Nuclear, The Story Not Told

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Two numbers.

Hydro: 1.30 deaths per terawatt-hour of energy generated.
Nuclear: 0.03.

I spent thirty years in power electronics and systems engineering. I’m used to evaluating risk from data. When I first looked at those two numbers sitting side by side in the same table, I had to read them twice. Then I went looking for where the data came from, who had reviewed it, and whether anyone had poked holes in it. Nobody had. The methodology is consistent across multiple independent sources, and the numbers have held for years.

The question that’s stuck with me since is a simple one: why does one of those numbers drive policy, and the other one barely gets mentioned?

That’s not a rhetorical question. It has an answer. And the answer is worth understanding, because the same mechanism is almost certainly operating right now on something else entirely.

August 1975

Henan Province, China. Typhoon Nina hits a cold front and drops roughly a year’s worth of rain in 24 hours. The Banqiao dam, built to handle a so-called thousand-year flood, gets overtopped. Sluice gates are partially blocked by sediment. The dam fails. Then 62 downstream reservoirs fail in sequence.

A wall of water six meters high and ten kilometers wide moves down that valley at close to 50 kilometers an hour.

Direct deaths: approximately 26,000. Total deaths, once you account for the famine and disease that followed the destruction of the regional water supply and agricultural system: somewhere between 171,000 and 230,000 people. The Chinese government suppressed the numbers for decades. Most people in the West have never heard of Banqiao.

Nobody stopped building dams.

No global regulatory freeze. No decades-long moratorium on new hydroelectric construction. Banqiao killed more people than every nuclear incident in history combined, and the policy response was essentially nothing.

Hold that thought.

Nuclear’s Actual Record

Three events define public perception of nuclear power.

Three Mile Island, 1979. Partial meltdown. Serious incident. Zero direct deaths. Peer-reviewed studies found no measurable increase in cancer incidence in the surrounding population. The containment system worked.

The public response: mass panic. The regulatory response: construction permits frozen across the United States. No new nuclear plant ordered after 1974 was completed for decades.

Chernobyl, 1986. A genuinely catastrophic failure of a reactor design with known safety flaws, operated outside its safety envelope during a test. Peer-reviewed death toll: approximately 433. That number gets reported in popular media as tens of thousands, sometimes hundreds of thousands. The peer-reviewed literature does not support those figures.

Fukushima, 2011. This one needs context. A magnitude 9 earthquake and a 15-meter tsunami killed approximately 20,000 people directly and destroyed the regional infrastructure. The nuclear plant failure was a knock-on of that disaster, not a standalone event. Hospitals were already overwhelmed, supply chains already broken, shelters already strained.

That context matters, and it makes what follows more striking, not less.

Deaths from radiation: zero. UNSCEAR and the WHO both confirm no member of the public or plant worker died from acute radiation exposure.

Deaths from the evacuation: 2,313. Officially certified by the Japanese Reconstruction Agency. Cohort studies compared evacuated groups against matched groups in the same disaster zone who were not subject to the nuclear evacuation order. The excess mortality still tracks to the evacuation specifically, not the general disaster. Patients pulled off ventilators and loaded onto buses. Elderly evacuees in unheated gymnasiums in March without their cardiac medications. Long-term displacement that drove a documented spike in strokes, heart attacks, and suicides.

The evacuation killed 2,313 people. The radiation killed none.

Germany’s response: shut down all nuclear plants within twelve months.

The Thing Nobody Talks About

If nuclear is the thing people are afraid of, coal is the thing nobody discusses.

Coal sits at 24.62 deaths per terawatt-hour. That’s not an accident rate. That’s a baseline. It runs every hour the plant is operating, in every community downwind, invisibly.

The mechanism is particulate matter, specifically PM2.5. Fine particles that bypass your lung filtration and enter the bloodstream. They drive ischemic heart disease, stroke, and chronic obstructive pulmonary disease. The people dying from this don’t die in a dramatic event. They die over years, and the death certificate says heart disease, not power plant.

In the United States alone, between 1999 and 2020, 460,000 deaths were directly attributed to coal particulate emissions. That’s a peer-reviewed figure from a study published in Science in 2023, based on Medicare records.

Globally, fossil fuel air pollution causes an estimated 5.13 million excess deaths per year.

No headlines. No evacuation zone. No footage. No panel of experts. It just happens, continuously, and we have decided as a society that this is acceptable.

Why the Data Didn’t Matter

This is the part worth sitting with.

The deaths-per-TWh data has been available and consistent for a long time. The numbers aren’t new. So why did the policy response to nuclear go one way and the response to everything else go another?

The answer isn’t scientific illiteracy, though that plays a part. The answer is how human risk perception actually works, and specifically a category psychologists call dread risk.

Before applying it to nuclear, try it somewhere closer to home.

Roughly 40,000 people die in car accidents in the United States every year. Commercial aviation kills, in a typical year, somewhere between zero and a few dozen. Per mile traveled, you are orders of magnitude more likely to die in a car than on a plane. Most people know this, at least abstractly. And yet a significant portion of the population is afraid to fly and gets in the car without a second thought.

That’s not stupidity. That’s a predictable failure mode of the human nervous system. Dying in a plane crash feels uncontrollable, invisible in its causes, and catastrophic in its image. Dying in a car feels like something that happens to other people who weren’t paying attention. The statistics are not what’s driving the fear response. The characteristics of the event are.

