Redundancies are unprofitable. Being slow and careful is unprofitable. Being less embedded in and less essential and having less access to the customers’ networks and machines is unprofitable—at least in the short term, by which these companies are measured. This is true for companies like CrowdStrike. It’s also true for CrowdStrike’s customers, who also didn’t have resilience, redundancy, or backup systems in place for failures such as this because they are also an expense that affects short-term profitability.

The market rewards short-term profit-maximizing systems, and doesn’t sufficiently penalize such companies for the impact their mistakes can have. (Stock prices depress only temporarily. Regulatory penalties are minor. Class-action lawsuits settle. Insurance blunts financial losses.) It’s not even clear that the information technology industry could exist in its current form if it had to take into account all the risks such brittleness causes.

The asymmetry of costs is largely due to our complex interdependency on so many systems and technologies, any one of which can cause major failures. Each piece of software depends on dozens of others, typically written by other engineering teams sometimes years earlier on the other side of the planet. Some software systems have not been properly designed to contain the damage caused by a bug or a hack of some key software dependency.

This market force has led to the current global interdependence of systems, far and wide beyond their industry and original scope. It’s why flying planes depends on software that has nothing to do with the avionics. It’s why, in our connected internet-of-things world, we can imagine a similar bad software update resulting in our cars not starting one morning or our refrigerators failing.

Right now, the market incentives in tech are to focus on how things succeed: A company like CrowdStrike provides a key service that checks off required functionality on a compliance checklist, which makes it all about the features that they will deliver when everything is working. That’s exactly backward. We want our technological infrastructure to mimic nature in the way things fail. That will give us deep complexity rather than just surface complexity, and resilience rather than brittleness.

Netflix is famous for its Chaos Monkey tool, which intentionally causes failures to force the systems (and, really, the engineers) to be more resilient. The incentives don’t line up in the short term: It makes it harder for Netflix engineers to do their jobs and more expensive for them to run their systems. Over years, this kind of testing generates more stable systems. But it requires corporate leadership with foresight and a willingness to spend in the short term for possible long-term benefits.

The National Highway Traffic Safety Administration crashes cars to learn what happens to the people inside. But cars are relatively simple, and keeping people safe is straightforward. Software is different. It is diverse, is constantly changing, and has to continually adapt to novel circumstances. We can’t expect that a regulation that mandates a specific list of software crash tests would suffice. Again, security and resilience are achieved through the process by which we fail and fix, not through any specific checklist. Regulation has to codify that process.

If your model is "it's okay to lie if I've been lied to" then we're all knee deep in bullshit forever and can never escape Transaction Cost Hell.

Do I agree that entering The Infinite Lie Vortex is wise or good for you spiritually? No, not at all, just look at what it's called.

it is very common practice on the job market to have a CV that obfuscates the reality of your contribution at previous workplaces. Putting aside whether you're a professional web developer because you got paid $20 by your uncle to fix some HTML, the issue with lying lies in the intent behind it. If you have a good idea of what impression you are leaving your interlocutor with, and you are crafting statements such that the image in their head does not map to reality, then you are lying.

Unfortunately thanks to our dear leader's masterful consummation of toxicity and incompetence, the truth of the matter is that: They left their previous job due to burnout related to extensive bullying, which future employers would like to know because they would prefer to blacklist everyone involved to minimize their chances of getting the bad actor. Everyone involved thinks that they were the victim, and an employer does not have access to my direct observations, so this is not even an unreasonable strategy All their projects were failures through no fault of their own, in a market where everyone has "successfully designed and implemented" their data governance initiatives, as indicated previously

What I am trying to say is that I currently believe that there are not enough employers who will appreciate honesty and competence for a strategy of honesty to reliably pay your rent. My concern, with regards to Nat's original article, is that the industry is so primed with nonsense that we effectively have two industries. We have a real engineering market, where people are fairly serious and gather in small conclaves (only two of which I have seen, and one of those was through a blog reader's introduction), and then a gigantic field of people that are cosplaying at engineering. The real market is large in absolute terms, but tiny relative to the number of candidates and companies out there. The fake market is all people that haven't cultivated the discipline to engineer but nonetheless want software engineering salaries and clout.

There are some companies where your interviewer is going to be a reasonable person, and there you can be totally honest. For example, it is a good thing to admit that the last project didn't go that well, because the kind of person that sees the industry for what it is, and who doesn't endorse bullshit, and who works on themselves diligently - that person is going to hear your honesty, and is probably reasonably good at detecting when candidates are revealing just enough fake problems to fake honesty, and then they will hire you. You will both put down your weapons and embrace. This is very rare. A strategy that is based on assuming this happens if you keep repeatedly engaging with random companies on the market is overwhelmingly going to result in a long, long search. For the most part, you will be engaged in a twisted, adversarial game with actors who will relentlessly try to do things like make you say a number first in case you say one that's too low.

Suffice it to say that, if you grin in just the right way and keep a straight face, there is a large class of person that will hear you say "Hah, you know, I'm just reflecting on how nice it is to be in a room full of people who are asking the right questions after all my other terrible interviews." and then they will shake your hand even as they shatter the other one patting themselves on the back at Mach 10. I know, I know, it sounds like that doesn't work but it absolutely does.

Neil Gaiman On Lying People get hired because, somehow, they get hired. In my case I did something which these days would be easy to check, and would get me into trouble, and when I started out, in those pre-internet days, seemed like a sensible career strategy: when I was asked by editors who I'd worked for, I lied. I listed a handful of magazines that sounded likely, and I sounded confident, and I got jobs. I then made it a point of honour to have written something for each of the magazines I'd listed to get that first job, so that I hadn't actually lied, I'd just been chronologically challenged... You get work however you get work.

Nat Bennett, of Start Of This Article fame, writes: If you want to be the kind of person who walks away from your job when you're asked to do something that doesn't fit your values, you need to save money. You need to maintain low fixed expenses. Acting with integrity – or whatever it is that you value – mostly isn't about making the right decision in the moment. It's mostly about the decisions that you make leading up to that moment, that prepare you to be able to make the decision that you feel is right.

As a rough rule, if I've let my relationship with a job deteriorate to the point that I must leave, I have already waited way too long, and will be forced to move to another place that is similarly upsetting.

And that is, of course, what had gradually happened. I very painfully navigated the immigration process, trimmed my expenses, found a position that is frequently silly but tolerable for extended periods of time, and started looking for work before the new gig, mostly the same as the last gig, became unbearable. Everything other than the immigration process was burnout induced, so I can't claim that it was a clever strategy, but the net effect is that I kept sacrificing things at the altar of Being Okay With Less, and now I am in an apartment so small that I think I almost fractured my little toe banging it on the side of my bed frame, but I have the luxury of not lying.

If I had to write down what a potential exit pathway looks like, it might be: Find a job even if you must navigate the Vortex, and it doesn't matter if it's bad because there's a grace period where your brain is not soaking up the local brand of madness, i.e, when you don't even understand the local politics yet Meet good programmers that appreciate things like mindfulness in your local area - you're going to have to figure out how to do this one Repeat Step 1 and Step 2 on a loop, building yourself up as a person, engineer, and friend, until someone who knows you for you hires you based on your personality and values, rather than "I have seven years doing bullshit in React that clearly should have been ten raw HTML pages served off one Django server"

A CEO here told me that he asks people to self-evaluate their skill on a scale of 1 to 10, but he actually has solid measures. You're at 10 at Python if you're a core maintainer. 9 if you speak at major international conferences, etc. On that scale, I'm a 4, or maybe a 5 on my best day ever, and that's the sad truth. We'll get there one day.

I will always hate writing code that moves the overall product further from Quality. I'll write a basic feature and take shortcuts, but not the kind that we are going to build on top of, which is unattractive to employers because sacrificing the long-term health of a product is a big part of status laundering.

