Podcast notes: The hidden costs of cheap meat – Leah Garces on Ezra Klein show

Guest: Leah Garces, president of Mercy for Animals

50 years ago, meat costs $7/lb, today chicken is $1.80/lb
“These prices are fake”

Chickens today grow much bigger, much faster, much cheaper
-Reach slaughter weight in 6 weeks time, 3x faster than before
-50K birds in one warehouse – lose any sense of individuality
-Used to be 2-5sf of space per bird, now 3/4sf per bird – wall to wall

Battery cages
-for laying hens to produce eggs
-6 to 10 birds in a barren wired cage, crowded, causes aggressive behaviors, peck each other
-solution: industry shears off beak tips to reduce damage

Fish numbers are hard to quantify because they’re reported in tons
Land animals – 80 BILLION in the world, 70 billion are chickens (90%)

America is highest meat consumer – per capita eats 27 animals per year
America produces / have access to – 225 pounds of meat per person per year
Numbers are growing – last year was highest number ever produced and consumed

Chickens today grow so big so fast that they collapse under their own weight – especially their breast muscle – can’t survive past 6 weeks old, have heart attacks, too metabolically taxed
Product of selective breeding just for breast muscle
Chickens in wild can live many years
The meat has more fat and protein content than before, white stripes in the meat which are literally disease markers as result of fast growth

Chicken warehouse / factory farms
-chickens are very immobile, plopped down
-lots of sores on body
–“marshmallow on toothpicks”
-often panting, taxed by weight / size
-very fragile

What % are raised industrial – globally it’s 90%; in America it’s 99%

A positive trend: 1/3 production of eggs is cage-free now, as result of pressure campaigns
-still an industrial setting (indoors, over crowded, given antibiotics)
-just not kept in cages anymore

Gestation crates for pigs
-source of bacon, sausage
-pregnant pigs kept in metal crate so small that pregnant pig can’t turn her body, can’t really lay down; bottom of cage is cold and wet with slatted floors for feces; after giving birth, kept in a slightly larger farrowing crate, essentially a breeding machine, piglets are taken away – and the cycle starts again
-the pigs scream when their babies are taken away

70% of medically imported antibiotics in US are used in animals
-necessary for these animals to survive, grow faster
-antibiotic resistance is growing; millions of future deaths will come from antibiotic resistant diseases
-to reduce antibiotics would require changing genetics of the animals
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Meat industry needs to internalize these external costs
-CDC tracks viruses of concern: most are things like avian flu, swine flu – spreading from birds and pigs
-zoonotic diseases (from animals -> humans)
-pigs are more closely related to us than chickens

Impact on climate change
-livestock farming – mostly cows emitting methane – 14.5% of global greenhouse gas emissions (could be underestimate, as high as 25%)
–half of world’s usable land is for agriculture, most for livestock
-1/3 of arable land is used just to raise crops to feed farm animals (eg, soy, maize)
-contributor to deforestation
-contributor to air pollution (chickens produce ammonia and dust particles to local area; pig waste is collected in a cesspool which is sprayed into the air / fields, usually in low-income areas)
eg, Eastern North Carolina – former slaves area, hog industry moved nearby, hog waste ends up on clothes, cars, houses, but don’t have power to fight back, “nobody’s gonna put this in San Francisco”
–Gulf of Mexico dead zone – fertilizer run off into Mississippi, then into Gulf of Mexico – a zone the size of Rhode Island where no sea life can exist, only some species near the surface – the bottom has no oxygen nor life

100x more land used to produce a calorie of meat than a calorie of vegetable

Why isn’t meat more expensive? What are the externalities?
-animal suffering
-climate change
-air pollution
-farmers – owe lots of debt, often in low income areas
for chickens, farmers collectively owe $5B+ in debt, held hostage by it
a chicken farmer is basically a babysitter
keeps chickens alive for 6 weeks, then company collects and pays them
a form of indentured servitude

What about tax payer / government subsidies?
-in 2011, government purchased $40M of extra chicken supply – tax dollars paying for over production
-during covid, gave $270M in pandemic assistance
–spent $40M for “de-population” of chickens and pigs – slaughtered right on farm, “ventilation shutdown”, gets too hot and the animals suffocate
-why does government / taxpayer dollars pay? The industry should pay

2011 – Prop 12 – banned production and sale of extreme close confinement of animals raised for meat – almost 70% of Californians voted in favor
Industry appealed, Supreme Court hearing the case, Biden supports industry / overturning Prop 12

Compassion is an infinite muscle – we can have direct impact on improving farm animals’ lives

What are some modest steps for improvement?
-internalizing industry costs thru regulation – pollution tax, improving factory conditions
-meat prices will rise, consumption will decline

3 recommended books from Leah
-Wastelands by Addison – Smithfield’s case in North Carolina
-Meatonomics
-Animal Machines – Ruth Harrison – catalyst for “Five Freedoms” for animal welfare

The Venn of blockchain and AI

I’ve been thinking about the relationship between blockchains and AI lately. Both are emerging foundational technologies and I think it’s no accident they are both coming of age at the same time.

