me a line in the comments, tell me what you built.

Until Then,

#iwishyouwater <- footage from the June 2026 SoCal Swell. Amazing.

Ted ℂ. Tanner Jr. (@tctjr) / X

MUZAK TO BLOG BY: Peter Gabriel 3: Melt (remastered). Intruder and Games Without Frontiers surely came from the Inpert Land. The introduction to Intruder was glass on glass scratching. i applied to Real World Studios a long time ago and lost the damn rejection letter. Amazing stationery, multi colored and typed so nicely and hand-signed. Never felt so free in the face of rejection.

Book if you want to pick it up. This is the expanded version i have the original print. Psycho-Cybnernetics

June 23, 2026June 23, 2026 TCTJr Human-Performance|Random_Thoughts

Book Review: The Obstacle Is The Way

First, as always, i trust everyone is safe. Second, i haven’t written a book review in quite some time, and for the second time, i finished “The Obstacle Is The Way” , by Ryan Holiday. Thus i was compelled by the reader gods to review said tome.

It is said that before entering the sea
a river trembles with fear.
She looks back at the path she has traveled,
from the peaks of the mountains,
the long winding road crossing forests and villages.
And in front of her,
she sees an ocean so vast,
that to enter
there seems nothing more than to disappear forever.
But there is no other way.
The river can not go back.
Nobody can go back.
To go back is impossible in existence.
The river needs to take the risk
of entering the ocean
because only then will fear disappear,
because that’s where the river will know
it’s not about disappearing into the ocean,
but of becoming the ocean.

~ The River Cannot Go Back by Kahil Gibran

So, Dear Reader, you have before you this insurmountable situation, this issue, this frustration, or perhaps the looming public-speaking engagement that has already occupied far more psychological space than the event itself deserves. What if, buried somewhere inside any of the aforementioned, there is actually something beneficial for you not because every hardship arrives carrying a neatly wrapped lesson, but because almost anything can become useful once you stop allowing it to dictate the story?

i decided to re-read “The Obstacle Is The Way” by Ryan Holiday. For those who have read my blogs or actually know me, I am pro-reading a book three times. This comes from the book “How To Read A Book.’

Holiday’s central idea hit me like a wave against a sheer cliff: “What stands in the way becomes the way,” a line he pulls directly from Marcus Aurelius’ Meditations, which i had just happened to reread for the third time, this time more for comparison than consolation.

He is talking about Stoicism, of course, though not the dusty, marble-bust version taught as an elective and promptly forgotten. This is Stoicism adapted for the modern grind and organized around three disciplines: perception, action, and will. Perception is the ability to separate the event from the elaborate tragedy your ego has constructed around it; action is the deliberate movement that follows; and will is the internal architecture that remains when circumstances have removed nearly every other form of agency.

Holiday begins the discipline of perception on page 11, but the idea becomes useful around page 19 in “Recognize Your Power,” where the distinction emerges between what has happened and what we choose to make it mean. The obstacle is real; the helplessness surrounding it is often manufactured.

This becomes sharper in “Alter Your Perspective” on page 36. Perspective is not positive thinking, nor is it pretending that something objectively awful is secretly wonderful. It is the ability to change the frame without changing the facts to step far enough outside the situation that you can see its actual dimensions rather than merely experience its emotional gravity. i have started to deeply use this saying in difficult situations: “Perspective vs Perception”.

Perception and perspective are often treated as interchangeable, but they operate at different layers of the mind. Perception is the immediate interpretation of the emotional and cognitive machinery deciding what the obstacle means the moment it appears, while perspective is the distance we create afterward, the ability to step outside ourselves and see the same event within a larger system, timeline, or set of possibilities. Perception says, “This is happening to me”; perspective asks, “What is actually happening here?” One governs the first reaction, the other determines whether that reaction becomes a prison.

That is considerably more difficult than it sounds. It requires self-reflection and awareness of self.