Psychologists have a precise vocabulary for this. The factors that amplify perceived risk include: whether exposure is voluntary or involuntary, whether the mechanism is visible or invisible, whether effects are immediate or delayed, and whether the hazard carries prior cultural associations with catastrophe. Score high on those dimensions and people will treat a low-probability event as an existential threat. Score low, and they’ll accept a high-probability harm without complaint.

Nuclear hits every trigger. Radiation is invisible. Exposure is involuntary. The effects are delayed. And the word “nuclear” has carried the weight of Hiroshima and Nagasaki since 1945. Thirty years of Cold War civil defense films, fallout shelter drills, and duck-and-cover exercises had done their work long before Three Mile Island. The public was primed.

Coal has none of that. It kills more people per unit of energy than any other source in widespread use. It kills them slowly, distributed across populations, through mechanisms that show up on death certificates as heart disease and stroke. There is no “coal incident” that preempts the evening news. So coal never triggered the dread response. It just kept running.

Here’s where it gets interesting from a broader perspective. Once you understand that nuclear had a unique set of psychological vulnerabilities, you also understand that those vulnerabilities were predictable. And predictable vulnerabilities are usable ones.

You didn’t need to fabricate data to keep nuclear from expanding. You didn’t need to lie about the death toll or invent risks that didn’t exist. You just needed to keep the fear operational. Any outcome you wanted from that situation, whether it was energy policy, geopolitical competition, protection of existing energy assets, or genuine environmental concern, ran through the same lever. The mechanism did the work regardless of the motive behind it.

That’s how you get a policy response that froze an industry after an incident with zero deaths, while an energy source that kills hundreds of thousands of people a year kept operating without comment.

The data didn’t change the policy because the data was never the driver of the policy. The fear was the driver, weaponized by a lot of different actors. And fear, once well-established in a culture, doesn’t need new information to sustain itself.

What It Cost

After Three Mile Island, the NRC froze construction permits and shifted to an adversarial licensing posture. Plants that were 80% complete had to be redesigned to meet new rules written after they broke ground. The rules kept changing. Cable separation distances. Concrete specifications. Redundant backup systems. Each rule issued as a response to perceived risk, none ever rolled back.

The industry has a name for it: the regulatory ratchet. Rules only move in one direction.

The result: construction timelines doubled. Overnight capital costs increased by over 200%. An industry that had been commercially viable became financially impossible.

One example makes it concrete.

The Shoreham Nuclear Power Plant on Long Island, New York. Construction started in 1973. The original cost estimate was $75 million. The plant was completed in 1984 at a final cost of $6 billion. The regulatory environment had changed so many times during construction that the finished plant was essentially built twice. After completion, the plant ran a single low-power test. It never delivered commercial electricity to a single home. In 1989, Long Island Lighting Company transferred ownership to New York State for one dollar. The plant was decommissioned.

Zero deaths at Three Mile Island. The containment worked. The response was to make nuclear power economically unbuildable for the next forty years, and to leave a completed $6 billion power plant sitting idle until it could be taken apart.

The Question the Data Left Open

The engineering question was answered a long time ago. Deaths per terawatt-hour is a clean metric. The data is consistent across multiple independent methodologies. Nuclear kills fewer people per unit of energy than any fossil fuel, and roughly the same as wind and solar. Hydro sits at 1.30, driven almost entirely by a single dam failure in 1975 that most people in the West have never heard of.

The question the data leaves open is not whether nuclear is dangerous. The data settled that. The question is why the data didn’t matter, what it cost us that it didn’t, and whether you can now look at any other technology or industry and spot the same pattern running.

Find something that scores high on the dread risk dimensions. Invisible mechanism. Involuntary exposure. Delayed effects. Prior cultural associations with catastrophe. Then look at what the data actually says about it versus what the policy response has been.

The mechanism is still running. The only variable is what it’s pointed at today.

Here is the file

nuclear_video_slidesDownload

M.A. Harris is a systems and mechanical engineer with 30 years in power electronics and a particular interest in how engineering data interacts with public policy. He writes hard science fiction as M.A. Harris and runs The Unretired Engineer on YouTube.

📺 YouTube: https://www.youtube.com/@Scifiengineer-09
🔗 LinkedIn: https://www.linkedin.com/in/mark-a-harris
📚 Published works (M.A. Harris): https://www.amazon.com/author/m-a-harris

The Why-Shaped Itch

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From cave walls to the cosmos: how humans built the One out of questions they couldn’t stop asking

Philosophy | May 2026

Intelligence is based in memory, without why they are useless. The moment you can ask why, you will, and you’ll keep asking until you hit a wall the evidence can’t get you past. That wall is where religion lives.

This isn’t a weakness. The why-drive is the engine behind every model humans build of the world. It starts with fire and weather and then, inevitably, it turns to the question behind all questions: what started this?

Vocabulary

Most discussions of God get tangled before they start because people use the same word to mean very different things. Here’s the map I work with:

  • The Origin — the start of everything, defined as an event. No feeling of intention behind it.
  • The Final Cause — which is to say, first cause, a step between the Origin and the Absolute. Still largely intentionless, but there’s a tint of something.
  • The Absolute — Less an event than an impersonal creative condition.
  • The One — the Absolute plus intent. This is a matter of faith, not evidence. It cannot be known from what we observe.
  • God — the personification of the origin of all we perceive. The One given a face and a relationship with humanity.
  • god (lower case) — a referent to one of many deities in a system where there is no single final origin.