The only piece of software I've written that is unambiguously helpful is this dumb hack that I used to cut up episodes of the Glass Cannon Podcast into one minute segments so that my skip track button on my underwater headphones is now a janky fast forward one minute button. It took me like ten minutes to write, and is my greatest pride.

Have I actually worked with Google? My CV says so, but guess what, not quite! I worked on one project where the money came from Google, but we really had one call with one guy who said we were probably on track, which we definitely were not!

Did I salvage a A$1.2M project? Technically yes, but only because I forced the previous developer to actually give us his code before he quit! This is not replicable, and then the whole engineering team quit over a mandatory return to office, so the application never shipped!

Did I save a half million dollars in Snowflake expenses? CV says yes, reality says I can only repeat that trick if someone decided to set another pile of money on fire and hand me the fire extinguisher! Did I really receive departmental recognition for this? Yes, but only in that they gave me A$30 and a pat on the head and told me that a raise wasn't on the table.

Was I the most highly paid senior engineer at that company? Yes, but only because I had insider information that four people quit in the same week, and used that to negotiate a 20% raise over the next highest salary - the decision was based around executive KPIs, not my competence!

I think that most people are underestimating just how radical the upside of AI could be, just as I think most people are underestimating how bad the risks could be.

the effects of powerful AI are likely to be even more unpredictable than past technological changes, so all of this is unavoidably going to consist of guesses. But I am aiming for at least educated and useful guesses, which capture the flavor of what will happen even if most details end up being wrong. I’m including lots of details mainly because I think a concrete vision does more to advance discussion than a highly hedged and abstract one.

I am often turned off by the way many AI risk public figures (not to mention AI company leaders) talk about the post-AGI world, as if it’s their mission to single-handedly bring it about like a prophet leading their people to salvation. I think it’s dangerous to view companies as unilaterally shaping the world, and dangerous to view practical technological goals in essentially religious terms.

AI companies talking about all the amazing benefits of AI can come off like propagandists, or as if they’re attempting to distract from downsides.

the small community of people who do discuss radical AI futures often does so in an excessively “sci-fi” tone (featuring e.g. uploaded minds, space exploration, or general cyberpunk vibes). I think this causes people to take the claims less seriously, and to imbue them with a sort of unreality. To be clear, the issue isn’t whether the technologies described are possible or likely (the main essay discusses this in granular detail)—it’s more that the “vibe” connotatively smuggles in a bunch of cultural baggage and unstated assumptions about what kind of future is desirable, how various societal issues will play out, etc. The result often ends up reading like a fantasy for a narrow subculture, while being off-putting to most people.

Yet despite all of the concerns above, I really do think it’s important to discuss what a good world with powerful AI could look like, while doing our best to avoid the above pitfalls. In fact I think it is critical to have a genuinely inspiring vision of the future, and not just a plan to fight fires.

The five categories I am most excited about are: Biology and physical health Neuroscience and mental health Economic development and poverty Peace and governance Work and meaning

We could summarize this as a “country of geniuses in a datacenter”.

you might think that the world would be instantly transformed on the scale of seconds or days (“the Singularity”), as superior intelligence builds on itself and solves every possible scientific, engineering, and operational task almost immediately. The problem with this is that there are real physical and practical limits, for example around building hardware or conducting biological experiments. Even a new country of geniuses would hit up against these limits. Intelligence may be very powerful, but it isn’t magic fairy dust.

I believe that in the AI age, we should be talking about the marginal returns to intelligence7, and trying to figure out what the other factors are that are complementary to intelligence and that become limiting factors when intelligence is very high. We are not used to thinking in this way—to asking “how much does being smarter help with this task, and on what timescale?”—but it seems like the right way to conceptualize a world with very powerful AI.

in science many experiments are often needed in sequence, each learning from or building on the last. All of this means that the speed at which a major project—for example developing a cancer cure—can be completed may have an irreducible minimum that cannot be decreased further even as intelligence continues to increase.

Sometimes raw data is lacking and in its absence more intelligence does not help. Today’s particle physicists are very ingenious and have developed a wide range of theories, but lack the data to choose between them because particle accelerator data is so limited. It is not clear that they would do drastically better if they were superintelligent—other than perhaps by speeding up the construction of a bigger accelerator.

Many things cannot be done without breaking laws, harming humans, or messing up society. An aligned AI would not want to do these things (and if we have an unaligned AI, we’re back to talking about risks). Many human societal structures are inefficient or even actively harmful, but are hard to change while respecting constraints like legal requirements on clinical trials, people’s willingness to change their habits, or the behavior of governments. Examples of advances that work well in a technical sense, but whose impact has been substantially reduced by regulations or misplaced fears, include nuclear power, supersonic flight, and even elevators

Thus, we should imagine a picture where intelligence is initially heavily bottlenecked by the other factors of production, but over time intelligence itself increasingly routes around the other factors, even if they never fully dissolve (and some things like physical laws are absolute)10. The key question is how fast it all happens and in what order.

I am not talking about AI as merely a tool to analyze data. In line with the definition of powerful AI at the beginning of this essay, I’m talking about using AI to perform, direct, and improve upon nearly everything biologists do.

CRISPR was a naturally occurring component of the immune system in bacteria that’s been known since the 80’s, but it took another 25 years for people to realize it could be repurposed for general gene editing. They also are often delayed many years by lack of support from the scientific community for promising directions (see this profile on the inventor of mRNA vaccines; similar stories abound). Third, successful projects are often scrappy or were afterthoughts that people didn’t initially think were promising, rather than massively funded efforts. This suggests that it’s not just massive resource concentration that drives discoveries, but ingenuity.

there are hundreds of these discoveries waiting to be made if scientists were smarter and better at making connections between the vast amount of biological knowledge humanity possesses (again consider the CRISPR example). The success of AlphaFold/AlphaProteo at solving important problems much more effectively than humans, despite decades of carefully designed physics modeling, provides a proof of principle (albeit with a narrow tool in a narrow domain) that should point the way forward.

The AI trust crisis 14th December 2023 Dropbox added some new AI features. In the past couple of days these have attracted a firestorm of criticism. Benj Edwards rounds it up in Dropbox spooks users with new AI features that send data to OpenAI when used. The key issue here is that people are worried that their private files on Dropbox are being passed to OpenAI to use as training data for their models—a claim that is strenuously denied by Dropbox. As far as I can tell, Dropbox built some sensible features—summarize on demand, “chat with your data” via Retrieval Augmented Generation—and did a moderately OK job of communicating how they work... but when it comes to data privacy and AI, a “moderately OK job” is a failing grade. Especially if you hold as much of people’s private data as Dropbox does! Two details in particular seem really important. Dropbox have an AI principles document which includes this: Customer trust and the privacy of their data are our foundation. We will not use customer data to train AI models without consent. They also have a checkbox in their settings that looks like this: Update: Some time between me publishing this article and four hours later, that link stopped working. I took that screenshot on my own account. It’s toggled “on”—but I never turned it on myself. Does that mean I’m marked as “consenting” to having my data used to train AI models? I don’t think so: I think this is a combination of confusing wording and the eternal vagueness of what the term “consent” means in a world where everyone agrees to the terms and conditions of everything without reading them. But a LOT of people have come to the conclusion that this means their private data—which they pay Dropbox to protect—is now being funneled into the OpenAI training abyss. People don’t believe OpenAI # Here’s copy from that Dropbox preference box, talking about their “third-party partners”—in this case OpenAI: Your data is never used to train their internal models, and is deleted from third-party servers within 30 days. It’s increasing clear to me like people simply don’t believe OpenAI when they’re told that data won’t be used for training. What’s really going on here is something deeper then: AI is facing a crisis of trust. I quipped on Twitter: “OpenAI are training on every piece of data they see, even when they say they aren’t” is the new “Facebook are showing you ads based on overhearing everything you say through your phone’s microphone” Here’s what I meant by that. Facebook don’t spy on you through your microphone # Have you heard the one about Facebook spying on you through your phone’s microphone and showing you ads based on what you’re talking about? This theory has been floating around for years. From a technical perspective it should be easy to disprove: Mobile phone operating systems don’t allow apps to invisibly access the microphone. Privacy researchers can audit communications between devices and Facebook to confirm if this is happening. Running high quality voice recognition like this at scale is extremely expensive—I had a conversation with a friend who works on server-based machine learning at Apple a few years ago who found the entire idea laughable. The non-technical reasons are even stronger: Facebook say they aren’t doing this. The risk to their reputation if they are caught in a lie is astronomical. As with many conspiracy theories, too many people would have to be “in the loop” and not blow the whistle. Facebook don’t need to do this: there are much, much cheaper and more effective ways to target ads at you than spying through your microphone. These methods have been working incredibly well for years. Facebook gets to show us thousands of ads a year. 99% of those don’t correlate in the slightest to anything we have said out loud. If you keep rolling the dice long enough, eventually a coincidence will strike. Here’s the thing though: none of these arguments matter. If you’ve ever experienced Facebook showing you an ad for something that you were talking about out-loud about moments earlier, you’ve already dismissed everything I just said. You have personally experienced anecdotal evidence which overrides all of my arguments here.

