The corporate / academic / public AI value gap

There is a huge gap between the benefits of Artificial Intelligence the public is being sold, the benefits of AI which are being marketed to corporate adopters, and the actual motivations of AI researchers.

  • Tech providers pitch AI as a driver of innovation (self-driving cars) and global good (mitigating global warming).  But the B2B case-studies pitched to corporate clients more often pitch AI solutions as better automation, mostly enabling cost-reduction (specifically, reducing human-in-the-loop labor).
  • While many AI researchers are motivated by genuine interest in improving the human condition, other motivations diverge — a desire to push the bounds of what we can do, a genuine belief in transhumanism (the desire for AI to replace, or transform into something entirely unrecognizable, humanity), or simply because AI pays bigly.

These drivers — replacing human employment, and perhaps humans themselves — are, to put it mildly — not visions the public has bought into.

But these internal motivations are drowned out by the marketing AI pitch by which AI is sold to the public: “AI will solve [hunger/the environment/war/global warming]”.  This leaves the people not “in the know” about AI progress — 99% of the population — not even thinking to use democracy to direct AI research towards a world the (average) person actually wants to live in.

This is not particularly fair.

Marketed AI vs Profitable AI

To the public, the tech giants selling AI solutions (Google, Microsoft, and Apple) pitch visions of AI for good.  

The public face of these advertising campaigns is usually brand advertising, perhaps pitching consumers on a software ecosystem (Android, iOS), but rarely selling any specific product.  This makes it easy to sell the public a vision of the future in HDR, backed by inspirational hipster soundtracks.

You all know what I’m talking about — you’ve seen them on TV and in movie theaters — but the imagery is so honed, so heroic, that we should look at the pictures anyway.

Google’s AI will do revolutionary things, like fix farming, improve birthday parties, and help us not walk off cliffs: 

Microsoft’s AI is safe.  You can tell because this man is looking very thoughtfully into the distance:

But if that is not enough to convince you, here is a bird:

Microsoft goes into detail on their “AI for Good” page.  The testimonials highlight the power of AI as applied to:

  • Environmental sustainability (image recognition of land use, wildlife tracking, maximizing farm yields)
  • Healthcare (dredging through data to find diseases)
  • Accessibility (machine translation, text to speech)
  • Humanitarian action and Cultural Heritage preservation

Even the Chinese search engine Baidu, not exactly known for their humanitarian work, has joined the OpenAI “safe AI” consortium, which is nominally dedicated to developing and selling only safe AI.

The theme among all these public “good AI” initiatives — the sales pitch to the public — is:

“We’re developing advanced AI, but we’re partnering with NGOs, hospitals, and more, to make this AI work for people, not against them.  Look at all the good we can do!”

This isn’t fake.  Microsoft is working with nonprofits, NGOs, and more, to deploy for-the-people AI.  But these applications don’t get us closer to the real question:

“What solutions are normal companies actually deploying with AI-as-a-service cloud technology?”

We can peek behind the curtain at Amazon.  Amazon’s AWS has been for the last decade synonymous with “the cloud”, and still has a full 50% market share.  The bleeding edge of AWS are plug-and-play machine learning and AI tools: Amazon Forecast (machine learning), Amazon Polly (text to speech), Amazon Rekognition (video object recognition), Amazon Comprehend (natural language processing), and more.

And Amazon, alone and refreshingly among tech giants, doesn’t even pretend to care why their customers use AI:

“We certainly don’t want to do evil; everything we’ve released to customers to innovate [helps] to lift the bar on what’s actually happening in the industry. It’s really up to the individual organisation how they use that tech”

Amazon sells AI to C-suites, and we know what the hooks are, because the marketing pitches are online.  AWS publishes case studies about how their plug-and-play AI and ML solutions are used by customers. 

We can look at a typical example here, outlining how DXC used AWS’s ML and AI toolkits to improve customer service call center interactions.  Fair warning:  the full read is catastrophically boring — which is to be expected when AI used not to expand the horizon of what is possible… but instead used to excise human labor from work which is already being done:

“DXC has also reduced the lead time to edit and change call flow messaging on its IVR system. With its previous technology, it took two months to make changes to IVR scripts because DXC had to retain a professional voice-over actor and employ a specialized engineer to upload any change. With Amazon Polly, it only takes hours”

Using Amazon Connect, DXC has been able to automate password resets, so the number of calls that get transferred to an agent has dropped by 30–60 percent.

DXC anticipates an internal cost reduction of 30–40 percent as a result of implementing Amazon Connect, thanks to increased automation and improved productivity on each call.

In total, what did DXC do with its deployed AI solution?  AI is being used to:

  • Replace a voice-over actor
  • Eliminate an operations engineer
  • Eliminate customer service agents

There’s nothing evil in streamlining operations.  But because of the split messaging being used to sell AI research to the public vs to industry — on one hand, visions of environmental sustainability and medical breakthroughs, and on the other hand, the mundane breakthrough of applying a scalpel to a call center’s staffing — the public has little insight (other than nagging discomfort) into automation end-game.  

The complete lack of (organized) public anger or federal AI policy — or even an attempt at a policy — speaks to the success of this doublespeak.

Research motivations

So why are actual engineers and researchers building AI solutions?

I could dredge forums and form theories, but I decided to just ask on reddit, in a quick and completely unscientific test.  Feel free to read all the responses — I’ve tried to aggregate them here and distill them into the four main themes.  Weighted by upvotes, here’s the summary:

Preface: none of these are radical new revelations.  They match, in degrees, what you’d find with a more exhaustive dragnet of public statements, blogs, or after liquoring up the academic research mainstream.

Walking down the list:

1. Improving the human condition

A plurality goal is to better the human condition, which is promising.  An archetypal response is a vision of a future without work (or at least, without universal work):

“I believe the fundamental problem of the human race is that pretty much everyone has to work for us to survive.

So I want to work on fixing that.”

It’s not a vision without controversy — it’s an open question whether people can really live fulfilled lives in a world where they aren’t really needed — but at minimum it’s a vision many could get behind, and is at root predicated in a goal of human dignity.

2. It pays

Close behind are crude economics.  Top comment:

“Dat money”

I don’t intend to sound negative — capitalism is the lever which moves the world, and in capitalism, money follows value.  But as shown by AWS, value can come from either revolutionary invention (delivering novel value), or cost excision (delivering cheaper value).

Either direction pays the bills (and engineers), and few megacorp engineers care to peek behind the curtain at which specific aspect of the AI product delivered to clients pays the bills.

3. Transhumanism

Here’s where the interests of true believers in AI diverge from the mainstream.  Top comment:

“I don’t really care about modern ML solutions, I am only concerned with AGI. Once we understand the mechanisms behind our own intelligence, we move to the next phase in our species’ evolution. It’s the next paradigm shift. Working on anything else wouldn’t be worth it since the amount of value it brings is so vast.”

“I’m in it for the money” is just realism.  “A world without work” and “making cheddar” are motivations which appeal to the mainstream, and is at least comprehensible (if frustrating) to those whose jobs are on the line.  

Transhumanism is different.  There’s a prevalent (although possibly not majority) philosophy among many AI researchers, practitioners, and enthusiasts, that the goal of developing strong (human-level) AI is not a tool for humans, but an end unto itself.  The goal is the creation of a grander intelligence beyond our own:

“Let an ultraintelligent machine be defined as a machine that can far surpass all the intellectual activities of any man however clever. Since the design of machines is one of these intellectual activities, an ultraintelligent machine could design even better machines; there would then unquestionably be an ‘intelligence explosion,’ and the intelligence of man would be left far behind. Thus the first ultraintelligent machine is the last invention that man need ever make.”

Or, step-by-step:

  • Humans create AI 1.0 with IQ human + 1
  • AI 1.0 creates AI 2.0, which is slightly smarter
  • AI 2.0 creates AI 3.0, which is WAY smarter
  • AI 3.0 creates AI 4.0, which is incomprehensibly smarter

And whatever comes next… we can’t predict.

This is not a complete summary of transhumanism.  There’s a spectrum of goals, and widespread desire for AI which can integrate with humans — think, nanobots in the brain, neural augmentation, or wholesale digital brain uploads.  But either way — whether the goal is to retrofit or replace humans — the end goal is at minimum a radically transformed concept of humanity.