On gender: rendering God as he or she assumes things that aren’t in evidence and are arguably contra-indicated by the concept itself. A creator might seem more female if you think in terms of procreation, more male or neuter from a philosophical standpoint. Neither is satisfying unless you set out from an assumed initial condition, personification is a human need.

Beginning

Animism came first — scratching the why-itch into a set of beliefs that could be shared across a tribe. It works at small scale. As culture complexifies, you get gods: local, specific, squabbling. Then philosophy pushes further back, past the gods, toward a single origin, and you start to get God.

The Hellenistic world shows this arc clearly. It started with gods, evolved philosophy that defined the absolute origin, and from there derived a concept of God. That Hellenistic concept of the One then wrapped around evolved Judaism — with its apocalyptic messianic tradition — and produced Christianity. Islam followed, melding tribal Arabian religion with Judaism and Christianity into something that collapsed individual conscience into a tribal collective. That’s the source of its strength but a reason that it’s historically been a threat to neighboring structures.

Egypt started a similar philosophical evolution and then, probably due to the shaping effect of Nile Valley culture on its social structure, devolved back to gods. The environment bends the theology.

Consciousness

Even extremely simple worms react to stimulation in idiosyncratic ways, suggesting some differentiation in even minimal nervous systems. Single-cell organisms show behavioral differentiation that might indicate some level of something. Ants recognize themselves in a mirror and try to remove marks that would get them attacked at the nest entrance.

Does only self-consciousness constitute mind? Does consciousness without self-consciousness exist? These are thoughts we struggle with as we look at the evidence in the world we live in and apply it to the question of origin. What is the relationship of Mind and Consciousness to the Absolute?

The evidence says there’s an origin. Whether that beginning had intent is the question the evidence cannot answer.

Origin

The origin of our universe produced complex organization that chained up through cosmology to chemistry, to life, intelligence, ecology, and society. That’s not random noise out of an infinite field of interactions. It’s structured emergence across effectively infinite time and space.

This argues, at minimum, for an Absolute that set the conditions for what is. It also suggests that ethics, philosophy, and meaning were intrinsic from the start not invented by humans but discovered, the same way mathematics and physics are discovered. Invention from nothing is not real, we find what was already there (in my opinion a categorically more difficult problem given the complexity of our reality.)

Whether you take the next step, from Absolute to One, from impersonal origin to intent, is where evidence runs out and faith begins. Not faith as credulity, but faith as a position you hold in the absence of proof in either direction.

From the One to God is personification: a human need, driven by the desire for relationship with the absolute rather than mere acknowledgment of it.

That’s not irrational. It’s the oldest human need there is.

More on engineering, technology, and science fiction on YouTube. Fiction and commentary on the bigger questions at Substack.

The Technology Lapped the Argument

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*Related video: https://youtu.be/Rt3u7k1Qn2Y

I spent the last years of my engineering career inside the EV supply chain. SiC power modules, fast charging infrastructure, the physics that makes any of this possible. When the market stalled and Wolfspeed went into Chapter 11, I was among the people who lost their jobs to the gap between technology that was ready and deployment that wasn’t managed well.

So I have some standing to say this: most of what you’re hearing about EVs right now is noise. The signal is somewhere else entirely.

What the argument is actually about

The loudest voices say EVs are failing. Mandates reversed, high-profile products stumbled, the market is retreating. Some of that is true. Most of the framing around it isn’t.

What’s happening is a culture war machine doing what it does: taking a nuanced engineering and industrial policy question and flattening it into a yes/no yelling match. The anti-EV drum is politically useful to a coalition that learned to hate the mandate era. That’s a social politics problem, not an industrial one.

The previous administration pushed hard on EVs and that push was heavy-handed. Mandates handed down by people who had never read a cost model. Timelines written by committees with no idea what it takes to retool a supply chain. You can’t magic this into existence. The regulatory overcorrection was real and it wasn’t sustainable. The current environment is closer to “let the market work,” which was always the more defensible position.

That’s not a retreat on EVs. It’s a retreat on mandates. Those are different things.

The one policy thread that actually matters

There’s a legitimate industrial policy question buried under the noise, and it has nothing to do with the culture war.

Chinese battery manufacturers operate at a cost and scale US industry can’t currently match. BYD LFP packs at $81/kWh against North American packs struggling to get below $120/kWh. That gap is structural. It’s the product of state subsidy, overcapacity investment, and a decade of manufacturing maturity that didn’t happen here.

Tariffs are a response to that. Not a new tool — tariffs have always been part of industrial policy. They buy time for US manufacturing to find its footing before the cost differential makes the conversation moot. They slow the bleeding. They don’t end the issue. The hard commercial work still has to happen inside that window of protection.

While the argument raged, the engineers kept working

The two real technical objections to mass EV adoption were range and charge time.

Range has been largely answered for most drivers. The argument was always more perception than physics for the majority of use cases. There’s still a range/cost trade-off at the edges, and for certain use cases — long hauls, thin infrastructure corridors — it’s real. But for the suburban commuter with a predictable route, it was a phantom.

Charge time was the harder objection. It’s the one that lands with people who have driving patterns that don’t fit a neat daily commute.

That objection just got answered. CATL has announced a lithium-ion pack that goes from 10% to 80% charge for a long-range car in 5 minutes — essentially what you spend at a gas pump. With a cycle life as good or better than existing packs. That’s not a lab result. That’s a discontinuity arriving at production scale.