One consistent theme I’ve seen in conversations about this issue is that people are much more comfortable trusting their data to local models that run on their own devices than models hosted in the cloud. The good news is that local models are consistently both increasing in quality and shrinking in size.

Women spend twice as much time as men, on average, on childcare and household work. All groups experience a free-time gender gap, with women having 13% less free time than men, on average. Mothers spend 2.3X as much time as fathers on the essential and unpaid work of taking care of home and family Young women (18-24) experience one of the largest free-time gender gaps, having 20% less free time than men their age Working women spend 2X as many hours per week as working men on childcare and household work combined Mothers who work part-time spend 3.8X as much time on childcare and household work as fathers who work part-time Married women without children spend 2.3X as much time as their male counterparts on household work Among Latinos, mothers spend more than 3.6X as much time as fathers taking care of children and doing household work

The unequal division of unpaid work in the home, such as cooking, cleaning, and shopping for food and clothing, is a powerful testament to the tenacity of old gender norms. Women do significantly more of this work than men do, even when there are no children living in the home. This holds true for women regardless of their marital status, their employment status, or their level of education.

Among all adults without children, women do twice as much household work as men, dedicating 12.3 hours per week to these tasks, on average, compared to 6 hours for men. Similarly, among all single people without children, women do nearly twice as much household work as men, spending 10.6 hours per week on household tasks compared to 5.7 hours for men.

getting married seems to exacerbate the burden of household work on women. Married women do substantially more household work than their single women peers, while married men spend just a few minutes a day more than their single peers. Married women without children do 2.3 times as much household work as their male counterparts (14.3 hours per week versus 6.2 hours).

Working women spend significantly more time than working men on unpaid work in the home. This is the case whether they work full-time or part-time. It is the case whether they have children or not. Take household work like cooking, laundry, and the like. Women who work full-time do 1.8 times as much as men who work full-time; they spend 9.7 hours per week on it compared to 5.4 hours for men. Women who work part-time do 2.5 times as much household work as men who work part-time.

Across every group studied, men spend more time than women socializing, watching sports or playing video games, or doing similar activities to relax or have fun. Women overall have 13% less free time than men, on average. The gap balloons among some groups, with women having up to one-quarter less free time than men.

Women overall have 13% less free time than men, on average. The gap balloons among some groups, with women having up to nearly one-quarter less free time than men.

there is a wide gulf between our ideals and our realities, as we have seen in this report on how Americans divide the work of taking care of home and family. One reason for the persistence of these gender disparities is that the U.S. has failed to modernize its public policies to fit 21st century economic realities. Even though 78% of American women are in the labor force, the nation’s social infrastructure is still largely premised on the assumption that mothers will be at home with children.

Every high-income nation in the world provides for paid leave for new parents—except the United States. Most provide ample financial and institutional support for childcare and preschool. Our peers devote a substantial share of public spending to family benefits, but the U.S. invests only minimally in supporting families. For instance, family benefits account for 2.4% of GDP in Germany compared to 0.6% in the United States.

Even when young children enter school, typical American school hours are grossly misaligned with the workday, forcing families to either spend money on after school care or reduce their work hours.

Public policy alone will not entirely eliminate these deeply rooted gender disparities. Cultural change is needed too. But smart policy can nudge along positive behavioral change that ultimately advances equity and equality. For example, several countries include mechanisms in their family policy to encourage fathers to take paid parent leave. Many Nordic nations have a ‘use it or lose it’ provision for fathers. Other countries, like Canada, provide extra paid weeks of leave to families if both parents use the time.

The unequal division of care work, particularly, affects women’s opportunity and well-being in ways that cannot be measured solely in dollars and cents.

One way Americans deal with the housing affordability crisis is to move to distant suburbs and exurbs, where housing is cheaper than it is in central cities and job hubs. The tradeoff, however, is typically a long commute to and from work. But for women who are caring for children or elderly relatives, long commutes are often not feasible. Children and elderly parents get sick and need to get to doctors in the middle of a workday. School hours begin too late and end too early to accommodate a commute to a 9-to-5 job.

when schools close due to climate-driven events, mothers might have to take unpaid time off of work or pay for childcare. As Americans experience more dangerous heat waves, wildfires, and floods driven by climate change, the caregiving demands on women can increase, as they are more likely to be the ones responsible for helping children and elderly adults stay out of harm’s way.

Earlier this month, the Gender Equity Policy Institute released a new analysis of the 2022 Time Use Survey, administered by the U.S. Bureau of Labor Statistics. The Time Use Survey is not perfect or granular, but it’s widely understood as the best and broadest data set of how Americans allocate their time. The survey asks respondents where and how they worked, how much time they allocated to childcare and/or domestic tasks, and how many hours were dedicated to “leisure.” You can take a closer look at the survey methodology and results here — and while they do breakdown results by race, age, income, education level, marital status, occupation, and number of children in the household, they do not distinguish between heterosexual and queer relationships (and also divide gender as male or female)….which means this analysis does not account for how queer relationships, in so many meanings of the word, fit into this larger analysis. With that said — the findings are stunning.

women spend more than twice as much time allocated to household work than men, even when they are single or do not have children. And getting married doesn’t split the load for women, as it theoretically would, but increases it: married women without children still do 2.3 times as much housework labor as their husbands.

men just had to hunt, but women back at the cave had to tend fires and care for kids and tan animal hides blah blah blah etc etc etc. These narratives make the nonsensical make sense: how else would you explain dramatically different approaches to cleanliness that seem to be fairly neatly divided by gender? It has to be biological, otherwise, well, women have just been caring about things that ultimately matter very little for centuries.

this particular argument falls apart when you consider women who aren’t “naturally” good at cleaning, or multi-tasking, or even caregiving — or just don’t like it. (Hello, all the ADHDers in my DMs also responding to these stats). Within this framework, the only way to conceive of these women is as faulty specimens: people who would be weeded out by natural selection. And yet somehow these people are still here, being women!

“Everything we call a sex difference, if you take a different perspective — what’s the power angle on this — often explains things,” neuroscientist Lise Eliot tells Darcy Lockman in All the Rage. “It has served men very well to assume that male-female differences are hard-wired.”

cleaning can be pleasurable the same way that popping a zit is pleasurable. It’s gross but oddly cathartic to erase abjection and mess, or to create order out of chaos. But you cannot separate the pleasures of degreasing the stove top from the pleasures of successfully meeting the demands of proper performance of womanhood. It’s like people who say they “just like being thin.” So much of why you like it is because it grants you societal power.

men can clean! I know men who are good at it, who take pleasure in it, who are also excellent noticers and multitaskers — and almost all of them were either 1) raised by single mothers or 2) spent time in the military. They were socialized, over an extended period, to understand cleanliness and multi-tasking as essential — with personal and social consequences if they did not.