Given that we live in a world stubbornly resistant to even well-understood technological revolutions — nuclear power, GMOs, and at times even vaccines — it’s fair to say that transhumanism is not a future the average voter is onboard for.

4. Just to see if we can 

And just to round it out, a full 16% of the votes could be summarized (verbatim) as:

“Why not?”

Researchers — and engineers — want to build AI, because building AI is fun.  And there’s nothing unusual about Fun Driven Development.  Most revolutionary science doesn’t come from corporate R&D initiatives; it comes from fanatical, driven, graduate students, startups, or bored engineers hacking on side projects.

Exploration for the sake of exploration (or with a thin facade of purpose) is what got us out of trees and into lamborghinis.

But at the end of the day, “for the fun” is an intrinsic motivation akin to “for the money”.  The motivation gives one engineer satisfaction and purpose, but doesn’t weight heavily on the scales when answering “should this research exist?” — in the same way we limit fun of experimental smallpox varietals, DIY open-heart surgery, and backyard nuclear bombs

Misaligned motivations

The public has been sold a vision of AI for Good; AI as an ex machina for some (or all) of our global crises, now and future:

These initiatives aren’t fake, but they also represent a small fraction of actual real-world AI deployments, many if not most of which focus on selling cost-reductions to large enterprises (implicitly and predominantly, via headcount reductions).

AI researchers and implementers, in plurality, believe in the potential good of AI, but more frequently are in it for the money, to replace (or fundamentally alter) humans, or just for the fun of it.

The public, and their elected governments, can’t make informed AI policy if they are being sold only one side of the picture — with the unsavory facts hidden, and the deployment goals obscured.   These mixed messages are catastrophically unfair to the 99% of humanity not closely following AI developments, but whose lives will be, one way or another, changed by the release of even weak (much less, strong) AI.

Ethically Consistent Cryonics

John woke.  First wet, then cold.  

“Hello John.  I’m Paul Mmmdcxxvi”

Paul’s face drifted into focus.  

“You froze to death (by the standards of the time) climbing Everest in 2005.  You were chipped out of a glacier last week.  Thanks to recent medical advances, you defrosted with minimal tissue damage.”

Hrrrngh.  “Hrrrrng” 

“It is no longer 2005” Paul helpfully added.  “But I need to explain a few things, to frame the real date.  Take your time.  Take a drink.”  Paul gestured at a bottle on a nearby table.

John sat upright, drank, and after a few minutes, looked around.  

The room was dimly lit, but vast.  Behind Paul, extending as far as John could see, stood row upon row of barrels, stacked upwards into complete darkness.  Nearby barrels misted slightly.  Between rows, up in the air, crawled… something?  Somethings?  

Behind Paul blinked a single standard LCD monitor — the only clear light.

“How are you feeling, John?”  Paul prompted.

“Better.” Surprisingly well, John realized.  “So it’s not 2005.  When is it?”

“You missed a lot, so I’ll need to start where you left off.  I apologize if I’m scattered; please do interrupt”  Paul paused, and started:

“Our civilization had two great breakthroughs in the middle of the 21st century.  

“The first was moral.  The earth struggled with ethics in the early 21st century.  We had made great advances in mathematics and physics, but our moral calculus was stagnant by comparison.   Good people put together moral frameworks like Utilitarianism, concepts like Quality Years of Life saved, and tools to compute the time-decay discounts for future lives.  

“Gradually, a formal field of research emerged — computational ethics.  Many of our best minds went to work, and researchers constructed some truly heroic Google Sheet formulas.   But at the end of the day, they couldn’t avoid the inherent contradictions.”

“Contradictions?” John objected.  “I’m no mathematician, but before I died, I lived a decent life.  I still feel bad about stealing Pokemon cards in 5th grade.”

“Not surprising, for a 20th century mind.   But you were optimizing for observable, personal moral impact.  Computational ethics tried to be scientific about optimizing human morality.  For example: how could you justify eating premium Ostrich burgers, while UNICEF struggled to fundraise for famine relief?”

“Well” John admitted, “I assumed my tax dollars mostly took care of that.”

“Naturally.  And that’s just the tip of the iceberg.  We started with the “easy” optimization, maximizing Quality Years of Life.  It worked well at first; we eliminated Polio, hunger, and male-pattern-baldness.  But we got stuck.  It turned out there was no way to optimize out suicide, but leave BASE jumping as a optional joie de vivre.  Or leave potato chips.  Or leave the fun game where teenagers shoot fireworks at each other.”

John mindlessly scratched at the old burns where his left ear once grew.  “That’s a shame, I had a really fun childhood.”

“But it got even worse.  When we ran the numbers, there was no consistent system which condemned murder but condoned voluntary vasectomies.  The average vasectomy destroyed billions of QALYs, by eliminating children, children of children, their grandchildren…”

Wait, what?  “Well, that’s ridiculous.  One is killing a real person, and one is just eliminating a hypothetical future.  If you evaluate your moral utility as a delta from what you could have done instead, you’re going to go crazy.”

“That’s what we eventually figured out.  Science would have moved much faster if gifted with clear thinkers like you.  So we cut all that ‘possible futures’ stuff out, and settled on a simple axiom.”

“Axiom?”  Is this some geometry thing?  “ I just figure that dying is bad, much worse than the other bad stuff.”

“Exactly.  That’s where we landed: ‘People dying is bad’.  It’s been our guiding law, in the few thousand centuries since.  We’ve rebuilt society accordingly.”

That seems fine.  John figured.  Wait.  The “thousand centuries” thing is concerning.  Paul seems like a nice guy, but storytelling isn’t his forte.  “Speaking of which, where is everyone?  Why is it so cold?  And what’s with all the storage tanks?  Why’d you defrost me in an Amazon warehouse?

“That gets me to the second great breakthrough: cryo-sleep.  

“You were incredibly lucky — most people who fell headfirst and naked into a glacier in 2005 ended up a decomposed mess.  But in the 2030s, we perfected supercooled nitrogen bath immersion, and could reliably freeze freshly-dead bodies, with minimal to no risk.”


“Cosmetic tissue loss, and nobody’s going to win any Nobel prizes.  But the point is, we can stop people from fully dying.  Once we figure out how to fix what killed someone, we can defrost them, fix them, and send them back out to enjoy a full life.

Huh.  “That’s really the dream, then.  Live fast, die young, and …”  



“Eventually, perhaps.  But if you die too fast, nobody can put you back together.  We could save the gunshot victims, and stop some heart attacks.  But you know what they say — all the king’s horses and all the king’s men, can’t really do much, when Humpty’s parachute strings get tangled… so we couldn’t justify letting thrill-seekers skydive.”

Ok, so the future is kind of boring.  I can live with that, I guess. “So what do people do for fun now-days, if the risky stuff is off the table?”

“I’m getting there.  There was a bigger unsolved problem, a nut we haven’t cracked yet.  I’ve personally been working on it for the last forty years.”


“Old age.  Even if you eliminate the dumb deaths like snakes, train crashes, and undeployed parachutes, eventually, people just break down.  When a person makes it to the end — around 120 is the best we ever did— we run out of ways to keep them ticking.

“It’s likely fixable.  But it’s possible that we won’t ever be able to reverse aging, only forestall it.   Thermodynamics is a bitch.  So we decided it’s ethically unsound to ever let a person die of old age.  Cryo-sleep isn’t forever, but death from old age might be.   So we head it off.  When someone hits 80, they go into storage, and stay there until we’ve figured out how to stop aging”

That’s neat, but I’m only 30, and I’m also recently defrosted, cold, and hungry.  This doesn’t seem super important.  “I really appreciate this backstory, but I’d appreciate the cliff notes.  Is there anyone else who could swap in?”

“I’m getting there.  There isn’t anyone else.”

… fuck?

“We almost figured it out too late — by the late 21st century, the vast majority of our energy infrastructure was dedicated to cryogenics.  The more people who kept breathing, the more people who kept getting older.  When they hit 80, they filled up scarcer and scarcer cryo tanks.  

“We only had a few decades left before we hit Peak Cryo.  And if we run out of places to store people, it’ll be a catastrophe — we’ll have to pick and choose who to defrost and recycle.  We can’t let that happen!

“Obviously we can’t just give up on fixing ageing.  Everyone would end up dead!  But it doesn’t make sense to parallelize that research — haven’t you read The Mythical Man-Month?  We couldn’t waste effort — every hour counts.”