The cost curve has been moving the same direction for years. $137/kWh in 2020. $108/kWh in 2025. Consensus puts it at $60-80 by 2030. And unlike the internal combustion engine, which hit a performance plateau a while ago — where further gains tend to require turbos, complexity, and reliability trade-offs — battery technology still has a long way to run on the improvement curve.

The original technical case against EVs is now essentially closed.

The part most people haven’t thought through

Here’s the engineering insight that gets lost in the political argument: the EV value proposition is not uniform across vehicle categories. It runs in opposite directions depending on what you’re building.

A large pickup truck is a worst case. Heavy vehicle requires a big battery. Big battery adds weight. More weight means lower range. Lower range demands an even bigger battery. The vicious circle compounds fast, and the economics get brutal.

A mid-range car is a much better case. A small car is better still. An e-bike is the virtuous circle running full speed — light vehicle, small battery, low cost, genuinely better than the alternative on almost every axis.

The early EV push got this backwards. It over-emphasized large, expensive vehicles in categories where EV physics work against you, and under-served the mid and lower segments where the virtuous circle runs hard. That product mix failure wasn’t inevitable — it was a choice, driven by margin structure and political optics.

The companies that are finally starting to fix this understand where the physics are on their side.

What I expect to happen

The social politics will move on when the machine finds a new nuanced topic to flatten. It always does. The yelling tends to continue well past the point where it makes any sense, but it does eventually stop.

The regulatory environment is already normalizing. The tariff question is real and separate — you don’t abandon it just because the culture war has moved on, because the industrial competition with China doesn’t care about US political cycles.

Quietly, at the mid and lower price points, there’s no deep quitting. The product mix is starting to fill in where it should have been years ago.

By the time the US political environment fully resolves its feelings about EVs, the engineering will be a generation ahead of the debate. The technology wasn’t right five years ago for most buyers. It’s about right today. Tomorrow it will be better, and the gas engine curve doesn’t have the same headroom left to answer with.

The technology lapped the argument. Almost nobody noticed while it was happening.

Mark Harris is a systems and mechanical engineer, recovering from a career in EV power electronics, and the author of Stranded in the Stars (Book One, The Sea of Suns Trilogy). He writes about engineering, technology, and the creative life at This World and Others. The Unretired Engineer is on YouTube at https://www.youtube.com/@Scifiengineer-09

We Handed Them the Market

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Related video: Range Anxiety — The Unreal Reality

I’ve been involved with EV power and propulsion for much of the last 30 years. My latest stint was at Wolfspeed, developing SiC power modules for EVs and fast chargers. When the EV market stalled and the company went into Chapter 11, I was among the people who lost their jobs.

I still think EVs are the right direction. I don’t own one. That’s not a contradiction, it’s the actual story, and the video above is where I work through it.

The short version: range anxiety was always overblown for most drivers, and the auto makers never built the product mix that met the needs of the broad market. Now the industry is driving hard away from EVs, especially in the US, and that’s just wrong-headed. The video closes on that but doesn’t dig into why. This post does.

The Part That Stings

While the US was arguing about mandates and turning the issue into clickbait, China was engineering.

BYD is selling comfortable, adequate-range EVs in the $15–20K range. That’s the vehicle that moves the majority of buyers. Not the Cybertruck, not the F-150 Lightning, not the Rivian. A practical car at a price most people can actually consider.

We handed them that market. Not through malice or conspiracy, but through a combination of policy that optimized for the wrong things and an industry that focused on protecting its margins.

The policy pushed hard for EV adoption with mandates, subsidies, timelines. Some of that pressure was probably warranted. The market would have gotten there on its own, but the question of when and at whose expense was real. The intervention accelerated some things. What it didn’t do was direct the industry toward the product that would actually move the needle for most buyers.

The industry copied Tesla’s playbook; premium vehicles, long range, performance, high price points. That was the wrong lesson. Tesla used that model to fund the manufacturing and infrastructure investment that actually mattered. Everyone else just took the margin and stopped there.

The charger network made the same error I described in a previous video: build for the metric that looks good in the grant report, not the outcome that matters to the driver. 97% uptime. 71% charging success rate. Two different measurements, only one of which tells you whether the thing worked.

Why Big Auto Isn’t Saving Itself

I always loathed the heavy-handed government push on EVs and what I read as gaslighting on the rationale. Mandates handed down by people who had never looked at a cost model. Timelines written by committees that had no idea what it actually takes to retool a supply chain or build an infrastructure.

At the same time, I think some intervention was warranted. Not because the market was wrong about EVs, but because the market was optimizing for the next quarter. And the externalities of the status quo were landing on people who weren’t in the pricing model.

Intervention at scale creates dependencies. The industry made bets premised on the government backstop continuing. When the political environment shifted, those bets didn’t just look bad, they collapsed. And the response has been to drive hard back toward gasoline, as if that solves anything.

US old-line auto companies have been struggling for decades, and the reasons are structural. They’re trapped by regulatory capture and built-in costs that make adaptation nearly impossible.

Start at the sales end. Their dealer networks are regulated state by state, which makes wholesale change all but impossible. Safety regulations run through a system where insurers push regulators to require improvements that the industry develops partly because those improvements push up vehicle margins. Manufacturing plants are at their core decades old, and the capital they represent sits on the books, write it down and you impair the balance sheet. Design is path dependent by habit and incentive: most changes are incremental tweaks to last year’s platform because that’s easy, cheap, and legible to accounting.