“When people come over and our house is a mess, I feel this reflects badly on me as a person, adult, wife, and mother. My husband doesn't feel that at all,” a reader named Leah told me. “My father-in-law once told my husband he wished our house was cleaner when he comes to visit (don’t get me started). So now I scour the place top to bottom. My husband just shrugs and says: whatever, my dad can deal with it. The same applies to our kid. My husband can send him to school in ripped, dirty, or too-small clothes and not worry what people will think of him and his parenting skills. Me? I’m worried they are about to call CPS on us.”

if you’re “safe” being bad at womanhood — you can blow off societal repercussions. But a messy or dirty domestic space intersects with so many other stereotypes of class, race, education level, body size, and marital status. “My grandma grew up extremely poor in the South and I’ve always felt there was a strong class element to her standard of cleanliness,” a woman named Aimee told me. “Like even if you had no money, get a broom and a rag because you at least need a clean house.”

Or: a single woman can have a dirty apartment if she’s a hot, dirty mess; take away the hot thinness and she’s just a slovenly cat lady spinster.

I don’t think most people believe this! Yet these understandings endure, asking women to feel like failures or oddities, setting up shop in our relationships and slowly festering.

It is tremendously hard to divest from a way of understanding your own value in the world — and, by extension, others.

Men are socialized to see their spaces as utilitarian, spaces that serve them. Women, we serve our spaces.”

We can attempt, as a society, to socialize boys and men to be more clean, to notice more, to multitask more, to spend more time on domestic tasks and to allocate less time to leisure. We can teach men that their value (and their morality) is also rooted in their capacity to maintain a clean home: that they should also serve their spaces.

some of this work has happened: men in their 30s and 40s today do more domestic labor than their grandparents or great-grandparents did. But the gender discrepancy in domestic labor closed and closed and then….got stuck at a 65/35 split, and hasn’t budged in years.

The math just doesn’t math when it comes to domestic labor. When dads start allocating more time to parenting, it doesn’t subtract from the time moms spent parenting. When women leave the home to work for pay, the number of hours they allocate to domestic tasks doesn’t decrease, as one might assume, but goes up. If men do more, women still…..do more.

If you feel bound by these expectations: what would it take to free you? Because when I survey the hundreds of responses that arrived in my DMS when I posted the stats above, what I felt most was fatigue. Just look in the fucking drawer, is that where the bras go??? one woman wrote. God I’m so tired.

I’m always suspicious of arguments that ask anyone who’s subjugated, in some way, to do more. But in this case, the work is difficult, particularly for bourgeois women, because the work feels like something we’re so rarely asked to do: less.

At my grandmother's funeral, literally every single speaker talked about how much she loved her...house. How much care she put into it, how it was the joy of her life and source of her identity. When I was a kid, there was "clean" and there was "grandma clean". And after my mother died and I was rocked by grief, my grandmother attempted to console me by suggesting it wasn't that big of a loss because my mom (her daughter-in-law) wasn't a good housekeeper. (My mom was working full time, had four kids at home, and was literally dying.) I was so angry with my grandmother I barely every spoke to her again, and then at her funeral listening to everyone talk about her love of her house (!!) drove home what a waste of a life that was. She could have been kind. She could have been kind to my mom, who'd lost her own mother and was struggling. She could have been kind to me, her grand-daughter who was grieving. She could have had a life that meant something, but instead she focused on her house, which she saw as an extension of herself, and serviced her own pride and vanity. What a WASTE.

I feel like I am constantly drowning in a sea of paperwork/phone calls/emails - fighting with our health and dental insurance, calling contractors/plumbers/etc to do things for our house, filling out new tax paperwork. Not to mention fielding the barrage of spam emails/calls. I feel like this kind of stuff has expanded exponentially and I can’t tell if it’s just me, but it feels like every single one of these is a fight - someone sent the wrong paperwork to us, so I have to fill it out again; the insurance company makes a mistake on the claim and I have to file an appeal and follow up on it; contractor says they’re going to do something, doesn’t do it completely, and I have to fight to get them to come finish the job they agreed to. I get that we are all stretched so thin & distracted, but I am exhausted by trying to keep up with the apparently “essential” stuff of modern American adulthood

Deleting sections of a bill holding companies accountable for making data-driven decisions that could lead to discrimination in housing, employment, health care, and the like spurred a strong response from civil society organizations including the NAACP, the Japanese American Citizens League, the Autistic Self Advocacy Network, and Asian Americans Advancing Justice, among dozens of others.

In a letter this week to E&C Democrats, obtained by WIRED, the groups wrote: “Privacy rights and civil rights are no longer separate concepts—they are inextricably bound together and must be protected. Abuse of our data is no longer limited to targeted advertising or data breaches. Instead, our data are used in decisions about who gets a mortgage, who gets into which schools, and who gets hired—and who does not.”

these provisions contained generous “pro-business” caveats. For instance, users would be able to opt out of algorithmic decisionmaking only if doing so wasn’t “prohibitively costly” or “demonstrably impracticable due to technological limitations.” Similarly, companies could have limited the public’s knowledge about the results of any audits by simply hiring an independent assessor to complete the task rather than doing so internally.

I wanted to take care of everyone’s feelings. If I made them feel good, I would rewarded with their affection. For a long time, socializing involved playing a weird form of Mad-Libs: I wanted to say whatever you wanted to hear. I wanted to be assertive, but also understanding and reasonable and thoughtful.

I really took what I learned and ran with it. I wanted to master what I was bad at and made other people happy. I realized that it was: bad to talk too much about yourself good to show interest in other people’s hobbies, problems, and interests important to pay attention to body language my job to make sure that whatever social situation we were in was a delightful experience for everyone involved

As a SaaS startup scales, sales and customer support staff pay for themselves, and the marginal cost to serve your one-thousandth versus one-millionth user is near-zero. The result? Some SaaS companies achieve gross profit margins of 75 to 90 percent, rivaling Windows in its monopolistic heyday.

Rent-seeking has become an explicit playbook for many shameless SaaS investors. Private equity shop Thoma Bravo has acquired over four hundred software companies, repeatedly mashing products together to amplify lock-in effects so it can slash costs and boost prices—before selling the ravaged Franken-platform to the highest bidder.

In the Kafkaesque realm of health care, software giant Epic’s 1990s-era UI is still widely used for electronic medical records, a nuisance that arguably puts millions of lives at risk, even as it accrues billions in annual revenue and actively resists system interoperability. SAP, the antiquated granddaddy of enterprise resource planning software, has endured for decades within frustrated finance and supply chain teams, even as thousands of SaaS startups try to chip away at its dominance. Salesforce continues to grow at a rapid clip, despite a clunky UI that users say is “absolutely terrible” and “stuck in the 80s”—hence, the hundreds of “SalesTech” startups that simplify a single platform workflow (and pray for a billion-dollar acquihire to Benioff’s mothership). What these SaaS overlords might laud as an ecosystem of startup innovation is actually a reflection of their own technical shortcomings and bloated inertia.

Over 1,500 software startups are focused on billing and invoicing alone. The glut of tools extends to sectors without any clear need for complex software: no fewer than 378 hair salon platforms, 166 parking management solutions, and 70 operating systems for funeral homes and cemeteries are currently on the market. Billions of public pension and university endowment dollars are being burned on what amounts to hackathon curiosities, driven by the machinations of venture capital and private equity. To visit a much-hyped “demo day” at a startup incubator like Y Combinator or Techstars is to enter a realm akin to a high-end art fair—except the objects being admired are not texts or sculptures or paintings but slightly nicer faces for the drudgery of corporate productivity.