Ugh, I get it.  “So you froze everyone.  To stop the clock.”

“Precisely.  Some people were unhappy, but most understood the logic — that it was the only possible choice, given our consistent moral foundation.”

Being dead had benefits.  “I suppose that’s an insane but rational choice.  So how close are we…  you…  to actually solving ‘old age’?”

“Me? Honestly, I gave it an honest effort for a decade, but found the biology all very confusing.  I was just a furniture salesman before this, you know?  I’ve spent the last 25 years mostly organizing my predecessor’s notes, in the hopes I could give you a head start.”

John blinked several times, and then several more.  “Me?”

“Of course, you.  It’s really just dumb luck the excavator-bots dug you up (while freeing up some storage space on the north slope) right before I retired.  You’re the youngest person alive —or whatever — by a solid decade, so you’re next in line.

“It’s totally natural to not feel up to the task.  But don’t sweat it — it’s not a huge setback if you fail.  Once you’ve put in 50 years of the good-old-college-try, you’ll get retired, someone fresh will swap in.”

Uh, yeah, prepare for disappointment.   “And if it takes longer than that?  What if nobody currently ‘alive’ can solve it?”

“Worst case, we have a system for making new people, of course.  It was the first thing we developed.  But we won’t start unless it’s a true catastrophe, and run out of existing people.  Given storage constraints, it’s irresponsible to go around making more people except as a last resort.”

I read some sci-fi back in college.  Surely there’s a less-stupid way out of this.  “What about storing people off-world?  Maybe on the moon?”

“Dangerous.  Here let me — ah, I have the notes”  Paul swiveled back to the monitor.   “My predecessor spent a few years on this, and couldn’t figure out how to deal with the potential micrometeorite damage.  But I’d certainly encourage you to take a shot.  Fresh neurons, fresh ideas!”

Well.  “And if I say no?”

“I can tell you’re struggling.  It’s ok.  This is why decisions in the 21st century were so hard.  Your people had no real moral axis!

“If you say no, obviously there are backup plans.  The automation” Paul gestured up at the tangle of arms and wires menacingly crawling between stacks of barrels — cryo-tanks, John realized —  “would simply re-freeze you and put you in the back of the queue.  It would be quite unpleasant, but you have my promise you’d survive.  Someday we’ll all get defrosted and have a good laugh about it.”

Paul slowly rose, his right arm on a cane, waving his left arm as if to hail a cab.  “I don’t like to cut this off so soon.  But I’ve already given you far more time than the norm thanks to the unusual circumstances of your defrosting, and I really shouldn’t linger.  It’s far too dangerous, at my age.”

The closest mess of wires enveloped and lifted Paul as he shook John’s hand.

“Take your time and mull it over.  Just shout, and the help” (gesturing to the nearest arm) “will point you to a shower and a hot meal.  The research can wait until you’re ready.   One nice benefit of our ethical framework, is that no decision ever has to be rushed.”  

The wires disappeared quietly in the dark.  Uncomfortably alone, John stared at the monitor blinking in front of him.  This is the stupidest possible futureAt least Terminator had an awesome robot uprising.  

But at the same time, and what have I got to lose?  The future is already stupid, and I certainly can’t make it any worse.  I deserve a shower and breakfast first.  I can take a crack at this for at least a day.  I can try anything for a day.  It can’t be much worse than being properly dead.   

And it’s not like a bit of hard work is going to kill me John admitted, because dying is absolutely not an option anymore.

Peacetime Bureaucracy / Wartime Bureaucracy

Peacetime bureaucracy forces all tests through the CDC.  Wartime bureaucracy allows hospitals to run their own tests.

Peacetime bureaucracy priorities food labeling rules over functional supply chains.  Wartime bureaucracy gets food out of fields before it rots.

Peacetime bureaucracy creates ethical review boards.  Wartime bureaucracy allows volunteers to infect themselves to test vaccines.

Peacetime bureaucracy inventories ICU beds.   Wartime bureaucracy builds hospitals.

Peacetime bureaucracy certifies medical-purpose N95 respirators.  Wartime bureaucracy uses construction masks.

Peacetime bureaucracy waits for double-blind, controlled efficacy studies. Wartime bureaucracy tells shoppers to wear masks.  

Peacetime bureaucracy prioritizes fraud prevention.  Wartime bureaucracy writes checks.

Peacetime bureaucracy plays diplomatic games to stay funded.  Wartime bureaucracy takes advice from Taiwan.

Peacetime bureaucracy considers every life sacred.  Wartime bureaucracy balances QALYs saved against the price tag.

Peacetime bureaucracy prioritizes racially sensitive nomenclature.  Wartime bureaucracy stops international flights.

Peacetime bureaucracy requires HIPAA certification for telemedicine.  Wartime bureaucracy lets doctors use Skype.

Peacetime bureaucracy optimizes for eventual correctness.  Wartime bureaucracy treats time as the enemy.

Peacetime bureaucracy optimizes for public support in the next election cycle.  Wartime bureaucracy has a long-term plan.

Investors know the difference between peacetime CEOs and wartime CEOs, and trade them out when times demand change.  How do we build institutions which quickly exchange peacetime bureaucracy for wartime bureaucracy? 

Two months into COVID-19, we’re barely halfway there. Next decade (or next year) there will be a next disaster, war, or pandemic.  When that happens, we need wartime officials ready to act — not peacetime officials reluctantly easing into the role.  These public officials must be able to make hard choices with incomplete information.

We need to learn from COVID-19, but the preparation can’t stop at “stockpile masks and ventilators”. Preparation means having officials ready to eliminate red tape, make new rules, and make hard choices on day 1 — not day 30.

We got lucky this time, a trial-run on a pandemic whose victims are (predominantly) the old and sick. To fail utterly at curbing COVID-19 precipitates an ethical, but not civilizational, failure.

We’re unlikely to be so lucky next time. The future is full of darker, bloodier pandemics than COVID-19 — both natural ones, and man-made ones. When one strikes (and it will) we need a wartime bureaucracy and a playbook ready, telling us which of the old rules still matter — and which rules will not.


It’s easy to think without reading, but also easy to read without thinking.

I’ve started reading nonfiction again.  I have a good reason for stopping: I was stuck halfway through Proofs and Refutations for about a year and a half, and as a committed completionist, I couldn’t start any other books until it was done.  After powering through the dregs of P&R, I know a lot about…. proofs, less about polyhedrons, and I’m free to re-engage in educational literature.

It’s easy to read without reflecting, though.  I’d venture that 90% of “consumed content by volume” — especially online content — functions only to:

  1. Reinforce biases, in an (optimistically) intellectual circlejerk
  2. Get the reader frothing mad when they Read Stupid Opinions by Stupid People

I don’t think I’m uniquely bad at “intellectually honest reading” —  but “median human” is a low bar, and not one I’m confident I always clear.  If I’m going to go through the motions of reading brain books, I need a forcing function to ensure the input actually adjusts my priors;  if after having read a book, I haven’t changed my mind about anything, I’m wasting my time on comfortable groupthink.

My forcing function — until I get tired of doing it — will be to write something here.  There may be inadvertent side-effects (like accidentally reviewing the book, although I hope not), but my only commitment is: to outline at least one stance, large or small, the book has changed my mind on.  Or, lacking that, forced me into an opinion on a topic I hadn’t bothered to think about.

If I can’t find one updated stance, I’m wasting my time. Committing that stance to writing forces crystallization, and committing that writing to a (marginally) public audience forces me to make the writing not entirely stupid.

I make no commitment to keeping this up, but I waited to write this until I had actually written an un-review, so at least n=1, and by publicly declaring a plan, I can (hopefully) guilt myself into maintaining the habit.

Schrödinger’s Gray Goo

Scott Alexander’s review of The Precipice prompted me to commit to keyboard an idea I play with in my head: (1) The biggest risks to our humanity the ones we can’t observe, because they are too catastrophic to survive, and (2) we do ourselves a disservice by focusing on preventing the catastrophes we have observed.

Disclaimer: I Am Not A Physicist, and I’m especially not your physicist.