Multiple writers have already expressed this view:

AIs can be used to generate “deep fakes” while cryptographic techniques can be used to reliably authenticate things against such fakery. Flipping it around, crypto is a target-rich environment for scammers and hackers, and machine learning can be used to audit crypto code for vulnerabilities. I am convinced there is something deeper going on here. This reeks of real yin-yangery that extends to the roots of computing somehow

From Venkatesh Rao: https://studio.ribbonfarm.com/p/the-dawn-of-mediocre-computing

I think AI and Web3 are two sides of the same coin. As machines increasingly do the work that humans used to do, we will need tools to manage our identity and our humanity. Web3 is producing those tools and some of us are already using them to write, tweet/cast, make and collect art, and do a host of other things that machines can also do. Web3 will be the human place to do these things when machines start corrupting the traditional places we do/did these things.

From Fred Wilson: https://avc.com/2022/12/sign-everything/

In both writers’ examples, blockchain helps solve some of the problems that AI creates, and vice-versa. I’m reminded of Kevin Kelly who said, Each new technology creates more problems than it solves.

Blockchains and AI have a sort of weird and emergent technological symbiosis and I’m here for it.

So the brain flatulence below is just my way to think aloud, using the writing process to work through the question(s).

*Note: when I say “blockchain”, I include what Fred Wilson calls web3 and Venkatesh calls crypto; there are just a few canonical applications that we’re all familiar with (namely, bitcoin and ethereum); and when I say “AI”, I am thinking about the most popular machine learning models like GPT3 and Stable Diffusion

*Note also: I am just a humble user of these new and powerful AI tools, and can barely understand the abstract of a typical machine learning research paper; so part of the reason why I’m writing this is to find out where I’m wrong a la Cunningham’s law

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A blockchain is a tool for individual sovereignty; while an AI is a tool for individual creativity

A blockchain operates at maximum transparency; while an AI operates largely as a black box

A blockchain clearly shows the chain of ownership and history; while an AI… (does something like the opposite in the way it aggregates and melds and mutates as much data as possible?)

A blockchain is “trustless”, in the sense that what you see on-chain is the agreed upon “truth” of all its users; while an AI is (?), in the sense that what it generates is more or less unique to the specific prompt / question / user (and even this can change as the model is updated, or new data is added]

An AI is much easier to use than a blockchain

An AI can create vast quantities of content, very cheaply; while a (truly “decentralized”) blockchain is limited by scalability and cost

An AI is centralized (to a specific company, or model, or data set) in the sense that decision making rests with a team or company; while a blockchain is decentralized and decision making is distributed

A surprising user experience – as in, an unexpected but delightful output – is typically net positive for a user of AI, while seeing something happen on a blockchain that you don’t expect would generally be pretty bad (yes, of course there are airdrops)

Blockchains are a competitive threat to industries with a high degree of centralization (such as fiat currency issuance, and payment networks); AI is a competitive threat to many individual online workers (such as language translators, and freelance writers, and basic QA/QC employees)

Both blockchains and AI have multiple open source products that can be forked by developers

Both blockchains and AI are platforms upon which many other products and services can be built

Both blockchains and AI are technologies that exploded into the popular consciousness in the last 10 years

Both “blockchain” and “AI” are very broad suitcase words, in part because they are both the product of many technologies combined in innovative ways: for blockchain that is everything from cryptography to smart contract programming to PoW mining to distributed consensus mechanisms; for AI that’s, uh…well everything listed here and more, I suppose

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I’ll end here for now, but let me know what I got wrong, what I’m missing, and what questions or ideas this might inspire

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Addendum #1: I asked ChatGPT

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Addendum #2: This NYT article notes that SBF (“Sam Bankscam Fraud”) donated at least $500M to organizations researching AI alignment and AI safety. Not exactly the kind of symbiosis I want to explore, but worth noting.

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Addendum #3:

Blockchains can only give precise answers, while AI can give approximate answers or even fabricate answers

Blockchains are censorship resistant, while AI is centralized (most are created by small doxxed teams) and have implemented restrictions on usage (most have rules against, for example, CSAM or nudity)

James Bond author Ian Fleming’s writing advice: “You have to get the reader to turn over the page.”

playground AI, matt williamson

Source: https://crimereads.com/ian-fleming-explains-how-to-write-a-thriller-circa-1963/

Direct quotes:

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There is only one recipe for a best seller and it is a very simple one. You have to get the reader to turn over the page. […] If you look back on the best sellers you have read, you will find that they all have this quality. You simply /have/to turn over the page.