As popular as SaaS has become, much of the modern economy still runs on the humble, unfashionable spreadsheet. For all its downsides, there are virtues. Spreadsheets are highly interoperable between firms, partly because of another monopoly (Excel) but also because the generic .csv format is recognized by countless applications. They offer greater autonomy and flexibility, with tabular cells and formulas that can be shaped into workflows, processes, calculators, databases, dashboards, calendars, to-do lists, bug trackers, accounting workbooks—the list goes on. Spreadsheets are arguably the most popular programming language on Earth.

Johnson asked Hansen to figure out whether the lab had made a mistake. Detecting trace levels of chemicals was her specialty: She had recently written a doctoral dissertation about tiny particles in the atmosphere.

Hansen didn’t want to share her results until she was certain that they were correct, so she and her team spent several weeks analyzing more blood, often in time-consuming overnight tests. All the samples appeared to be contaminated. When Hansen used a more precise method, liquid chromatography, the results left little doubt that the chemical in the Red Cross blood was PFOS. Hansen now felt obligated to update her boss. Johnson was a towering, bearded man, and she liked him: He seemed to trust her expertise, and he found something to laugh about in most conversations. But, when she shared her findings, his response was cryptic. “This changes everything,” he said. Before she could ask him what he meant, he went into his office and closed the door.

In the middle of this testing, Johnson suddenly announced that he would be taking early retirement. After he packed up his office and left, Hansen felt adrift. She was so new to corporate life that her office clothes — pleated pants and dress shirts — still felt like a costume. Johnson had always guided her research, and he hadn’t told Hansen what she should do next. She reminded herself of what he had said — that the chemical wasn’t harmful in factory workers. But she couldn’t be sure that it was harmless.

Hansen’s bosses never told her that PFOS was toxic. In the weeks after Johnson left 3M, however, she felt that she was under a new level of scrutiny. One of her superiors suggested that her equipment might be contaminated, so she cleaned the mass spectrometer and then the entire lab. Her results didn’t change. Another encouraged her to repeatedly analyze her syringes, bags and test tubes, in case they had tainted the blood. (They had not.) Her managers were less concerned about PFOS, it seemed to Hansen, than about the chance that she was wrong.

Hansen doubted herself. She was 28 and had only recently earned her Ph.D. But she continued her experiments, if only to respond to the questions of her managers. 3M bought three additional mass spectrometers, which each cost more than a car, and Hansen used them to test more blood samples. In late 1997, her new boss, Bacon, even had her fly out to the company that manufactured the machines, so that she could repeat her tests there. She studied the blood of hundreds of people from more than a dozen blood banks in various states. Each sample contained PFOS. The chemical seemed to be everywhere.

After the war, 3M hired some Manhattan Project chemists and began mass-producing chains of carbon atoms bonded to fluorine atoms. The resulting chemicals proved to be astonishingly versatile, in part because they resist oil, water and heat. They are also incredibly long-lasting, earning them the moniker “forever chemicals.”

One afternoon in 1998, a trim 3M epidemiologist named Geary Olsen arrived with several vials of blood and asked her to test them. The next morning, she read the results to him and several colleagues — positive for PFOS. As Hansen remembers it, Olsen looked triumphant. “Those samples came from my horse,” he said — and his horse certainly wasn’t eating at McDonald’s or trotting on Scotchgarded carpets. Hansen felt that he was trying to humiliate her. (Olsen did not respond to requests for comment.) What Hansen wanted to know was how PFOS was making its way into animals.

PFOS, a man-made chemical produced by her employer, really was in human blood, practically everywhere. Hansen’s team found it in Swedish blood samples from 1957 and 1971. After that, her lab analyzed blood that had been collected before 3M created PFOS. It tested negative. Apparently, fluorochemicals had entered human blood after the company started selling products that contained them. They had leached out of 3M’s sprays, coatings and factories — and into all of us.

Almost as soon as Hansen placed her first transparency on the projector, the attendees began interrogating her: Why did she do this research? Who directed her to do it? Whom did she inform of the results? The executives seemed to view her diligence as a betrayal: Her data could be damaging to the company. She remembers defending herself, mentioning Newmark’s similar work in the ’70s and trying, unsuccessfully, to direct the conversation back to her research. While the executives talked over her, Hansen noticed that DeSimone’s eyes had closed and that his chin was resting on his dress shirt. The CEO appeared to have fallen asleep. (DeSimone died in 2017. A company spokesperson did not answer my questions about the meeting.)

In 2002, when 3M announced that it would be replacing PFOS with another fluorochemical, PFBS, Hansen knew that it, too, would remain in the environment indefinitely. Still, she decided not to involve herself. She skipped over articles about the chemicals in scientific journals and newspapers, where they were starting to be linked to possible developmental, immune system and liver problems.

In the 2016 book “Secrecy at Work,” two management theorists, Jana Costas and Christopher Grey, argue that there is nothing inherently wrong or harmful about keeping secrets. Trade secrets, for example, are protected by federal and state law on the grounds that they promote innovation and contribute to the economy. The authors draw on a large body of sociological research to illustrate the many ways that information can be concealed. An organization can compartmentalize a secret by slicing it into smaller components, preventing any one person from piecing together the whole. Managers who don’t want to disclose sensitive information may employ “stone-faced silence.” Secret-keepers can form a kind of tribe, dependent on one another’s continued discretion; in this way, even the existence of a secret can be kept secret. Such techniques become pernicious, Costas and Grey write, when a company keeps a dark secret, a secret about wrongdoing.

Hansen’s superiors had given her the same explanation that they gave journalists, she finally said — that factory workers were fine, so people with lower levels would be, too. Her specialty was the detection of chemicals, not their harms. “You’ve got literally the medical director of 3M saying, ‘We studied this, there are no effects,’” she told me. “I wasn’t about to challenge that.” Her income had helped to support a family of five. Perhaps, I wondered aloud, she hadn’t really wanted to know whether her company was poisoning the public.

Jim Johnson, who is now an 81-year-old widower, lives with several dogs in a pale-yellow house in North Dakota. When I first called him, he said that he had begun researching PFOS in the ’70s. “I did a lot of the very original work on it,” he told me. He said that when he saw the chemical’s structure he understood “within 20 minutes” that it would not break down in nature. Shortly thereafter, one of his experiments revealed that PFOS was binding to proteins in the body, causing the chemical to accumulate over time. He told me that he also looked for PFOS in an informal test of blood from the general population, around the late ’70s, and was not surprised when he found it there.

Johnson said that he eventually tired of arguing with the few colleagues with whom he could speak openly about PFOS. “It was time,” he said. So he hired an outside lab to look for the chemical in the blood of 3M workers, knowing that it would also test blood bank samples for comparison — the first domino in a chain that would ultimately take the compound off the market. Oddly, he compared the head of the lab to a vending machine. “He gave me what I paid for,” Johnson said. “I knew what would happen.” Then Johnson tasked Hansen with something that he had long avoided: going beyond his initial experiments and meticulously documenting the chemical’s ubiquity. While Hansen took the heat, he took early retirement. Johnson described Hansen as though she were a vending machine, too. “She did what she was supposed to do with the tools I left her,” he said.

I pointed out that Hansen had suffered professionally and personally, and that she now feels those experiences tainted her career. “I didn’t say I was a nice guy,” Johnson replied, and laughed. After four hours, we were nearing the bottom of our bottomless coffees.