Besides death, all failure is psychological.
—Jocko Willink

One nit for me is that the book occasionally leans too heavily into motivational-poster territory, where every failure becomes an opportunity, every rejection a redirection, and every collapsing building apparently an invitation to admire the architecture on the way down.

Not every obstacle is a conveniently positioned stepping stone. Some are institutional, irrational, or manufactured by mediocre people who protect comfortable systems; some consume years, damage confidence, and yield very little beyond the knowledge that you should never repeat the experience. Critics who dismiss the book as “airport Stoicism” are therefore not entirely wrong, because its formula can feel shallow when the problem is more serious than a missed deadline or an uncomfortable meeting.

Yet perhaps that criticism also misses the utility of the thing. A survival manual does not need to explain the complete nature of suffering; sometimes it merely needs to help you survive the night.

Rejection becomes evidence that we are unworthy. Being ignored becomes proof that we are irrelevant. An organization’s inability to use us becomes a verdict on whether we had anything useful to offer in the first place.

Rarely is any of that true. It is our ego that is talking to us, and by us, i mean the various forms of YOU.

Take, for example, ice skating. Sometimes the rink is simply too small not in its dimensions, but in what it permits you to become. Sometimes the coaches, judges, or skaters around you lack the imagination, patience, or courage to understand a different line, a different rhythm, or a program that does not fit neatly inside the expected form. Sometimes the system is built to reward repetition, protect hierarchy, and favor the technically familiar over the artistically dangerous. And sometimes you step onto the ice believing you were invited to skate your own program, only to discover that everyone else expects you to trace the same circles, hit the approved marks, and never venture too far from the boards.

Personally i prefer artistically dangerous.

Holiday’s “Live in the Present Moment,” beginning on page 45, is useful here because the mind loves to convert one obstacle into an entire imaginary future. A stalled initiative becomes a ruined career; a difficult conversation becomes permanent exclusion; one emphatic NO becomes evidence that every door ahead is already locked.

The present is usually less catastrophic than the mythology we build around it.

Years ago, in “FLAW: Not Thinking Big Enough or What Is Success?”, i wrote that we almost never think big enough in our endeavors, and when we finally believe we are approaching something audacious, grandiose, or stupendous enough, the word NO begins arriving from every direction.

No, you cannot do that.

No, it has never been done.

No, the organization does not work that way.

No, the market is not ready.

No, you are not the one.

The point was not to celebrate rejection for its own sake, or to pretend that every person saying NO is secretly frightened by your brilliance. Sometimes NO is valuable information; sometimes the idea is wrong, the timing is terrible, or your execution is deficient. The discipline is learning the difference between a warning and the gravitational pull of people who have mistaken familiarity for truth.

Every NO becomes another artifact from the boundary between what already exists and what someone is attempting to create. As I wrote then: for every NO you collect, you are one step closer to your success, not because success is cosmically guaranteed, but because anything genuinely new must first survive contact with those who cannot yet see it.

However, most often you are correct in your endeavor, just making it even more audacious. When the vision remains intact after honest inspection, collect the NO. Keep moving EverForward!

Interesting note here: i used Google’s NGRAM viewer to look up the usage of the word NO. It appears to have heightened in the mid-1940s and 1980s and has declined since then. Does that mean everything is OK? Note to self, i need to research why that happened.

That is where Holiday’s “Finding the Opportunity,” on page 53, becomes more sophisticated than the usual command to remain positive. The opportunity is not always hidden inside the obstacle like a prize in a cereal box. Sometimes the opportunity is information: the NO reveals who has imagination, where the system is brittle, which assumptions are protected, what must be rebuilt, or whether the work belongs somewhere else entirely.

Both resistance and acceptance produce a signal. Knowing which to parse is the perspective.

The Stoic response is therefore not to pretend the rejection does not hurt, nor is it to remain perfectly still while someone repeatedly strikes you with a hammer. Acceptance is not resignation. Resignation says there is nothing to be done; acceptance says this is the terrain, these are the constraints, these are the NOs I have collected, and now I must decide where to place my feet.