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My contribution to the art of thriller-writing has been to attempt the total stimulation of the reader all the way through, even to his taste buds. For instance, I have never understood why people in books have to eat such sketchy and indifferent meals. English heroes seem to live on cups of tea and glasses of beer, and when they do get a square meal we never hear what it consists of. Personally, I am not a gourmet and I abhor food-and-winemanship. My favorite food is scrambled eggs.

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What I aim at is a certain disciplined exoticism. I have not re-read any of my books to see if this stands up to close examination, but I think you will find that the sun is always shining in my books—a state of affairs which minutely lifts the spirit of the English reader—that most of the settings of my books are in themselves interesting and pleasurable, taking the reader to exciting places around the world, and that, in general, a strong hedonistic streak is always there to offset the grimmer side of Bond’s adventures. This, so to speak, “pleasures” the reader

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I can recommend hotel bedrooms as far removed from your usual “life” as possible. Your anonymity in these drab surroundings and your lack of friends and distractions in the strange locale will create a vacuum which should force you into a writing mood and, if your pocket is shallow, into a mood which will also make you write fast and with application.

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I never correct anything and I never go back to what I have written, except to the foot of the last page to see where I have got to. If you once look back, you are lost. How could you have written this drivel? How could you have used “terrible” six times on one page? And so forth. If you interrupt the writing of fast narrative with too much introspection and self-criticism, you will be lucky if you write 500 words a day and you will be disgusted with them into the bargain.

Cullen Roche’s macro masterclass on The Bitcoin Layer – podcast notes

Cullen Roche – CIO of Discipline Funds
-Merrill Lynch asset management
-2008 financial crisis transformed his view of world – how macro can dominate everything, while micro doesn’t really matter
–Japan had been going through a similar transformation for 20 years – learned a lot from it
-Japan’s financial system modeled after Fed, US – Japan’s lessons taught him that what would happen would be the opposite of MSM narrative
-Beat the drum that post GFC would be sluggish dis-inflation
-At Discipline – hyper focused on financial planning as foundation for portfolio (more bottoms-up); “more Vanguard than Cathy Woods”

Doesn’t believe now will be like 70s style stagflation, more like 2008

Structural trends – globalization, technology, demographics – all long-term disinflationary anchors
Big lesson of Covid – fiscal policy can cause big inflation, it’s the Treasury not Fed that has the bazooka

Cyclical trends – short term inflation bump due to that fiscal policy; will come down if government doesn’t splurge
Debt cycle not consistent with high inflation environment

High inflation can only come from private sector (consumer / corporate borrowing), or public sector (government spending)
Low likelihood of big fiscal spending

In 3-4 years, we’ll realize inflation was transitory

Fed outlook
-they’ve been more aggressive than he expected
-“Fed’s kinda screwed” – look bad coming off 2021 inflation
-should have moved earlier in 2021 – eg, a modified Taylor rule
-too much focus on lagging indicators – eg, employment
–Fed is old school monetarists / Keynesians – don’t wanna live thru 1970s again

He believes today looks more like 2008, not 1970s
Big worry of real housing market downturn

Monetary policy will function through housing market and mortgage rates
As rates > 5%, “something’s gotta give” – not enough housing demand at that level

Last 10 years was one cycle, a blowoff / FOMO effect
Economy now digesting this excess, the bust component

Housing is slow moving animal
Concerned Fed will create more downside than we expect

If in 2023, inflation ticks steadily lower, housing falls fast, unemployment rises faster than expected – Fed will walk a lot back, have a mini 2008
Could be 2008 credit style disinflation rather than runaway inflation of 70s

Nik: hyper focused on housing sector, outsized impact on US economy, 3 months of home price declines

10% decline in home prices is like a flesh wound – only takes us back to middle 2021
If home prices are volatile, balance sheets become volatile
Housing has become an important economic asset – more than previous generations
We don’t understand knock on effects – like 2008
Outlier risk of housing prices falling 25%, risks lurking in shadows
“2008 humbled me a lot” – thought he had a bullet proof macro framework

More analysts now forecasting larger price drops in housing – more expected volatility in 2023, 2024

Argument that covid boom was sorta fake – driven by government spending
If housing falls 25%, causes a lot more collateral damage than anyone expects

Nik: in 2007-2008, analysts argued we’ve never seen national housing price declines YoY, only regional declines – surprised everyone

This isn’t like 1987 crash – it’s not a single event
It’s a structural event – because housing is long drawn-out process, and so core to US economy
Construction still in boom period – lots of supply coming online, but don’t have demand or new construction