Average levels of PFOS are falling, but nearly all people have at least one forever chemical in their blood, according to the Centers for Disease Control and Prevention. “When you have a contaminated site, you can clean it up,” Elsie Sunderland, an environmental chemist at Harvard University, told me. “When you ubiquitously introduce a toxicant at a global scale, so that it’s detectable in everyone ... we’re reducing public health on an incredibly large scale.” Once everyone’s blood is contaminated, there is no control group with which to compare, making it difficult to establish responsibility.

At least 45% of U.S. tap water is estimated to contain one or more forever chemicals, and one drinking water expert told me that the cost of removing them all would likely reach $100 billion.

n 2022, 3M said that it would stop making PFAS and would “work to discontinue the use of PFAS across its product portfolio,” by the end of 2025 — a pledge that it called “another example of how we are positioning 3M for continued sustainable growth.” But it acknowledged that more than 16,000 of its products still contained PFAS.

It symbolizes everything everyone has ever hated about digitization. It celebrates a lossy, creative compression for the most flimsy reason: An iPad shedding an irrelevant millimeter or two. It's destruction of beloved musical instruments is the perfect metaphor for how utterly tone-deaf technologists are capable of being. But the real story is just how little saved up goodwill Apple had in the bank to compensate for the outrage.

This should all be eerily familiar to anyone who saw Microsoft fall from grace in the 90s. From being America's favorite software company to being the bully pursued by the DOJ for illegalities. Just like Apple now, Microsoft's reputation and good standing suddenly evaporated seemingly overnight once enough critical stories had accumulated about its behavior.

Apple had such treasure chest of goodwill from decades as first an underdog, then unchallenged innovator. But today they're a near three-trillion dollar company, battling sovereigns on both sides of the Atlantic, putting out mostly incremental updates to mature products.

what if every police patrol car had a bank of cameras that scan not just every number plate but every face within a hundred yards against a national database of outstanding warrants? What if the cameras in the subway do that? All the connected cameras in the city? China is already trying to do this, and we seem to be pretty sure we don’t like that, but why? One could argue that there’s no difference in principle, only in scale, but a change in scale can itself be a change in principle.

Generative AI is now creating a lot of new examples of scale itself as a difference in principle. You could look the emergent abuse of AI image generators, shrug, and talk about Photoshop: there have been fake nudes on the web for as long as there’s been a web. But when high-school boys can load photos of 50 or 500 classmates into an ML model and generate thousands of such images (let’s not even think about video) on a home PC (or their phone), that does seem like an important change. Faking people’s voices has been possible for a long time, but it’s new and different that any idiot can do it themselves. People have always cheated at homework and exams, but the internet made it easy and now ChatGPT makes it (almost) free. Again, something that has always been theoretically possible on a small scale becomes practically possible on a massive scale, and that changes what it means.

This might be a genuinely new and bad thing that we don’t like at all; or, it may be new and we decide we don’t care; we may decide that it’s just a new (worse?) expression of an old thing we don’t worry about; and, it may be that this was indeed being done before, even at scale, but somehow doing it like this makes it different, or just makes us more aware that it’s being done at all. Cambridge Analytica was a hoax, but it catalysed awareness of issues that were real

The ability of generative AI tools to analyse huge datasets can also be used to help spark creative inspiration. This can help us if we’re struggling for time or battling writer’s block. For example, if a social media manager is looking for ideas on how to engage alumni on Instagram, they could ask ChatGPT for suggestions based on recent popular content. They could then pick the best ideas from ChatGPT’s response and adapt them. We may use these tools in a similar way to how we ask a colleague for an idea on how to approach a creative task.

We may use these tools in a similar way to how we use search engines for researching topics and will always carefully fact-check before publication.

we will not publish any press releases, articles, social media posts, blog posts, internal emails or other written content that is 100% produced by generative AI. We will always apply brand guidelines, fact-check responses, and re-write in our own words.

We may use these tools to make minor changes to a photo to make it more usable without changing the subject matter or original essence. For example, if a website manager needs a photo in a landscape ratio but only has one in a portrait ratio, they could use Photoshop’s inbuilt AI tools to extend the background of the photo to create an image with the correct dimensions for the website.

what consequences has the rise of management consulting had for the organization of American business and the lives of American workers? The answers to these questions put management consultants at the epicenter of economic inequality and the destruction of the American middle class.

Managers do not produce goods or deliver services. Instead, they plan what goods and services a company will provide, and they coordinate the production workers who make the output. Because complex goods and services require much planning and coordination, management (even though it is only indirectly productive) adds a great deal of value. And managers as a class capture much of this value as pay. This makes the question of who gets to be a manager extremely consequential.

In the middle of the last century, management saturated American corporations. Every worker, from the CEO down to production personnel, served partly as a manager, participating in planning and coordination along an unbroken continuum in which each job closely resembled its nearest neighbor.

Even production workers became, on account of lifetime employment and workplace training, functionally the lowest-level managers. They were charged with planning and coordinating the development of their own skills to serve the long-run interests of their employers.

At McDonald’s, Ed Rensi worked his way up from flipping burgers in the 1960s to become CEO. More broadly, a 1952 report by Fortune magazine found that two-thirds of senior executives had more than 20 years’ service at their current companies.

Top executives enjoyed commensurately less control and captured lower incomes. This democratic approach to management compressed the distribution of income and status. In fact, a mid-century study of General Motors published in the Harvard Business Review—completed, in a portent of what was to come, by McKinsey’s Arch Patton—found that from 1939 to 1950, hourly workers’ wages rose roughly three times faster than elite executives’ pay. The management function’s wide diffusion throughout the workforce substantially built the mid-century middle class.

The earliest consultants were engineers who advised factory owners on measuring and improving efficiency at the complex factories required for industrial production. The then-leading firm, Booz Allen, did not achieve annual revenues of $2 million until after the Second World War. McKinsey, which didn’t hire its first Harvard M.B.A. until 1953, retained a diffident and traditional ethos

A new ideal of shareholder primacy, powerfully championed by Milton Friedman in a 1970 New York Times Magazine article entitled “The Social Responsibility of Business is to Increase its Profits,” gave the newly ambitious management consultants a guiding purpose. According to this ideal, in language eventually adopted by the Business Roundtable, “the paramount duty of management and of boards of directors is to the corporation’s stockholders.” During the 1970s, and accelerating into the ’80s and ’90s, the upgraded management consultants pursued this duty by expressly and relentlessly taking aim at the middle managers who had dominated mid-century firms, and whose wages weighed down the bottom line.

Management consultants thus implemented and rationalized a transformation in the American corporation. Companies that had long affirmed express “no layoff” policies now took aim at what the corporate raider Carl Icahn, writing in the The New York Times in the late 1980s, called “corporate bureaucracies” run by “incompetent” and “inbred” middle managers. They downsized in response not to particular business problems but rather to a new managerial ethos and methods; they downsized when profitable as well as when struggling, and during booms as well as busts.

Downsizing was indeed wrenching. When IBM abandoned lifetime employment in the 1990s, local officials asked gun-shop owners around its headquarters to close their stores while employees absorbed the shock.

In some cases, downsized employees have been hired back as subcontractors, with no long-term claim on the companies and no role in running them. When IBM laid off masses of workers in the 1990s, for example, it hired back one in five as consultants. Other corporations were built from scratch on a subcontracting model. The clothing brand United Colors of Benetton has only 1,500 employees but uses 25,000 workers through subcontractors.

The shift from permanent to precarious jobs continues apace. Buttigieg’s work at McKinsey included an engagement for Blue Cross Blue Shield of Michigan, during a period when it considered cutting up to 1,000 jobs (or 10 percent of its workforce). And the gig economy is just a high-tech generalization of the sub-contractor model. Uber is a more extreme Benetton; it deprives drivers of any role in planning and coordination, and it has literally no corporate hierarchy through which drivers can rise up to join management.