1. Missing bullet holes

The classic parable of survivorship bias comes from the Royal Air Force during WWII.  The story has been recounted many times

Back during World War II, the RAF lost a lot of planes to German anti-aircraft fire. So they decided to armor them up. But where to put the armor? The obvious answer was to look at planes that returned from missions, count up all the bullet holes in various places, and then put extra armor in the areas that attracted the most fire.

Obvious but wrong. As Hungarian-born mathematician Abraham Wald explained at the time, if a plane makes it back safely even though it has, say, a bunch of bullet holes in its wings, it means that bullet holes in the wings aren’t very dangerous. What you really want to do is armor up the areas that, on average, don’t have any bullet holes.

Why? Because planes with bullet holes in those places never made it back. That’s why you don’t see any bullet holes there on the ones that do return.

The wings and fuselage look like high-risk areas, on account of being full of bullet holes.  They are not. The engines and cockpit only appear unscathed because they are the weakest link.  

2. Quantum interpretations

The thought-experiment of Schrödinger’s cat explores possible interpretations of quantum theory:

The cat is penned up in a steel chamber, along with the following device: In a Geiger counter, there is a tiny bit of radioactive substance, so small, that perhaps in the course of the hour one of the atoms decays, but also, with equal probability, perhaps none; if it happens, the counter tube discharges and through a relay releases a hammer that shatters a small flask of hydrocyanic acid. If one has left this entire system to itself for an hour, one would say that the cat still lives if meanwhile no atom has decayed. The first atomic decay would have poisoned it. 

Quantum theory posits that we cannot predict individual atomic decay; the decay is an unknowable quantum event, until observed.  The Copenhagen interpretation of quantum physics declares that the cat’s state is collapsed when the chamber is opened — until then, the cat remains both alive and dead.

The many-worlds interpretation declares the opposite — that instead, the universe bifurcates into universes where the particle did not decay (and thus the cat survives)  and those where it did (and thus the cat is dead).

The many-worlds interpretation (MWI) is an interpretation of quantum mechanics that asserts that the universal wavefunction is objectively real, and that there is no wavefunction collapse. This implies that all possible outcomes of quantum measurements are physically realized in some “world” or universe.

The many-worlds interpretation implies that there is a very large—perhaps infinite—number of universes. It is one of many multiverse hypotheses in physics and philosophy. MWI views time as a many-branched tree, wherein every possible quantum outcome is realised. 

3. The view from inside the box

The quantum suicide thought-experiment imagines Schrödinger’s experiment from the point of view of the cat.  

By the many-worlds interpretation, in one universe (well, several universes) the cat survives.  In the others, it does. But a cat never observes universes in which it dies. Any cat that walked out of the box, were it a cat prone to self-reflection, would comment upon its profound luck. 

No matter how likely the particle was to decay — even if the outcome was rigged 100 to 1 — the outcome remains the same.  The cat walks out of the box grateful to its good fortune.

4. Our box

Or perhaps most dangerously, the cat may conclude that since the atom went so long without decaying, even though all the experts predicted decay, the experts must have used poor models which overestimated the inherent existential risk.

Humans do not internalize observability bias.  It is not a natural concept. We only observe the worlds in which we — as humans — exist to observe the present.  Definitionally, no “humanity-ending threat” has ended humanity.   

My question is: How many extinction-level threats have we avoided not through calculated restraint and precautions (lowering the odds of disaster), but through observability bias?

The space of futures where nanobots are invented is (likely) highly bimodal; if self-replicating nanobots are possible at all, they will (likely) prove a revolutionary leap over biological life.  Thus the “gray goo” existential threat posited by some futurists:

Gray goo (also spelled grey goo) is a hypothetical global catastrophic scenario involving molecular nanotechnology in which out-of-control self-replicating machines consume all biomass on Earth while building more of themselves

If self-replicating nanobots strictly dominate biological life, we won’t spend long experiencing a gray goo apocalypse.  The reduction of earth into soup would take days, not centuries:

Imagine such a replicator floating in a bottle of chemicals, making copies of itself…the first replicator assembles a copy in one thousand seconds, the two replicators then build two more in the next thousand seconds, the four build another four, and the eight build another eight. At the end of ten hours, there are not thirty-six new replicators, but over 68 billion. In less than a day, they would weigh a ton; in less than two days, they would outweigh the Earth

Imagine a world in which an antiBill Gates stands with a vial of grey goo in one hand, and in the other a geiger counter pointed at an oxygen-14 molecule — “Schrödinger’s gray goo”.  Our antiBill commits to releasing the gray goo the second the oxygen-14 molecule decays and triggers the geiger counter.

In the Copenhagen interpretation, there’s a resolution.  The earth continues to exist for a minute (oxygen-14 has a half-life of 1.1 minutes), perhaps ten minutes, but sooner or later the atom decays, and the earth is transformed into molecular soup, a giant paperclip, or something far stupider.  This is observed from afar by the one true universe, or perhaps by nobody at all.   No human exists to observe what comes next. [curtains]

In the many-worlds interpretation, no human timeline survives in which the oxygen-14 model decays. antiBill stands eternal vigil over that oxygen-14 atom: the only atom in the universe for which the standard law of half-life decay does not apply.

5. Our world

As a species we focus on preventing and averting (to the extent that we avert anything), the risks we are familiar with:

  • Pandemics
  • War (traditional, bloody)
  • Recessions and depressions
  • Natural disasters — volcanoes, earthquakes, hurricanes 

These are all bad.  As a civilization, we occasionally invest money and time to mitigate the next natural disaster, pandemic, or recession.

But we can agree that while some of these are civilizational risks, none of them are truly species-level risks.  Yet we ignore AI and nanotechnology risks, and to a lesser but real degree, we ignore the threat of nuclear war.  Why though?

  • Nuclear war seems pretty risky
  • Rogue AI seems potentially pretty bad
  • Nanobots and grey goo (to the people who think about this kind of thing) seem awful

The reasoning (to the extent that reasoning is ever given) is: “Well, those seem plausible, but we haven’t seen any real danger yet.  Nobody has died, and we’ve never even had a serious incident”

We do see bullet holes labeled “pandemic”, “earthquake”, “war”, and we reasonably conclude that if we got hit once, we could get hit again.  Even if individual bullet holes in the “recession” wing are survivable, the cost to human suffering is immense, and worth fixing.  Enough recession/bullets may even take down our civilization/plane. 

But maybe we are missing the big risks, because they are too big.  Perhaps there exist fleetingly few timelines with a “minor grey goo incident” which atomizes a million unlucky people.  Perhaps there are no “minor nuclear wars”, “annoying nanobots” or “benevolent general AIs”. Once those problems manifest, we cease to be observers.

Maybe these are our missing bullet holes.

6. So, what?

If this theory makes any sense whatsoever — which is not a given — the obvious followup is that we should make a serious effort to evaluate the probability of Risky Things happening, without requiring priors from historical outcomes. Ex:

  • Calculate the actual odds — given what we know of the fundamentals — that we will in the near-term stumble upon self-replicating nanotechnology
  • Calculate the actual odds — given the state of research — that we will produce a general AI in the near future?
  • Calculate the actual odds that a drunk Russian submariner will trip on the wrong cable, vaporize Miami, and start WWLast?

To keep things moving, we can nominate Nicholas Taleb to be the Secretary of Predicting and Preventing Scary Things.  I also don’t mean to exclude any other extinction-level scenarios. I just don’t know any others off the top of my head.  I’m sure other smart people do.

If the calculated odds seem pretty bad, we shouldn’t second guess ourselves — they probably are bad.  These calculations can help us guide, monitor, or halt the development of technologies like nanotech and general AI, not in retrospect, but before they come to fruition.

Maybe the Copenhagen interpretation is correct, and the present/future isn’t particularly dangerous.  Or maybe we’ve just gotten really lucky.  While I’d love for either of these to put this line of thought to bed, I’m not personally enthused about betting the future on it.

Ineffective Altruism

Texas Lt. Gov. Dan Patrick hung up a morality piñata when he had the audacity to state, on record, that he’d be willing to take risks with his life to prevent an economic meltdown:

No one reached out to me and said, ‘As a senior citizen, are you willing to take a chance on your survival in exchange for keeping the America that all America loves for your children and grandchildren?’ And if that is the exchange, I’m all in

Like many other moral-minded folk, Cuomo took a free swing at the piñata a few hours later, and snapped back with this zinger:

While this tweet is genetically engineered to be re-twatted, it is a ridiculous statement.  People put prices on human life all the time. Insurance companies price human lives. Legislators do it all the time when enacting regulations meant to protect human life, at a cost. 