We’re probably in 4th inning of housing market downturn – still relatively early
Fed sorta oblivious to it, they don’t realize damage that 6-7% mortgage rates do to housing market

No idea what stocks will do in next 6-12 months

His duration framework for investment assets:
-Cash / Treasuries are Zero duration
-Bonds are 5 year duration instruments
-Stock market is 18 year instrument
–Bitcoin is 100 year duration instrument (gold is 40 year)

Stock market riskier today – valuations high, less attractive relative to other instruments, still high levels of irrational exuberance
Multiples need to come in / move sideways for longer period
High multiples —> Lower risk adjusted returns
Do you want stocks at 30 P/E with high volatility or 4.5% treasury bill with no volatility?
Stocks in 18-24 months – would be shocked if up significantly

Scenario: housing prices slowly grind down 10-20%, no real credit event —> would have stagnant stock market, and maybe 2025 it takes off
Risk is a real credit event – more like 2008 than 1970s – if Fed reverses, would be because real deterioration in balance sheets and economic environment
Fed will likely slowly walk rates back, ease off language, but rates will remain high and cause demand destruction – maybe late 2023 – likely to be behind curve again
At that point, credit has deteriorated, stock markets fallen 40%
Lots of starts and stops until then
Higher probability of hard landing outcome than Fed finding a soft landing

Bitcoin is a technology – more like VC than equity
Public markets are boring 18 year instruments, companies are more stable boring value
VC is much younger, standard deviations larger, thus much longer duration
Bitcoin is weird blend of digital gold + VC + payment system
Bet is it becomes alternative payment system – will take very long time to come to fruition
Will work in parallel with traditional fiat / credit system
Takes time for people to adopt the new mindset

He works with people who have already made good money, closer to retirement, more conservative
Results in shorter duration instruments (eg, Treasuries, equities)
Bitcoin, like VC, has too much potential downside for them
For a true efficient market theorist – maybe 0.5-1% bitcoin allocation (eg, if you put 1% in bitcoin in 2015, could be 20% of portfolio today)

Big advocate of re-balancing in counter-cyclical manner to reduce skew – reduce outsized risk in any single instrument
Don’t want golden handcuffs that you can’t sell, too much capital gains, help control your behavior, don’t wanna become a forced seller

Loved Nik’s book Layered Money and its hierarchical thinking approach

Anton Chekhov’s 6 principles of writing: “To what end? He hardly knew himself. He only knew that he must see Anna Sergeyevna, must speak to her, arrange a meeting, if possible.”

In a letter to his older brother Alexander, dated 10 May 1886, Anton Chekhov set out his six principles of writing: ‘truthful descriptions of persons and objects; total objectivity; extreme brevity; compassion; no political-social-economic effusions; audacity and originality: eschew cliché.

From Marginalian:

…no depiction of reality is realistic unless it include an empathic account of all perspectives, which might be the defining characteristic not only of Chekhov as a writer but of any great storyteller.

Here are his 6 rules, with a few examples pulled from his classic short story, The Lady with the Dog (1899):

1. Absence of lengthy verbiage of a political-social-economic nature

Repeated and bitter experience had taught him that every fresh intimacy, while at first introducing such pleasant variety into everyday life, and offering itself as a charming, light adventure, inevitably developed, among decent people (especially in Moscow, where they are so irresolute and slow to move), into a problem of excessive complication leading to an intolerably irksome situation.

2. Total objectivity

3. Truthful descriptions of persons and objects

He could remember carefree, good-natured women who were exhilarated by love-making and grateful to him for the happiness he gave them, however shortlived; and there had been others — his wife among them — whose caresses were insincere, affected, hysterical, mixed up with a great deal of quite unnecessary talk, and whose expression seemed to say that all this was not just love-making or passion, but something much more significant

4. Extreme brevity

When the Christmas holidays came, he packed his things, telling his wife he had to go to Petersburg in the interests of a certain young man, and set off for the town of S. To what end? He hardly knew himself. He only knew that he must see Anna Sergeyevna, must speak to her, arrange a meeting, if possible.

5. Audacity and originality: flee the stereotype

Anna Sergeyevna was accompanied by a tall, round-shouldered young man with small whiskers, who nodded at every step before taking the seat beside her and seemed to be continually bowing to someone. This must be her husband, whom, in a fit of bitterness, at Yalta, she had called a “flunkey.” And there really was something of the lackey’s servility in his lanky figure, his side-whiskers, and the little bald spot on the top of his head. And he smiled sweetly, and the badge of some scientific society gleaming in his buttonhole was like the number on a footman’s livery.

6. Compassion

And it seemed to them that they were within an inch of arriving at a decision, and that then a new, beautiful life would begin. And they both realized that the end was still far, far away, and that the hardest, the most complicated part was only just beginning.