In effect, management consulting is a tool that allows corporations to replace lifetime employees with short-term, part-time, and even subcontracted workers, hired under ever more tightly controlled arrangements, who sell particular skills and even specified outputs, and who manage nothing at all.

the managerial control stripped from middle managers and production workers has been concentrated in a narrow cadre of executives who monopolize planning and coordination. Mid-century, democratic management empowered ordinary workers and disempowered elite executives, so that a bad CEO could do little to harm a company and a good one little to help it.

Whereas at mid-century a typical large-company CEO made 20 times a production worker’s income, today’s CEOs make nearly 300 times as much. In a recent year, the five highest-paid employees of the S&P 1500 (7,500 elite executives overall), obtained income equal to about 10 percent of the total profits of the entire S&P 1500.

as Kiechel put it dryly, “we are not all in this together; some pigs are smarter than other pigs and deserve more money.” Consultants seek, in this way, to legitimate both the job cuts and the explosion of elite pay. Properly understood, the corporate reorganizations were, then, not merely technocratic but ideological.

corporate reorganizations have deprived companies of an internal supply of managerial workers. When restructurings eradicated workplace training and purged the middle rungs of the corporate ladder, they also forced companies to look beyond their walls for managerial talent—to elite colleges, business schools, and (of course) to management-consulting firms. That is to say: The administrative techniques that management consultants invented created a huge demand for precisely the services that the consultants supply.

Consulting, like law school, is an all-purpose status giver—“low in risk and high in reward,” according to the Harvard Crimson. McKinsey also hopes that its meritocratic excellence will legitimate its activities in the eyes of the broader world. Management consulting, Kiechel observed, acquired its power and authority not from “silver-haired industry experience but rather from the brilliance of its ideas and the obvious candlepower of the people explaining them, even if those people were twenty-eight years old.”

A deeper objection to Buttigieg’s association with McKinsey concerns not whom the firm represents but the central role the consulting revolution has played in fueling the enormous economic inequalities that now threaten to turn the United States into a caste society.

Meritocrats like Buttigieg changed not just corporate strategies but also corporate values.

GM may aspire to build good cars; IBM, to make typewriters, computers, and other business machines; and AT&T, to improve communications. Executives who rose up through these companies, on the mid-century model, were embedded in their firms and embraced these values, so that they might even have come to view profits as a salutary side effect of running their businesses well.

When management consulting untethered executives from particular industries or firms and tied them instead to management in general, it also led them to embrace the one thing common to all corporations: making money for shareholders. Executives raised on the new, untethered model of management aim exclusively and directly at profit: their education, their career arc, and their professional role conspire to isolate them from other workers and train them single-mindedly on the bottom line.

American democracy, the left believes, cannot be rejuvenated by persuading elites to deploy their excessive power somehow more benevolently. Instead, it requires breaking the stranglehold that elites have on our economics and politics, and reempowering everyone else.

We might think of the shifting relationship of human beings to the natural world in the terms offered by German sociologist Gerd-Günter Voß, who has traced our movement through three different models of the “conduct of life.”

The first, and for much of human history the only conduct of life, is what he calls the traditional. Your actions within the traditional conduct of life proceed from social and familial circumstances, from what is thus handed down to you. In such a world it is reasonable for family names to be associated with trades, trades that will be passed down from father to son: Smith, Carpenter, Miller.

But the rise of the various forces that we call “modernity” led to the emergence of the strategic conduct of life: a life with a plan, with certain goals — to get into law school, to become a cosmetologist, to get a corner office.

thanks largely to totalizing technology’s formation of a world in which, to borrow a phrase from Marx and Engels, “all that is solid melts into air,” the strategic model of conduct is replaced by the situational. Instead of being systematic planners, we become agile improvisers: If the job market is bad for your college major, you turn a side hustle into a business. But because you know that your business may get disrupted by the tech industry, you don’t bother thinking long-term; your current gig might disappear at any time, but another will surely present itself, which you will assess upon its arrival.

The movement through these three forms of conduct, whatever benefits it might have, makes our relations with nature increasingly instrumental. We can see this shift more clearly when looking at our changing experience of time

Within the traditional conduct of life, it is necessary to take stewardly care of the resources required for the exercise of a craft or a profession, as these get passed on from generation to generation.

But in the progression from the traditional to the strategic to the situational conduct of life, continuity of preservation becomes less valuable than immediacy of appropriation: We need more lithium today, and merely hope to find greater reserves — or a suitable replacement — tomorrow. This revaluation has the effect of shifting the place of the natural order from something intrinsic to our practices to something extrinsic. The whole of nature becomes what economists tellingly call an externality.

The basic argument of the SCT goes like this. We live in a technopoly, a society in which powerful technologies come to dominate the people they are supposed to serve, and reshape us in their image. These technologies, therefore, might be called prescriptive (to use Franklin’s term) or manipulatory (to use Illich’s). For example, social networks promise to forge connections — but they also encourage mob rule.

all things increasingly present themselves to us as technological: we see them and treat them as what Heidegger calls a “standing reserve,” supplies in a storeroom, as it were, pieces of inventory to be ordered and conscripted, assembled and disassembled, set up and set aside

In his exceptionally ambitious book The Question Concerning Technology in China (2016) and in a series of related essays and interviews, Hui argues, as the title of his book suggests, that we go wrong when we assume that there is one question concerning technology, the question, that is universal in scope and uniform in shape. Perhaps the questions are different in Hong Kong than in the Black Forest. Similarly, the distinction Heidegger draws between ancient and modern technology — where with modern technology everything becomes a mere resource — may not universally hold.

Thesis: Technology is an anthropological universal, understood as an exteriorization of memory and the liberation of organs, as some anthropologists and philosophers of technology have formulated it; Antithesis: Technology is not anthropologically universal; it is enabled and constrained by particular cosmologies, which go beyond mere functionality or utility. Therefore, there is no one single technology, but rather multiple cosmotechnics.

I think Hui’s cosmotechnics, generously leavened with the ironic humor intrinsic to Daoism, provides a genuine Way — pun intended — beyond the limitations of the Standard Critique of Technology. I say this even though I am not a Daoist; I am, rather, a Christian. But it should be noted that Daoism is both daojiao, an organized religion, and daojia, a philosophical tradition. It is daojia that Hui advocates, which makes the wisdom of Daoism accessible and attractive to a Christian like me. Indeed, I believe that elements of daojia are profoundly consonant with Christianity, and yet underdeveloped in the Christian tradition, except in certain modes of Franciscan spirituality, for reasons too complex to get into here.

this technological Daoism as an embodiment of daojia, is accessible to people of any religious tradition or none. It provides a comprehensive and positive account of the world and one’s place in it that makes a different approach to technology more plausible and compelling. The SCT tends only to gesture in the direction of a model of human flourishing, evokes it mainly by implication, whereas Yuk Hui’s Daoist model gives an explicit and quite beautiful account.

The application of Daoist principles is most obvious, as the above exposition suggests, for “users” who would like to graduate to the status of “non-users”: those who quietly turn their attention to more holistic and convivial technologies, or who simply sit or walk contemplatively. But in the interview I quoted from earlier, Hui says, “Some have quipped that what I am speaking about is Daoist robots or organic AI” — and this needs to be more than a quip. Peter Thiel’s longstanding attempt to make everyone a disciple of René Girard is a dead end. What we need is a Daoist culture of coders, and people devoted to “action without acting” making decisions about lithium mining.

Tools that do not contribute to the Way will neither be worshipped nor despised. They will simply be left to gather dust as the people choose the tools that will guide them in the path of contentment and joy: utensils to cook food, devices to make clothes. Of course, the food of one village will differ from that of another, as will the clothing. Those who follow the Way will dwell among the “ten thousand things” of this world — what we call nature — in a certain manner that cannot be specified legally: Verse 18 of the Tao says that when virtue arises only from rules, that is a sure sign that the Way is not present and active. A cosmotechnics is a living thing, always local in the specifics of its emergence in ways that cannot be specified in advance.