And the margin structure makes it worse. Bill-of-material cost for a vehicle increases slowly with size and content. Market value is largely bling-dependent. So the incentive always points toward large, well-fitted vehicles where the spread is widest, and away from the small practical vehicle where there’s almost none.

Meanwhile, the manufacturing model has already been cracked. A new generation of EV makers proved you can build at scale in the US, turn a profit, and drive down the cost curve without the legacy overhead strangling the old players. Big Auto is watching that happen and still can’t follow, because the legacy network isn’t just a cost problem, it’s a constraint on every decision they make.

Moving back to gasoline doesn’t fix any of this. It may help sales volume near-term, but fewer and fewer buyers are willing to pay up for big iron, and as the recent spike in gas prices reminded everyone, the cost of operating a gas vehicle is not as predictable as it felt a few years ago.

The wholesale abandonment of EVs is as wrong-headed as the mandates-first push that preceded it. You’re walking away from the future as it’s getting its feet under it, and you’re not fixing your actual problems in the process.

Different direction, same failure mode: optimizing for the political moment rather than the real problem.

What I Expect to Happen

The market will keep sorting this out despite the policy environment, not because of it.

Amazon is sponsoring the Slate, a small electric truck aimed squarely at the price point where the volume is. Ford is talking about smaller, value-forward platforms. The product mix gaps are starting to fill in, and the players doing it understand they have to meet buyers where they are, which is around $20K for a vehicle that’s good enough and built around what EVs actually do well.

BYD is a harder question. It was built on the back of Chinese state support and practices that wouldn’t survive scrutiny elsewhere, but that doesn’t change what it demonstrates: a level of technical maturity across product fit, design, and manufacturing that very few other automakers can match. Tariffs and regulatory barriers will slow it down. They won’t hold permanently. Some form of that capability will find its way into the US market, and when it does it will accelerate the shakeout that’s already coming for Big Auto.

Charging infrastructure will improve in the corridors where the economics support it and stay thin everywhere else, and that’s how it should work. Where it’s thin, the economics will eventually pull in local investors, the same way any other service infrastructure fills in. It won’t be fast, but it will happen.

The transition will come, just slower and more expensively than it had to be. The destination is probably the same. The cost of getting there is substantially higher, and much of the value being created will go to manufacturers who aren’t American. That’s the envelope effect of all the intervention and counter-intervention stacked on top of each other.

The engineers mostly knew it was going to be complicated. Technical change at a social scale always is. The complicated part is rarely the technology.

Mark Harris is a systems and mechanical engineer, recovering from a career in EV power electronics, and the author of Stranded in the Stars (Book One, The Sea of Suns Trilogy). He writes about engineering, technology, and the creative life at This World and Others. The Unretired Engineer is on YouTube at https://www.youtube.com/@Scifiengineer-09

Dismantling Silos: A Path to Agile Engineering

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Boundaries are necessary. That’s not the argument.

Every engineering project starts with bounding — what you’re solving, what the solution has to do, what’s out of scope. Without that, you’re not engineering, you’re wandering. The boundary is how you make the problem solvable.

The modern corporation learned the same lesson at scale. Adam Smith’s insight wasn’t complicated: split work into elements, run them in parallel, and you can deliver what no individual craftsman ever could. From Renaissance capital markets to the factory floor to the aerospace prime contractor, that logic held. Boundaries enabled scale.

When I joined the workforce in 1982, the logic was still holding — and you could feel why. I had a notebook and an HP calculator. A shared secretary supported the division manager, and before any report left the building it needed sign-off from both my branch manager and his. Not bureaucratic obstruction — that was the information architecture. Reports were dense, slow, and gatekept because they had to be. Management structure existed in large part to curate that flow — to compress what mattered, pass it up the chain, and keep the organization pointed in the right direction. The stovepipe wasn’t a bug. It was load-bearing.

Between 1982 and 2002 two things happened simultaneously that should have changed the equation. First, information handling exploded. The PC, networks, sensors — generating and moving information became cheap and fast. Second, process culture arrived. The US had watched the Japanese manufacturing renaissance and brought back a set of ideas about quality and process that got bolted onto the existing corporate hierarchy. At exactly the moment when individual engineers could span across an organization and get at information directly, the process culture locked the structure down harder.

The result in many companies: more capability to move information, less permission to use it. The stovepipes stayed. The rationale quietly expired.

I ran three programs across my career that show the delta. At SatCon on the AIPM program — Advanced Integrated Power Module, a DOE/Navy cost-share — I was simultaneously program manager and lead engineer, spanning manufacturing, electrical design, mechanical design, and simulation. We went from concept to demonstrated production-ready modules in three years on a modest budget. That approach, the sub-module test-before-integrate architecture we developed, is now standard inside automotive power electronics. Tesla uses a version of it.

At DRS, working with Allison Transmission on an integrated generator for military vehicles, we built a successful solution and demonstrated it to the Army. General officers asked why they couldn’t have more. It took ten years for the technology to gain traction — not because the engineering was wrong, but because the organizational and procurement structure couldn’t move.

At Wolfspeed, deep stovepipes. Marketing, sales, test engineering, module design, device fabrication — separate organizations, separate priorities, separate permission structures. Getting a new product from concept to release meant handing information off at each boundary and then jawboning it forward, because you couldn’t do their job for them and they had to queue the work against their own priorities. Fifteen products out the door. Every one of them harder than it needed to be.

The stovepipes were there to protect quality. They also stopped momentum.