On the flip side, it’s common to price out the cost of saving a life as well.  Effective Altruism is a niche but deeply principled movement which goes through great lengths to, with exacting rigor, price out the most effective way to save lives (and then, generally, donate 80% of their salary, time, and organs to those causes).

GiveWell is one of them.  They annually put together a version of this spreadsheet which calculates which charities are able to do the most work with the fewest dollars. 

It’s worth checking out. The math is more complicated than a naive observer would expect. It turns out that the shittiest things nature can do to a person often doesn’t kill them — Hookworms reduce educational attainment, blind children (!), and reduce life earnings, but rarely if ever… kill anyone. But because being blind and poor really sucks, many of GiveWell’s “most effective” charities attempt to eliminate hookworms and similar parasites.  

The way to massage this math onto a linear scale is to compute dollars per QALY saved, where QALY stands for Quality Adjusted Life Year — the equivalent of one year in perfect health.  By this measure, saving the life of an infant who would otherwise die in childbirth may save 75 QALYs, while saving the life of a senile, bedbound 80 year old may save 15 Quality Adjusted Life Hours.

This is a reasonable and principled method of making financial investments.  If you put stock in the Efficient Politics Hypothesis of government, stop here and feel good about the choices we’ve made.


We have decided to, as a nation, spend an indefinite amount of time-money at home eating ice cream and mastrubating to Netflix, in a crusade to stop* the spread of COVID-19.  

*by some difficult to define metric

How much time-money?  $2.2 trillion is the down-payment stimulus package.  It’s a very conservative lower-bound estimate of the cost of recovery (it’s not expected that this will fix the economy by any means), so we can run with it.

How many lives are we saving?  The high upper bound is 2.2 million (assuming a 100% infection rate (not generally considered realistic), with a fatality rate of .66% (best estimate)).

This works out to a conveniently round, very lower bound, $1,000,000 per life saved (in the outcome that the US quarantine does prevent the majority of those deaths).  What about those QALYs? I’m not going to try sum it out, but we can look at a few suggestive stats:

  • The average age at death (due to COVID-19) in Italy is 79.5.  Italy’s average life expectancy is, for reference, 82.5*
  • 99% of Italian COVID-19 victims had pre-existing conditions (same source).

*I understand that the average life expectancy of a living 79 year old is higher than 82, which is why I’m not doing the math, so please shut up so we can move on.

We can speculate that no, we will not be saving very many QALYs.  But we can use the crude ‘lives saved’ metric instead to generously lower-bound our math, and run with 2.2 million raw.

Effectiver Altruism

My question: how does this calculus compare to effective altruism?  I was genuinely curious, because $1,000,000 per life saved is somewhat disproportionate to the charity pitches you see on television:


“Save a Child for Only $39 per day*”

Exit right.

*assuming 70 year life expectancy @ $1,000,000 per life

I tried to find a toplist of “problems we can solve for X dollars to save Y lives per year”.  I did not find one. GiveWell (entirely reasonably) calculates the payout of donating to a specific charity, not of speculatively eliminating entire facets of human suffering.

So I put together a list.  These numbers aren’t precise.  They are very speculative.  My goal was to understand the orders of magnitude involved.

My focus was on problems we could solve that don’t involve serious tradeoffs, and don’t require hard political choices.  Trying to solve “war”, “suicide”, or “alcoholism” don’t cost money per se, they require societal committment we can’t put a price tag on.  For the most part, this leaves diseases.

I started with the highest-preventable-death diseases in the developing world, and ended up with 7 “campaigns” where we could non-controversially plug in money on one end, and pull extant wretched masses out of the other.   When considering the payout in lives saved from eradicating a disease, I used 30 years, because using “forever” is unfair (I’m sure there’s a time-decay value on life an actuary would prefer, but this was simple, and it doesn’t really change the conclusion).

Global Hunger

Hunger is the stereotypical “big bad problem”, and it wasn’t hard to find data about deaths:

Around 9 million people die of hunger and hunger-related diseases every year, more than the lives taken by AIDS, malaria and tuberculosis combined.

(for the record, this gave me some good leads on other problems).  How much would it cost to actually fix hunger?

Estimates of how much money it would take to end world hunger range from $7 billion to $265 billion per year.  

Pulling the high estimate, we get… 

Price Tag$265 billion
Lives Saved9,000,000
Cost Per Life$29,444 / life 


Malaria sucks, and a lot of smart people want to spend money to get rid of it.  How many people does Malaria kill?

In 2017, it was estimated that 435,000 deaths due to malaria had occurred globally

What would it take to actually eliminate Malaria?

Eradicating malaria by 2040 would cost between $90 billion and $120 billion, according to the Gates Foundation

We can highball this estimate to get …. 

Price Tag$120 billion
Lives Saved13,050,000
Cost Per Life$9,195 / life


Tuberculosis is still a huge killer in the developing world, but it’s a killer we can put rough numbers on:

The Lancet study said reducing tuberculosis deaths to less than 200,000 a year would cost around $10 billion annually… a chronic lung disease which is preventable and largely treatable if caught in time, tuberculosis is the top infectious killer of our time, causing over 1.6 million deaths each year.

Price Tag$10 billion
Lives Saved42,000,000
Cost Per Life$7,143 / life

The math here is fuzzier than I’m comfortable with, but works out in the same ballpark as Malaria, so I feel OK about the result.


Again, this wasn’t the cleanest math puzzle, but this report pegs the cost of ending AIDs at $26.2 billion/year for 16 years.  At 770,000 deaths per year from AIDs, we can (again, more mathematically than I like, ballpark the bill and lives saved over 30 years:

Price Tag$366.8 billion
Lives Saved10,780,000
Cost Per Life$33,963 / life

Maternal mortality

Like Tuberculosis, it ends up in the same ballpark as Malaria, so I’m inclined to believe it’s not more than half-asinine.

Dying in childbirth is bad, and kills people.  How much public health spending would it take to eliminate it?  Again, it was really hard to find good estimates, but we find that 

Researchers at UNFPA and Johns Hopkins University calculated that the annual cost of direct services, such as paying for medical staff, drugs and supplies when a woman is giving birth, will reach $7.8bn (£6.2bn) by 2030, up from an estimated $1.4bn last year.

To save how many lives?  

About 295 000 women died during and following pregnancy and childbirth in 2017

I’m honestly not sure how to compare these numbers, but if we ballpark that the $7.8 billion saves at least that number (?) each year, we work out to 

Price Tag$7.8 billion
Lives Saved295,000
Cost Per Life$26,440 / life

If you don’t like the fuzziness of the math, feel free to ignore it, or multiply it by 10.  Or whatever.


Measles is bad.  To convince you to vaccinate your children, I will attach a picture of a child with measles.

In the US, measles doesn’t flat-out kill many people, but in the developing world, it does:

Worldwide more than 140,000 people died from measles in 2018

What would it cost to actually eradicate measles?

Eradicating measles by 2020 is projected to cost an additional discounted $7.8 billion and avert a discounted 346 million DALYs between 2010 and 2050

Using the 30 year window I’ve been using, we end up with:

Price Tag$7.8 billion
Lives Saved140,000
Cost Per Life$1,857 / life

This is a shockingly low number, and can only conclude either that (1) I messed something up, or (2)  that we are a terrible, horrible, species for not having eradicated this decades ago.

Global Warming

Stepping outside of diseases, what about something big?  Global warming is big.

How many deaths might be attributed to climate change in the next century?  Obviously this is a make-believe number, but the number is definitely at least ‘several’:  

A report on the global human impact of climate change published by the Global Humanitarian Forum in 2009, estimated more than 300,000 deaths… each year

This is the lowest bound I can find short of flat-out AGW denialism.  It’s easy to find genocide-level projections, assuming crop failures in the developing world several orders of magnitude higher.  I won’t use them.