It is from the ten thousand things that we learn how to live among the ten thousand things; and our choice of tools will be guided by what we have learned from that prior and foundational set of relations. This is cosmotechnics.

Multiplicity avoids the universalizing, totalizing character of technopoly. The adherents of technopoly, Hui writes, “wishfully believ[e] that the world process will stamp out differences and diversities” and thereby achieve a kind of techno-secular “theodicy,” a justification of the ways of technopoly to its human subjects. But the idea of multiple cosmotechnics is also necessary, Hui believes, in order to avoid the simply delusional attempt to find “a way out of modernity” by focusing on the indigenous or biological “Other.” An aggressive hostility to modernity and a fetishizing of pre-modernity is not the Daoist way.

“I believe that to overcome modernity without falling back into war and fascism, it is necessary to reappropriate modern technology through the renewed framework of a cosmotechnics.” His project “doesn’t refuse modern technology, but rather looks into the possibility of different technological futures.”

“Thinking rooted in the earthy virtue of place is the motor of cosmotechnics. However, for me, this discourse on locality doesn’t mean a refusal of change and of progress, or any kind of homecoming or return to traditionalism; rather, it aims at a re-appropriation of technology from the perspective of the local and a new understanding of history.”

Always Coming Home illustrates cosmotechnics in a hundred ways. Consider, for instance, information storage and retrieval. At one point we meet the archivist of the Library of the Madrone Lodge in the village of Wakwaha-na. A visitor from our world is horrified to learn that while the library gives certain texts and recordings to the City of Mind, some of their documents they simply destroy. “But that’s the point of information storage and retrieval systems! The material is kept for anyone who wants or needs it. Information is passed on — the central act of human culture.” But that is not how the librarian thinks about it. “Tangible or intangible, either you keep a thing or you give it. We find it safer to give it” — to practice “unhoarding.”

It is not information, but relation. This too is cosmotechnics.

a cosmotechnics is the point at which a way of life is realized through making. The point may be illustrated with reference to an ancient tale Hui offers, about an excellent butcher who explains to a duke what he calls the Dao, or “way,” of butchering. The reason he is a good butcher, he says, it not his mastery of a skill, or his reliance on superior tools. He is a good butcher because he understands the Dao: Through experience he has come to rely on his intuition to thrust the knife precisely where it does not cut through tendons or bones, and so his knife always stays sharp. The duke replies: “Now I know how to live.” Hui explains that “it is thus the question of ‘living,’ rather than that of technics, that is at the center of the story.”

Players must assume full responsibility for their cards, a strategy dubbed “C.P.E.” — Conception, Planning, Execution — that was designed to combat the male tendency to execute (pick up milk from the market when asked) but leave the conceiving (recognizing that your toddler only drinks 2 percent milk) and planning (monitoring the fridge to ensure the 2 percent doesn’t run out) to the female partner. In other words, as Rodsky told me, “Own. Your. Shit.” Partners also must agree to a Minimum Standard of Care (or “M.S.C.”) for tasks, meaning that even if the owner of the Garbage card is fine with empty pizza boxes piling up on the kitchen counter, he may still have to throw them out promptly.

often, several persistent challenges emerge. Implementing the practice is time-consuming and so is maintaining it. In fact, in all of my interviews, I failed to find a couple who followed the rules to a tee for longer than a few months. Just to get Fair Play off the ground, the initiator has to read the book, procure the cards, create an organizational system (I’ve seen everything from Google spreadsheets so detailed they resemble stock-portfolio trackers to oversize whiteboards dotted with custom Etsy chore magnets), then explain it all to a partner.

A woman recently polled one group to ask whether her spouse should be expected to pick up his own socks if she owned the Cleaning card. Another turned to the Fair Play community after a family member died, seeking advice on how to ask her partner to cover her chores while she grieved. All this time and effort falls overwhelmingly on women by design.

social movements start with the oppressed,” Rodsky says, because “people who want the status quo maintained” won’t push for change. As Rodsky recently said on a podcast, “You teach somebody something now because it will benefit your future hours.” But the prep work has pushed many women away because, as one Brooklyn mother put it, it’s “more than I can handle right now.”

Stories also abound of partners agreeing to cards only to quietly drop them. This sometimes leads to “chore chicken,” a resentment-fueled phenomenon in which Partner A refuses to pick up the slack on Partner B’s tasks, so the dirty dishes fester or shirts languish at the dry cleaners. Partner A silently rages because this isn’t how C.P.E is supposed to work.

Other spouses, when invited to play fair, have responded with “I get it. You want me to do more. But I don’t want to play a game. Just tell me what to do.” That’s what Paige Connell, 33, a Boston mom of four under 6, heard when she first showed her husband the deck. “But that places the burden right back on the woman,” she says. “I don’t want to be his project manager.”

The manner in which the “fewer cards” partner responds to the Fair Play system can serve as a barometer for the health of a relationship, says Jenny Cooke Malstrom, a marriage and family therapist in Seattle. Fair Play doesn’t cause divorce, she says, but it’s more likely to backfire in at-risk relationships where the baseline level of love and respect is already low. “To have your husband straight up say ‘This isn’t important’ makes what’s already implicitly happening explicit,” Malstrom says.

a 2017 Sex Roles study which suggested that the perception of fairness in family labor is a stronger predictor of maternal mental health than the actual division of labor. The study is the primary reason her system intentionally prioritizes feelings of equity over literal equality. Rodsky isn’t the only relationship expert to say that a 50/50 chore split isn’t realistic. Brené Brown famously went viral in a 2020 appearance on The Tim Ferriss Podcast, when she explained that the balance of responsibility in her marriage changes day to day depending on each partner’s mental and emotional capacity — and that constant responsibility trading is critical to supporting one another.

“It was never written to be prescriptive. My intention was to [help women] hold their boundaries in a different way. Any movement towards ‘I’m not going to live this way anymore,’ I consider a win.”

With iA Writer 7 you can manually mark ChatGPT’s contributions as AI text. AI text is greyed out. This allows you to separate and control what you borrow and what you type. By splitting what you type and what you pasted, you can make sure that you speak your mind with your voice, rhythm and tone.

As a dialog partner AI makes you think more and write better. As ghost writer it takes over and you lose your voice. Yet, sometimes it helps to paste its replies and notes. And if you want to use that information, you rewrite it to make it our own. So far, in traditional apps we are not able to easily see what we wrote and what we pasted from AI. iA Writer lets you discern your words from what you borrowed as you write on top of it. As you type over the AI generated text you can see it becoming your own. We found that in most cases, and with the exception of some generic pronouns and common verbs like “to have” and “to be”, most texts profit from a full rewrite.

we believe that using AI for writing will likely become as common as using dishwashers, spellcheckers, and pocket calculators. The question is: How will it be used? Like spell checkers, dishwashers, chess computers and pocket calculators, writing with AI will be tied to varying rules in different settings.

We suggest using AI’s ability to replace thinking not for ourselves but for writing in dialogue. Don’t use it as a ghost writer. Because why should anyone bother to read what you didn’t write? Use it as a writing companion. It comes with a ChatUI, so ask it questions and let it ask you questions about what you write. Use it to think better, don’t become a vegetable.

This paper maps concepts from AI alignment onto a basic, three step interaction cycle, yielding a corresponding set of alignment objectives: 1) specification alignment: ensuring the user can efficiently and reliably communicate objectives to the AI, 2) process alignment: providing the ability to verify and optionally control the AI's execution process, and 3) evaluation support: ensuring the user can verify and understand the AI's output.

the notion of a Process Gulf, which highlights how differences between human and AI processes can lead to challenges in AI control.