What’s changed now isn’t the human desire to span boundaries — engineers have always wanted to do that. What’s changed is that the tools exist to actually do it. Companies that have built their information architecture from scratch rather than inheriting it — the Teslas, the newer defense tech firms — have demonstrated what happens when low-level actors have access to the full context of what the organization knows. Engineers and technicians can interrogate data, surface patterns, propose action. The information that used to require a management layer to curate is available directly. The span of control moves down the org chart.

For incumbent organizations with data already siloed, this is genuinely hard. The stovepipes aren’t just structural — they’re also where the institutional knowledge lives, and dismantling them requires executives who are willing to accept that the curation function they’ve been performing can be partially replaced. That’s not a technical problem. It’s a political one.

Christensen’s Innovator’s Dilemma describes what happens to incumbents who don’t solve it. A smaller firm with narrower scope but faster movement finds a niche. The niche gets cheaper and easier to serve. The incumbent can’t see it clearly because their whole architecture is optimized for something else. The niche expands. You know the rest.

The boundary isn’t the problem. Bounding a problem is still part of the engineering job. The question is whether, once the problem is bounded and the work begins, you’re working inside a structure that moves — or one that fills up and waits to overflow into the next pipe.

While many organizations are ‘implementing AI’ most are not working through the changes from first principles and often implementing all or nothing. The ones that don’t get around to making sure they break the stovepipes logically are going to run out of time.

This post accompanies the video Why Stovepipe Organizations Stop Working — The Unretired Engineer, April 2026.

Andy Weir’s Genius in Project Hail Mary

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Andy Weir has a rare gift: he writes ordinary people — genuinely, recognizably ordinary — who have a skill that is also recognizable, and then puts them in situations where their one extraordinary competence is the only thing standing between them and death (in the case of Project Hail Mary, the extinction of the Human race.) The heroism is quiet and technical and you could almost believe that you could do that in the right circumstances.

You believe it because he’s made you believe in the person first. I saw the movie. I read the book years ago. Both are excellent, and the movie is one of the most faithful book-to-screen adaptations in recent memory.

Like The Martian before it, the film sticks closely to the book in both thesis and spirit. That fidelity matters: both stories rely on the reader/viewer trusting that the protagonist’s problem-solving is real, not movie-magic. Break that contract and the whole thing collapses. Weir earns it on the page; the filmmakers preserved it on screen.

The one genuine gap between novel and film is interior monologue. Novels handle internal states naturally; movies almost cannot. But Weir constructs scenes that externalize internal conflict visually — and those translate superbly.

A couple of minor side arcs from the book are absent, and I think those were wise cuts. They deepened the protagonist on the page but would have felt excessive at feature length.

One thread that bothered me in the book and still bugs me in the movie: Ryland Grace is pulled into the program because in his post-doctoral research he had proposed that alien life does not require water and carbon — and had defended that position to a career-ending degree. When the AstroPhage is first discovered it appears very alien, so Grace is brought in for initial analysis. He then finds it’s made of the same materials as Earth life — which undercuts his entire reason for being there and threatens to sideline him. That it doesn’t is a good twist; go see the movie or read the book for how it resolves.

Here’s where my engineering brain creates further friction. The AstroPhage’s energy density is extraordinary, and the novel acknowledges this and hand-waves it away. I cannot see how any life form built on biology similar to our own could handle those energy levels — it feels bolted in, even if it probably wasn’t. Similarly, Rocky — the alien Grace meets at the target sun — turns out to be exactly what Grace originally proposed: a non-water/carbon life form, which feels a little convenient in vindicating him.

There are complaints about Rocky delivering a specific thematic point about first contact and communication. My view is the opposite (other than the niggle above) that whole piece is brilliantly on point and there would not have been much of a story without it.

None of that diminishes what Weir achieves. He takes relatable people with very human quirks and puts them in situations where they have to fight to survive — and we root for them completely. And here i put the very alien Rocky in the bucket of people…he is about the best alien I have seen in a move ever. I wish I were half the author he is, and I say that as someone who is trying. Project Hail Mary is the rare book where you finish it and immediately want someone else to read it so you can talk about it. The movie earns the same feeling. Go see it.

How Physics Empowers Free Will in a Deterministic Universe

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Why determinism never felt right to me — and how modern physics actually opens the door to real agency.

For years, the idea of hard determinism has bothered me. It clashes with how life actually feels. The universe as a giant clockwork machine—every particle with a fixed position and momentum, every event preordained from the Big Bang—sounds elegant in theory. But it implies that everything I’ve ever done or will do was inevitable. My choices? Just an illusion.

Hard determinists often present this view with a certain intellectual swagger, as if it signals deep sophistication. Yet many of them still look both ways before crossing the street. As Stephen Hawking wryly observed: “I have noticed that even those who assert that everything is predestined… still look both ways before they cross the road.”

That quip captures the tension. If the future is fixed, why bother acting at all? The view also carries an eerie resemblance to extreme Calvinism—some are saved, some damned, and nothing you do in this life ultimately changes the script. It never sat right with me, either intellectually or existentially.

Then I encountered the work of physicist and philosopher Jenann Ismael, particularly her book How Physics Makes Us Free. Link Her approach resonated strongly with an intuition I’d been developing for years: determinism and free will are not mutually exclusive. Physics doesn’t enslave us—it enables a deeper kind of freedom.

The “Now” Problem: Why the Instant is Trivial

Imagine the universe at a single frozen instant—the “Now.” In that timeless 3D snapshot, every particle has a position and energy vector. Past events fully determine what happens next. It looks perfectly deterministic.