What’s the cost of fixing global warming?  Long-term, there’s no good answer yet, because the technology doesn’t exist.  But there are (speculative) ways we can slow it down for reasonable sums via carbon sequestration: 

Returning that land to pasture, food crops or trees would convert enough carbon into biomass to stabilize emissions of CO2, the biggest greenhouse gas, for 15-20 years… With political will and investment of about $300 billion, it is doable

We can use these numbers to price tag the cost/payoff of delaying global warming:

Price Tag$300 billion
Lives Saved6,000,000
Cost Per Life$50,000 / life

This is the most speculative guesstimate of all, so if you want to ignore it too, feel free.

Compare & Contrast

My original goal was to build a snarky visualization game which invited users to bin-pack global problem solving which worked out to less than $2T.  I was foiled, because you could do literally everything on this list for less — by my (fuzzy) calculations, you could solve global hunger*, malaria, tuberculosis, delay global warming 20 years, cure AIDs, eliminate maternal mortality, and eliminate measles, for “only” $1.4T.

*to be fair, this one is annual, not a permanent elimination.

But I had already invested the time learning how to use Data Studio, so I made the chart anyway:

(you can play with it yourself here)


What I feel confident saying — even using wildly generous numbers, since I am:

  • using the absolute upper bound for US COVID-19 deaths,
  • using crude deaths for a disease which primarily affects the elderly, instead of QALYs when comparing to diseases which affect primarily the young,
  • using just one ($2.2T) of many recovery packages we’re going to pay for, and
  • generously upper/lower bounding all the numbers

is that 

The COVID-19 economic shutdown is 20x as expensive per life as any other public health intervention the US could fund.  The most expensive intervention on this list — “delaying global warming” — cost $50,000/head.  We’re paying $1,000,000/head for COVID-19.

Now, there is a range of valid value statements, depending on your priors and beliefs in how creative fiscal policy can be:

  • “We should do both”
  • “We don’t have money to do either”
  • “Maybe civilization was a bad idea”

I’m not claiming to be a superhero here.  I’m not an Effective Altruist, and probably don’t register as an altruist at all.  But cheap platitudes annoy me, especially when used to shut down arguments.

In the end, the most meaningful, easiest, way Cuomo could have qualified his Tweet would have been 

We will not put a dollar value on American life

It’s not a great look, or a great tweet.  But as far as I can tell, it’s the only way to make the numbers — maybe — add up.

You Should be Angry

If you are under the age of 30, in the west, your future has turned grim.  In a fair world, you would be in the streets, screaming to get back to your job and building your life.

But instead, you are inside, while the police sweep empty streets.

As of late March 2020, the economies of the US, and most of western Europe, have been shut down.  This action does not have precedent, and it will cripple a generation in poverty and debt. Short term, this will likely mean 20% unemployment, vast GDP contraction, and trillions in debt.

This price will be paid by those under 30, to save — some of — those over 80.

It is not necessary, and is not worth the price.  It was an instinctive reaction, and I hope history will not be kind to the politicians who caved to it.  The best time to stop this mistake was before it was made. 

The second best time is right now.

You are being lied to

We have been asked to shut down the US for two weeks — and similar timeframes in Italy, France and elsewhere.  Two weeks (15 days, per the Feds) is a palatable number. Two weeks is a long Christmas break.  The technorati elite on Twitter think the shutdown is a vacation, and for them it is, because their checking accounts are overflowing from the fat years of the 2010’s.

Two weeks is not the goal, and it never was the goal.

The Imperial College report is the study which inspired the shutdowns — first of the Bay Area, then all of California, then New York.   This report measured the impact of various mitigation strategies. For those not “in the know” (aka, normal humans) there are two approaches to treating this pandemic:

  • Mitigation, where we “flatten the curve” enough to keep ICUs full, but not overflowing.  Eventually, we will build up herd immunity, and disease persists at a low level.
  • Suppression, where we eliminate the disease ruthlessly and completely.  

You don’t have to read the paper.  This graph tells you everything you need to know:

The orange line is the optimal “mitigation” strategy.  We try to keep ICUs full, try to keep businesses and schools running, and power through it.  But people will die.

The green line is suppression.  We shut down businesses, schools, universities, and all civic life.  Transmission stops, because there is no interaction with the outside world.  The economy does not depress — it stops.

We aren’t following the orange line, because: people will die.

That is the IC report’s conclusion: no amount of curve flattening gets us through this pandemic in a palatable timeframe.  Thus, we must suppress — for 18 months or longer — until we have a vaccine.  I’m not paraphrasing. This is the quote:

This leaves suppression as the preferred policy option…. this type of intensive intervention package … will need to be maintained until a vaccine becomes available (potentially 18 months or more)

Italy, France, California, New York, Illinois, and more in the days to come, have nearly shuttered their economies.  All schools, universities, and social gatherings are cancelled, at risk of ostracization or police enforcement. This is the green line.

By enacting the green line — closing schools, universities, and businesses — the US is choosing to give up on mitigation, and choose suppression.  This doesn’t mean 2 weeks of suffering. It means 2 years to start, and years of recession to follow.

We are eating the young to save the unhealthy old

COVID-19 does not kill, except in the rarest of exceptions, the young.   Old politicians will lie to you. The WHO and CDC will lie to you — as they lied about masks being ineffective — to nudge you to act “the right way”.  Do not trust them.

Here are the real, latest, numbers:

In South Korea, for example, which had an early surge of cases, the death rate in Covid-19 patients ages 80 and over was 10.4%, compared to 5.35% in 70-somethings, 1.51% in patients 60 to 69, 0.37% in 50-somethings. Even lower rates were seen in younger people, dropping to zero in those 29 and younger.

No youth in South Korea has died from COVID-19.  Fleetingly few of the middle aged. Even healthy seniors rarely have trouble.  The only deaths were those seniors with existing co-morbidities.  In Italy, over 99% of the dead had existing illnesses:

With the same age breakdown for deaths as South Korea:

As expected, the numbers for the US so far are the same:

More than raw numbers, the percent of total cases gives a sense of the risk to different age groups. For instance, just 1.6% to 2.5% of 123 infected people 19 and under were admitted to hospitals; none needed intensive care and none has died… In contrast, no ICU admissions or deaths were reported among people younger than 20.

These numbers are under-estimates — the vast majority of cases were never even tested or reported, because the symptoms don’t even exist in many of the healthy.   The vast majority of the young would not even notice a global pandemic, and none — generously, “fleetingly few” would die.

The young — the ones who will pay for, and live through, the recession we have wrought by fiat — do not even benefit from the harsh medicine we are swallowing.  But they will taste it for decades. 

This is not even necessary

To stop COVID-19, the west shut itself down.  East Asia did not. East Asia has beaten COVID-19 anyway.

China is where the disease started (conspiracy theories aside).  Through aggressive containment and public policy, the disease has been stopped.  Not even mitigated — stopped:

There are two common (and opposite) reactions to these numbers:

  1. China is lying.  This pandemic started on Chinese lies, and they continue today.
  2. China has proven that the only effective strategy is containment

Neither is true.  But we also know that China can, and has, used measures we will never choose to implement in the west.  China can lock down cities with the military. China can force every citizen to install a smartphone app to track their movement, and alert those with whom they interacted.

So we can look at the countries we can emulate:  South Korea, Japan, Taiwan, and Singapore.  None (well, at most one) of them are authoritarian.  None of them have shut down their economies. Everyone one of them is winning against COVID-19.

South Korea

South Korea is the example to emulate.  The growth looked exponential — until it wasn’t:

South Korea has not shut down.  Their economy is running, and civic life continues, if not the same as normal, within the realm of normal.  So how did they win, if not via self-imposed economic catastrophe?  Testing.

The backbone of Korea’s success has been mass, indiscriminate testing, followed by rigorous contact tracing and the quarantine of anyone the carrier has come into contact with

Their economy will suffer not because of a self-imposed shutdown, but because the rest of the world is shutting itself down. 


Singapore won the same way: by keeping calm, testing, and not shutting down their economy.

Singapore is often the “sure… but” exception in policy discussions.   It’s a hyper-educated city-state. Lessons don’t always apply to the rest of the world. But a pandemic is different.  Pandemics kill cities, and Singapore is the world’s densest city. If Singapore can fix this without national suicide, anyone can.  So what did they do? 

  • Follow contacts of potential victims, and test
  • Keeping positives in the hospital
  • Communicate
  • Do not panic 
  • Lead clearly and decisively

I could talk about Japan and Taiwan, but I won’t, because the story is the same: Practice hygiene.  Isolate the sick. Social distance. Test aggressively.   