You may not control whether someone recognizes your value, but you control whether you continue creating it. You may not control whether an organization changes, but you control whether its inertia changes you. You may not control the politics, personalities, budgets, titles, markets, or invisible forces rearranging the board while everyone insists the game is fair, but you still control the quality and direction of your next move.

God, grant me the serenity to accept the things I cannot change, the courage to change the things I can, and the wisdom to know the difference.

~ Niebuhr – edited newer version

Which brings us to page 60: “Prepare to Act.”

Perception without action is simply a more sophisticated form of paralysis. You can understand every incentive, diagnose every dysfunction, identify every character flaw, and map the entire system down to the last trembling bureaucrat, yet none of that matters unless the understanding changes what you do next.

Perhaps the obstacle forces you to sharpen the argument, exposes a weakness that confidence had concealed, or teaches you that title and authority are not remotely the same thing. Perhaps it reveals that the mountain you have spent years climbing is leaning against the wrong wall.

Or perhaps it simply teaches you when to leave.

The obstacle becomes the way because it forces a decision that comfort would have permitted you to postpone. Friction produces information; resistance creates a signal; and failure, humiliation, stalled initiative, public stumble, an executive who underestimated you, or the latest addition to your collection of NOs all contain data, assuming you can extract it without letting the noise consume you.

An event is not an identity. A mistake is not a destiny. An obstacle is not a verdict. It is the terrain.

These words, passed down from the ancients, will carry me through every adversity and maintain my life in balance. These four words are: This too shall pass.

I will laugh at the world.

~OG Mandino

The Obstacle Is the Way is not a deep philosophical treatment of Stoicism; it is closer to a survival guide for the modern grind, a neon sign flickering in the darkness and reminding you to keep moving not blindly, not cheerfully, and not while pretending pain is somehow beautiful, but deliberately.

The obstacle may not have appeared for your benefit; life is not always that poetic. Once it is there, however, what you make from it belongs to you.

The insult can become clarity. The rejection can become direction. The frustration can become fuel. The public-speaking engagement can reveal a voice you did not know you possessed. The institution that refuses to change can teach you never again to confuse position with power, activity with progress, or inclusion with influence.

And the roadblock may finally force the question you should have asked long ago:

Why am I trying so hard to travel in this direction?

Perhaps the obstacle is not testing your commitment to the path.

Perhaps it is telling you to choose a better one, or even a harder one, which, Oh Dear Reader, is the best way as far as i am concerned. Always choose the hardest path. The untravelled path yields what i call “firsts”. It also has the best memories and stories later on in your life.

Do The Thing and get the courage later.

~ Joseph Campell

So, Dear Reader, when you find yourself standing before the impossible thing, resist the instinct to ask why this is happening to you. Ask what it is showing you. Separate the event from the story, study the terrain, collect the NO, and take the next deliberate action while preserving enough fire within yourself to recognize the next opportunity that will manifest itself in the #EverFoward!

Until Then,

#iwishyouwater <- Humans getting the real memo. Go Live. April 2026 Massive South Swell Zicatela Beach, Mexico.

Ted ℂ. Tanner Jr. (@tctjr) / X

Muzak To Blog By: Sugar Candy Mountain, 666, groovy 1960s throwback psychedelica.

The Word Itself

What They Actually Did

And Now On With The Show

The Floor

The Token Is Not A Layer

Atoms, Bits, Atoms

The Pendulum Knows The Way Home

A Proposal: The Joule Standard

Where It Measured

What This Means For Everybody Building Right Now

The Stance

Notes and References

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The claim, stripped of ceremony

The biology rail: precedence, or nature copying its own homework

The AI rail: substrate independence, and the word “learning”

The quantum rail: why Wecker is on the byline

One stage, many laws: getting Minkowski right first

The anti-eternalist move and where Everett secretly shakes its hand

Through the taxonomy

The Infinite Do-Loop

Appendix — The Cover, Decoded

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