But here’s the catch: that “Now” has no real existence for any actual observer. Relativity imposes strict limits. No particle (or person) can access information from outside its past light cone. At the exact moment of “Now,” that light cone has zero depth—nothing from even a tiny distance away has had time to reach you. Complete information about the universe is impossible in the present.

Laplace’s Demon—the hypothetical super-intellect that knows every particle’s state and can predict the entire future (or past)—assumes a “God’s-eye view from nowhere.” Modern physics makes that view untenable. Any real system faces data latency, noise, uncertainty, and computational irreducibility. The demon’s omniscience is a fantasy.

In short, strict determinism at the instantaneous “Now” (what I’ve called the InP, or Instant-Point) is technically true but functionally trivial. It tells us almost nothing useful about how agents like us actually operate.

Memory: The Engine of Agency

Freedom emerges not in the frozen instant, but across time through accumulated memory and structure.

Even in a blind, non-living universe, basic thermodynamics creates imprints: a rock scars the ground when it falls; waves erode a shoreline. These are primitive forms of “memory”—the past shaping the future through persistent physical traces.

Life takes this to another level. Biology is essentially memory in action. RNA, DNA, neural patterns—these are systems that record what worked and what didn’t. Evolution itself is a memory process: successful patterns persist and build upon one another.

Over eons, this scales up:

  • Simple input → output (basic matter)
  • Input → memory/comparison → internal model → action → output (living organisms)

A frog snaps at a fly. A squirrel flees at a predator’s scent. A honeybee dances to communicate nectar locations to the hive. These are not random reflexes but decisions grounded in accumulated history and pattern-matching.

Humans take it further. Language, culture, and shared knowledge externalize memory, allowing us to build on the experiences of countless others. Our decisions arise from rich internal models shaped by personal and collective history—not from some magical spark that violates physics, but from the universe’s own lawful processes.

The agent does decide. The cause of the action lies in the person’s internal identity and accumulated experience. Labeling that “determined” is technically accurate but misses the point. It’s how we function.

The Generalized Good as an Attractor

This memory-driven agency isn’t aimless. Over deep time, beings with even modest volition tend to optimize for what they perceive as “good”—survival, order, flourishing. Humans are guidable, not perfectible. We make mistakes and fall for bad influences, but signals from reality (what works vs. what fails catastrophically) are powerful if we’re willing to heed them.

History shows progress: fewer people in extreme poverty, fewer dying in wars (in percentage terms, at least). Our ancestors weren’t ignorant fools; their traditions often encoded hard-won lessons. Change isn’t inherently good, but neither is stasis. The “generalized good” acts as a global attractor, even if local maxima vary by time, place, and culture.

In deprived environments (think North Korea), external options shrink, yet people still imagine and yearn for “other worlds.” The internal model remains a generator of possibility.

My Thesis

Free will is not a violation of physics. It is the high-level, computational process of an autonomous agent using the universe’s built-in memory—personal, biological, and cultural—to steer itself through time.

Determinism at the microscopic level may hold, but it becomes trivial once you account for relativity, light cones, computational limits, and the reality of embedded agents. What matters is that you are the one deciding, drawing on your history and internal model. There is no external puppet master. The causes flow through you.

Physics doesn’t rob us of freedom. By creating a world with persistent memory, evolving complexity, and embedded perspectives, it makes genuine agency possible.

That’s why the universe feels open rather than claustrophobic. That’s how physics makes us free.

Your Charger Was Up. It Just Didn’t Work

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I put together a short take on this — under 60 seconds if you want the headline — and a longer breakdown of the structural issues for those who want the full picture.

▶ Short version (60 sec): https://youtube.com/shorts/zG-VtW2MUDU
▶ Full video: https://youtu.be/KAHuoShGtrs

There’s a number the EV charging industry reports, and there’s a number drivers experience. They’re not the same number, and the gap between them tells you everything about how this program was designed.

Operator-reported uptime: 97–99%. That’s a contractual requirement under the NEVI program — the $5 billion infrastructure buildout funded by the Bipartisan Infrastructure Law. On paper, the chargers are up nearly all the time.

Actual charging success rate: 71%. About a quarter of the time you pull up to a charger, it doesn’t charge your car. In many of those cases, nothing you do will make it work.

These are different measurements. One tells you the charger is technically online. The other tells you whether it did the job. Nobody confused them by accident — the reporting structure was built around the metric that was easiest to meet, not the one that mattered to the driver.

The failure modes are concrete. 60% of failed sessions involve a charger that’s simply out of service — not a user error, not a handshake problem between your car and the network. The unit isn’t working. Hardware degrades, software hangs, payment systems drop, network connections fail. These are expected failure modes for a system like this. The question is whether you’ve built the operations and maintenance infrastructure to catch them quickly. Most of the NEVI deployment didn’t.

New stations run at about 85% success. By year three, the same stations are below 70%. The 2022–2024 installation wave is hitting that curve now. And after year five, operators have no contractual obligation to keep the units running at all — so a lot of that hardware is simply going to disappear.

The regional variation is the tell. Seattle and LA are seeing failure rates around 24–25%. The East South Central region is at 7%. Same national program. The difference is operator discipline — some built real support structures, most didn’t, because the incentive to do so was never in the grant milestones.

This is a solvable problem. The gas station model solved it a century ago: put someone on site, make them responsible for the equipment, give drivers somewhere to wait while they charge. There’s no reason a charging network can’t work the same way. It’s just that the program specification never required it, so it wasn’t built.