And do not destroy your economy.

“The economy” means lives

The shutdown has become a game — fodder for memes, fodder for mocking Tweets, and inspirational Facebook posts, because it still feels like Christmas in March.  

It is not.  If your response to the threat of a recession is:

  • “The economy will just regrow”
  • “We can just print more money”
  • “We just have to live on savings for a while”

The answer is simple: you either live a life of extraordinary privilege, are an idiot, or both.  I can try to convince you, but first, ask yourself:  how many people have the savings you’ve built up — the freedom to live out of a savings account in a rough year? I’ll give you a hint:  almost none.

Likewise, working from home is the correct choice, for anyone who can manage it. Flattening the curve is a meaningful and important improvement over the unmitigated spread of this disease. But the ability to work from home is a privilege afforded to not even a third of Americans:

According to the Bureau of Labor Statistics, only 29 percent of Americans can work from home, including one in 20 service workers and more than half of information workers

If you are able to weather this storm by working from home, congratulations — you are profoundly privileged. I am one of you. But we are not average, and we are not the ones who risk unemployment and poverty. We are not the ones who public policy should revolve around helping. The other 71% of Americans — who cannot — are the ones who matter right now.

To keep the now-unemployed from dying in the streets, we will bail them out.  And that bailout, in the US alone, just to start, will cost a trillion dollars.  That number will almost certainly double, at a minimum, over the next two years.  

What else could we spend two trillion dollars on?   To start, we could void all student debt:  $1.56 trillion, as of 2020.  We could vastly expand medicare or medicaid.  You can fill in the policy of your choice, and we could do it.  But we won’t.

We are borrowing money to pay for this self-inflicted crisis.  We should be spending that money investing in the future — perhaps freeing students from a life of crippling debt — but instead, we are throwing it at the past.

The rest of the world

The US is not the world.  The US will muddle through, no matter how poor our decisions, usually (but not always) at the cost of our futures, not our lives.  The rest of the world does not have this luxury.

GDP saves lives.  GDP is inextricably linked to life expectancy, child mortality, deaths in childbirth, and any other measure of life you want to choose.  This is so well proven that it shouldn’t require citations, but I’ll put up a chart anyway:

A depression will set back world GDP by years.  The US and Europe buy goods from the developing world.  The 2008 recession — driven primarily by housing and speculation in western markets — crushed the economies not just of developed nations, but the entire world:

we investigate the 29 percent drop in world trade in manufactures during the period 2008-2009. A shift in final spending away from tradable sectors, largely caused by declines in durables investment efficiency, accounts for most of the collapse in trade relative to GDP

If you are unswayed by the arguments that a self-inflicted depression will hurt the working poor in the US, be swayed by this — that our short-sighted choices will kill millions in the developing world.

How did we get here?

Doctors are not responsible for policy.  They are responsible for curing diseases.  It is not fair to ask them to do more, or to factor long-term economic policy into their goal of saving lives right now.   We elect people to balance the long-term cost of these decisions.  We call them politicians, and ours have failed us.

The solution to this crisis is simple — we do our best to emulate East Asia.  We isolate the sick. We improve sanitization.  We mobilize industry to build tests, ventilators, and respirators, as fast as we can — using whatever emergency powers are needed to make it happen.   And we do this all without shutting down the economy, the engine which pays for our future.

We do the best we can.  And accept that if we fail, many of the sickest elderly will die.  

Next time will be different.  We will learn our lessons, be prepared, and organize our government response teams  the way that Taiwan and South Korea have. We will have a government and a response which can protect every American.

But now, today, we need to turn the country back on, and send the rest (the 71% who can’t work from home) back to work. We owe them a future worth living in. 

The best gift the Coronavirus can give us? The normalization of remote work

tl,dr: Covid-19, if (or at this point, “when”) it becomes a full pandemic, is going to rapidly accelerate the shift to remote-first tech hiring.  This is an amazing thing. It’s too bad it’ll take a pandemic to get us there.

The focus over the past couple weeks has been on how a pandemic will affect the economy.  This is a reasonable question to ask. But a brief economic downturn is just noise compared to the big structural changes we’ll see in turn — that long-term quarantine and travel restrictions will normalize remote work. It’s already happening, especially in tech companies.  

Remote work is a revolutionary improvement for workers of all stripes and careers, but I’m a tech worker, so I’ll speak from the perspective of a one.

Software development is second only to sales in ability to execute at full capacity while remote*, if given the opportunity.  But willingness to perform tech-first remote hiring has ground forward glacially slowly, especially at the largest and hippest tech companies — and at times, even moved backwards

*I’ve known top sales reps to make their calls on ski lifts between runs.  Software devs can’t quite match that quality-of-life boost.

A prolonged quarantine period (even without a formal government quarantine, tech companies will push hard for employees to work from home) will force new remote-first defaults on companies previously unwilling to make remote work a first-class option:

  • Conversations will move to Slack, MS teams, or the equivalent
  • Videoconferenced meetings will become the default, even if a few workers make it into the office
  • IT departments without stable VPNs (all too common) will quickly fix that problem, as the C-suite starts working from home offices

The shift to normalized remote work will massively benefit tech employees, who have been working for decades with a Hobson’s choice of employment — move to a tech hub, with tech-hub salaries, and spend it all on cost-of-living, or eat scraps:

  • Cost-of-living freedom: SF and New York are inhumanly expensive. Make it practical to pull SF-salaries while living in the midwest? Most SF employees have no concept of how well you can live on $250k in a civilized part of the country.
  • Family balance freedom: operating as an on-site employee with children is hard, and working from home makes it wildly easier to balance childcare responsibilities (trips to school, childcare, etc etc).
  • Commutes suck. Good commutes suck. Terrible commutes are a portal into hell. What would you pay to gain two hours and live 26-hour days? Well, working from home is the closest you can get.

I don’t mean to be callous — deaths are bad, and I wish it upon nobody. But the future is remote-first, and when we get there, history will judge our commute-first culture the same way we judge people in the middle ages for dumping shit on public streets.

I wish it didn’t take a global pandemic to get us here, but if we’re looking for silver linings in the coffins — it’s hard to imagine a shinier one that the remote-normalized culture we’re going to build, by fire, blood, and mucous, over the next six months.  

I’ll take it.

Happy Earth Day: drive more and (maybe) save the environment

Yesterday was Earth Day, so Facebook was naturally full of people bragging about how they walked to the store instead of driving, in order to save the Earth.  I feel obligated to point out that this very plausibly isn’t true.  I’m not the first person to run these numbers, but I was curious and wanted to investigate for myself.  My (rather rough) calculations are all here.

As a baseline, we want to calculate the kWh cost of driving a car 1 mile. I’m using a baseline of 33.41 kWh / gallon of gasoline:

Car MPG kWh/ 1 mile
Prius 58 .58
F-150 19 1.76

If you’re bragging on Facebook about your environmental impact, you’re probably driving a Prius, so we’ll roll with that.  Feel free to substitute your own car.

To get the calories burned per mile walking, I used numbers I found here.  The numbers here vary pretty widely with body weight and walking speed, but I’ll use 180 pounds at 3.0 mph for 95 calories per hour.

To get the energy costs per pound of food produced, I used the numbers I found here.  Click through for their sources.  kWh / 1 mile is calculated as

kWh/1 mile = 180/(calories/lb) * ( kWh/lb)

Just to be clear: this isn’t the calories in food.  This is the energy usage required to produce and transport the food to your mouth, which is essentially all fossil fuels.  Numbers vary widely per food source, as expected.

Food Calories / Lb kWh / Lb kWh / 1 mile
Corn 390 0.43 0.10
Milk 291 0.75 0.24
Apples 216 1.67 0.73
Eggs 650 4 0.58
Chicken 573 4.4 0.73
Cheese 1824 6.75 0.35
Pork 480 12.6 2.49
Beef 1176 31.5 2.54

So what’s the conclusion?  It’s mixed.

  • If you drive a Prius, you’re OK walking, as long as you replace the burned calories with Doritos (cheese + corn) and (corn syrup’d) Coca-Cola
  • If you drive a Prius, and you replace the burned calories with a chicken and apple salad (I couldn’t find numbers for lettuce, but they are undoubtedly even worse), you are destroying the planet
  • If you drive an F-150, you’re probably going to replace your burned calories with a steak, so you’re actually saving the environment by driving.

These numbers are of course rough, and do not include:

  • The energy cost of producing a car.  This becomes very complicated very quickly, becaus you likely would have done less damage by just buying a used car instead of a new Prius
  • This assumes you actually eat all the food you ordered, and didn’t leave carrots rotting in the back of your fridge (your fridge, by the way, uses energy).  Americans are notoriously terrible at doing this.
  • This calculates only energy usage — it does not attempt to quantify the environmental impact of turning Brazilian rainforests into organic Kale farms, to grow your fourth-meal salad.
  • This assumes a single rider per car-mile.  If you are carpooling on your drive to KFC, you can cut all the car energy usage numbers by half (or more, for families)

Anyway, I’m sure there are many other reasons these numbers are rough, I just wanted to point out that the conventional wisdom is pretty awful on environmental topics.

Procedural star rendering with three.js and WebGL shaders

Over the past few months I’ve been working on a WebGL visualization of earth’s solar neighborhood — that is, a 3D map of all stars within 75 light years of Earth, rendering stars and (exo)planets as accurately as possible.  In the process I’ve had to learn a lot about WebGL (specifically three.js, the WebGL library I’ve used).  This post goes into more detail about how I ended up doing procedural star rendering using three.js.  

The first iteration of this project rendered stars as large balls, with colors roughly mapped to star temperature.  The balls did technically tell you where a star was, but it’s not a particularly compelling visual:


Pretty much any interesting WebGL or OpenGL animation uses vertex and fragment shaders to render complex details on surfaces.  In some cases this just means mapping a fixed image onto a shape, but shaders can also be generated randomly, to represent flames, explosions, waves etc.  three.js makes it easy to attach custom vertex and fragment shaders to your meshes, so I decided to take a shot at semi-realistic (or at least, cool-looking) star rendering with my own shaders.  

Some googling brought me to a very helpful guide on the Seeds of Andromeda dev blog which outlined how to procedurally render stars using OpenGL.  This post outlines how I translated a portion of this guide to three.js, along with a few tweaks.

The full code for the fragment and vertex shaders are on GitHub.  I have images here, but the visuals are most interesting on the actual tool ( since they are larger and animated.

Usual disclaimer — I don’t know anything about astronomy, and I’m new to WebGL, so don’t assume that anything here is “correct” or implemented “cleanly”.  Feedback and suggestions welcome.

My goal was to render something along the lines of this false-color image of the sun:


In the final shader I implemented:

  • the star’s temperature is mapped to an RGB color
  • noise functions try to emulate the real texture
    • a base noise function to generate granules
    • a targeted negative noise function to generate sunspots
    • a broader noise function to generate hotter areas
  • a separate corona is added to show the star at long distances

Temperature mapping
The color of a star is determined by its temperature, following the black body radiation, color spectrum:


(sourced from wikipedia)

Since we want to render stars at the correct temperature, it makes sense to access this gradient in the shader where we are choosing  colors for pixels.  Unfortunately, WebGL limits the size of uniforms to a couple hundred on most hardware, making it tough to pack this data into the shader.

In theory WebGL implements vertex texture mapping, which would let the shader fetch the RGB coordinates from a loaded texture, but I wasn’t sure how to do this in WebGL.  So instead I broke the black-body radiation color vector into a large, horrifying, stepwise function:

bool rbucket1 = i < 60.0; // 0, 255 in 60 bool rbucket2 = i >= 60.0 && i < 236.0;  //   255,255
float r =
float(rbucket1) * (0.0 + i * 4.25) +
float(rbucket2) * (255.0) +
float(rbucket3) * (255.0 + (i - 236.0) * -2.442) +
float(rbucket4) * (128.0 + (i - 288.0) * -0.764) +
float(rbucket5) * (60.0 + (i - 377.0) * -0.4477)+
float(rbucket6) * 0.0;

Pretty disgusting.  But it works!  The full function is in the shader here

Plugging in the Sun’s temperature (5,778) gives us an exciting shade of off-white:


While beautiful, we can do better.

Base noise function (granules)

Going forward I diverge a bit from the SoA guide.  While the SoA guide chooses a temperature and then varies the intensity of the texture based on a noise function, I instead fix high and low surface temperatures for the star, and use the noise function to vary between them.  The high and low temperatures are passed into the shader as uniforms:

 var material = new THREE.ShaderMaterial({
   uniforms: {
     time: uniforms.time,
     scale: uniforms.scale,
     highTemp: {type: "f", value: starData.temperatureEstimate.value.quantity},
     lowTemp: {type: "f", value: starData.temperatureEstimate.value.quantity / 4}
   vertexShader: shaders.dynamicVertexShader,
   fragmentShader: shaders.starFragmentShader,
   transparent: false,
   polygonOffset: -.1,
   usePolygonOffset: true

All the noise functions below shift the pixel temperature, which is then mapped to an RGB color.

Convection currents on the surface of the sun generate noisy “granules” of hotter and cooler areas.  To represent these granules an available WebGL implementation of 3D simplex noise.    The base noise for a pixel is just the simplex noise at the vertex coordinates, plus some magic numbers (simply tuned to whatever looked “realistic”):

void main( void ) {
float noiseBase = (noise(vTexCoord3D , .40, 0.7)+1.0)/2.0;

The number of octaves in the simplex noise determines the “depth” of the noise, as zoom increases.  The tradeoff of course is that each octave increases the work the GPU computes each frame, so more octaves == fewer frames per second.  Here is the sun rendered at 2 octaves:


4 octaves (which I ended up using):


and 8 octaves (too intense to render real-time with acceptable performance):



Sunspots are areas on the surface of a star with a reduced surface temperature due to magnetic field flux.  My implementation of sunspots is pretty simple; I take the same noise function we used for the granules, but with a decreased frequency, higher amplitude and initial offset.  By only taking the positive values (the max function), the sunspots show up as discrete features rather than continuous noise.  The final value (“ss”) is then subtracted from the initial noise.

float frequency = 0.04;
float t1 = snoise(vTexCoord3D * frequency)*2.7 -  1.9;
float ss = max(0.0, t1);

This adds only a single snoise call per pixel, and looks reasonably good:


Additional temperature variation

To add a bit more noise, the noise function is used one last time, this time to add temperature in broader areas, for a bit more noise:

float brightNoise= snoise(vTexCoord3D * .02)*1.4- .9;
float brightSpot = max(0.0, brightNoise);

float total = noiseBase - ss + brightSpot;

All together, this is what the final shader looks like:



Stars are very small, on a stellar scale.  The main goal of this project is to be able to visually hop around the Earth’s solar neighborhood, so we need to be able to see stars at a long distance (like we can in real life).  

The easiest solution is to just have a very large fixed sprite attached at the star’s location.  This solution has some issues though:

  • being inside a large semi-opaque sprite (ex, when zoomed up towards a star) occludes vision of everything else
  • scaled sprites in Three.js do not play well with raycasting (the raycaster misses the sprite, making it impossible to select stars by mousing over them)
  • a fixed sprite will not vary its color by star temperature

I ended up implementing a shader which implemented a corona shader with

  • RGB color based on the star’s temperature (same implementation as above)
  • color near the focus trending towards pure white
  • size was proportional to camera distance (up to a max distance)
  • a bit of lens flare (this didn’t work very well)

Full code here.  Lots of magic constants for aesthetics, like before.

Close to the target star, the corona is mostly occluded by the detail mesh:


At a distance the corona remains visible:


On a cooler (temperature) star:


The corona mesh serves two purposes

  • calculating intersections during raycasting (to enable targeting stars via mouseover and clicking)
  • star visibility

Using a custom shader to implement both of these use-cases let me cut the number of rendered three.js meshes in half; this is great, because rendering half as many objects means each frame renders twice as quickly.


This shader is a pretty good first step, but I’d like to make a few improvements and additions when I have a chance:

  • Solar flares (and other 3D surface activity)
  • More accurate sunspot rendering (the size and frequency aren’t based on any real science)
  • Fix coronas to more accurately represent a star’s real visual magnitude — the most obvious ones here are the largest ones, not necessarily the brightest ones

My goal is to follow up this post a couple others about parts of this project I think turned out well, starting with the orbit controls (the logic for panning the camera around a fixed point while orbiting).