Infrastructure problems are always systemic. The hardware is fine. The failure is organizational.

Mark Harris is a systems and mechanical engineer and the author of Stranded in the Stars (Book One, The Sea of Suns Trilogy). He writes about engineering, technology, and the creative life at This World and Others.

The Physics Produced the Ship

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The Dagger Design

Most fictional spacecraft are designed backwards. The writer decides what the ship needs to do dramatically, then invents a reason it can do that. The result is technology that serves the plot. Which is fine, until you need it to do something different in book three, at which point you quietly bend the rules and hope no one notices.

Engineers don’t do that. Not because we’re more disciplined — because we can’t. You don’t change the spec because the schedule is tight. You re-examine the architecture or you live with the constraint.

That instinct, applied to fiction, produces something different.

The principal auxiliary warship in the Sea of Suns universe is called a Dagger. Here’s how it got its name — and it wasn’t because I thought “dagger” sounded good.

The Transit system — the FTL drive in this universe — works through a rail. The rail is a linear gravity generator that manipulates quantum foam to open a wormhole large enough for the ship to pass through. The rail controls volume you can push through: the more mass you want to move between stars, the more rails you need. Compute controls speed: the transit step is a calculation, and the faster you want to step, the more computing capacity you need.

That trade-off isn’t decoration. It’s the architecture.

An auxiliary warship needs to be fast. In this universe, fast means compute capacity. Compute capacity takes up volume inside the vessel. So a fast warship is, almost by definition, a ship that has traded its interior for processors. Twin rails — enough to move a meaningful crew and weapons load — with almost every remaining cubic metre given over to compute. Crew of two to five on a thousand-foot vessel. Not much else aboard.

Now you have a ship that’s fast, carries almost no cargo, and spends all its operational time in real space. Real space means it’s detectable. A detectable warship needs stealth. The most effective passive stealth for a vessel in this universe is minimising cross-section — flat surfaces, minimal radar return. You sheath the hull in flat panels that force the profile into a long, slender blade shape.

The name isn’t metaphor. It’s a description of what the physics produced.

I didn’t design a cool warship and retrofit a justification. The constraints generated the vessel, and then the vessel generated scenes I hadn’t planned, because once you know what a Dagger can and can’t do, certain tactical situations become inevitable.

That’s the engineer’s advantage in hard SF, and it’s not what most people think it is.

It’s not technical accuracy. You’ve invented the technology — accuracy isn’t really the point. It’s that engineering training gives you a specific habit of mind: ask what the constraints produce, not what you need them to produce. Follow the logic. Let the system build itself.

When the system is honest, the world it generates is consistent without effort, because everything follows from the same rules. The Dagger’s tactical role, its crew size, its limitations, the scenarios it enables — none of that required invention. It came out of the trade-off.

The reader doesn’t need to understand the Transit physics to feel that the Dagger is real. They just need to encounter it behaving consistently with itself across the whole story. That consistency is what creates the texture that makes a fictional universe feel inhabited rather than constructed.

Thirty years of engineering taught me that coherent systems generate their own logic. Turns out that works in fiction too.

Why Engineers Write Better Hard SF is on The Unretired Engineer YouTube channel —

Stranded in the Stars, Book One of the Sea of Suns Trilogy, is available on Kindle. The Dagger appears early and often. https://www.amazon.com/Stranded-Stars-M-Harris-ebook/dp/B0GT123PLP

The Problem With AI Answers Is That They’re Almost Right

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AI slop isn’t obvious. That’s what makes it dangerous.

If an AI gave you complete nonsense, you’d catch it. The problem is when it gives you something fluent, confident, and “mostly” correct — with a flaw buried in the middle that you’ll only find if you already know the answer.

That’s the thing about AI as a research tool: it will give you the consensus view, coherently expressed, at the level of resolution that the training data supports. Where the training data is thin, ambiguous, or where real expertise requires distinguishing between things that *look* similar but aren’t — that’s where it fails. And it fails confidently.

Even when you use the deep research tools there are problems. When I was developing some content for my YouTube channel, The Unretired Engineer I ran into this doing research on Wolfspeed’s financial situation and the SiC power electronics market. I asked a deep research tool to pull together an analysis. What came back looked thorough. The problem was that it took a lot of information that had gone out about the future of the fab and future plans for markets and conflated them with what had happened and what was likely to happen in the near future.

To someone without a background with Wolfspeed and the real status of the SiC, the analysis would have read as authoritative. It wasn’t. It had serious timing errors delivered with confidence. I knew it was wrong because I’d spent years in that space. If I hadn’t, I might have taken it as written.

The fix isn’t to stop using it. The fix is to put yourself into it.

When I work with AI on my engineering writing, or on the physics underlying my novels, I’m not asking it to do the thinking. I’m using my domain knowledge to steer it, to catch the near-misses, and to push it past the consensus into territory where the expertise actually matters. The AI amplifies what I bring. Without that, it’s just averaging.

Use it as a tool. But know what it can’t know — and that’s usually the thing that matters most.

—

https://youtube.com/shorts/mbmKm_JcHQ0?feature=share

Mark Harris is a system and mechanical engineer and the author of “Stranded in the Stars” (Book One, The Sea of Suns Trilogy), available now on [Amazon](https://www.amazon.com/Stranded-Stars-M-Harris-ebook/dp/B0GT123PLP)