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The Code That Controls Your Money | Wealthsimple

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The Code That Controls Your Money | Wealthsimple

 

When Thomas first started programming, it was 1969. He was a kid just out of high school in Toronto, without any particular life goal. His father was a carpenter, but good luck following in his family’s footsteps; Thomas was all thumbs. “My father knew I couldn’t hammer two pieces of wood together,” he laughs.

So his mother suggested something weird and newfangled: What about… computer programming?

Computers, in 1969, were still strange new curiosities, the size of big cabinets. But companies around the world were realizing they were invaluable for any task that required a lot of rapid-fire accounting, like tallying up payroll. Jobs were on offer to anyone who could learn even a little coding. So Thomas found “some fly-by-night, little pop-up school” in downtown Toronto, and over the next two months, learned the hot computer language of the day: COBOL (Common Business-Oriented Language).

After he graduated, he got hired in the check-sorting department of a major Canadian bank. (He doesn’t want me to name it, banks are secretive; “Thomas,” I should mention, is a pseudonym, if you hadn’t guessed that already.) Thomas wasn’t yet a programmer for the bank then, but over the next few years he made it clear he wanted to be, and his employer paid for him to do a bunch of honest-to-goodness college courses in coding, and in 1978 he began a long career at the bank as a programmer.

Thomas loved it. It was like constant puzzle-solving, a game of mental chess. He’d sit at his desk, writing out his code by hand, then give it to a “punchcard operator” who’d put holes in cards to represent his programming instructions. Twice a day they’d feed those cards into the huge “mainframe” computers at the bank. It would take hours for Thomas to find out if his code had actually worked correctly, or whether he’d made a goof that grounded things to a halt. If he had did, he’d pore over the error statements, rewrite the COBOL, and try again.

Over the next few years, Thomas became good at COBOL, and wrote thousands of invaluable lines of code. When the bank issued payments, it was his code, every day, helping them tally it all up correctly. As the ‘70s and ’80s and ’90s wore on, he and his coder colleagues probably wrote tens of millions of lines of COBOL. There’s one system he’s particularly proud of, a lightning-fast program that can process “anywhere between three and five million transactions a day. That’s my baby!” He wrote his first bits of that program in 1988.

And the thing is — that code is still running today.

Thomas retired from the bank in 2007 at about 60, and when he left, the bank was still relying on the system, which by then was 20 years old and written when Thomas had a lot more hair and when Phil Collins’s “Groovy Kind of Love” was a chart-topping hit. These days, the code is over three decades old. It’s still crunching millions of records a day. Indeed, he believes most of the code he and his peers wrote back in the day is still running because the bank can’t function without it.

In fact, these days, when the phone rings in the house Thomas retired to — in a small town outside of Toronto — it will occasionally be someone from the bank. Hey, they’ll say, can you, uh, help… update your code? Maybe add some new features to it? Because, as it turns out, the bank no longer employs anyone who understands COBOL as well as Thomas does, who can dive in and tweak it to perform a new task. Nearly all the COBOL veterans, the punch-card jockeys who built the bank’s crucial systems way back when, who know COBOL inside and out — they’ve retired. They’ve left the building, just like Thomas. And few young coders have any interest in learning a dusty, 50-year-old computer language. They’re much more excited by buzzier new fields, like Toronto’s booming artificial-intelligence scene. They’re learning fresh new coding languages.

So this large bank is still dependent on people like, Thomas, who is 73, to not only keep things running, but add new features and improvements.

Will his COBOL outlive him?

“Probably.”

COBOL democratized coding; companies could take everyday people and train them to be useful COBOL programmers in a few months.

That bank is not alone. COBOL programs — some written so long ago that color TV wasn’t even a thing yet — are everywhere in our daily lives.

Consider: Over 80% of in-person transactions at U.S. financial institutions use COBOL. Fully 95% of the time you swipe your bank card, there’s COBOL running somewhere in the background. The Bank of New York Mellon in 2012 found it had 112,500 individual COBOL programs, constituting almost 350 million lines; that is probably typical for most big financial institutions. When your boss hands you your paycheck, odds are it was calculated using COBOL. If you invest, your stock trades run on it too. So does health care: Insurance companies in the U.S. use “adjudication engines’”— software that figures out what a doctor or drug company will get paid for a service — which were written in COBOL. Wonder why, when you’re shopping at a retailer you will see a clerk typing into an old-style terminal, with green text on a black background? It’s because the inventory system is using COBOL. Or why you see airline booking agents use that same black screen with green type to change your flight? “Oh, that’s COBOL — that’s definitely COBOL,” laughs Craig Bailey, a senior engineer at Faircom, a firm that makes software to help firms manage those old systems.

No one quite knows how much COBOL is out there, but estimates suggest there are as many as 240 billion lines of the code quietly powering many of the most crucial parts of our everyday lives. “The second most valuable asset in the United States — after oil — is the 240 billion lines of COBOL,” says Philip Teplitzky, who’s slung COBOL for decades for banks across the U.S.

We’re often told that tech thrives because of its new, pioneering innovations — its willingness to do bold new things with code, to “move fast and break things,” as a young Mark Zuckerberg famously plastered on the wall at Facebook. And it’s certainly true that every day we see wild new code released, written in fresher, newer languages. If you’ve seen that crazy new AI that can write sentences like a human, its creation relied on Python, the well-known new computer language. When Facebook unleashes some new features on its browsers’ app, the coders are often using JavaScript, another hot one.

But in older, massive industries central to the economy? COBOL’s still omnipresent. It makes it hard to innovate. How can you tinker, bolt on new features, using an ancient language that energetic young coders have no interest in? If big old banks aren’t the firms pushing forward with services like Venmo or Square or other fizzy “fintech” products, it would follow that COBOL is part of the problem. But if that’s the case why, exactly, is Thomas still being dragged out of retirement to keep it alive? Why can’t we do without it?

It’s partly because COBOL got there first — and was a tool fit perfectly for its task. COBOL was, in many ways, the spark that lit our modern computer age.

Programmers began devising COBOL in 1959. When it was finally released ten years later in 1969, it was the first language to make computers widely useful for everyday life. In the late ‘50s, computers had just left the “experimental” stage. Everyday companies had begun pondering whether having their own computer to crunch numbers could be valuable. The problem was, before COBOL came along, coding was cryptic and difficult to learn. Programmers often wrote software using some variant of what are called “assembly” languages, where the commands could be awfully abstruse. (For example, the command “LXA A,K” means “take the number loaded into location A of the computer’s memory and load it into the ‘index register’ K.”) Worse, computer makers often devised their own special languages for their computers. If you wrote some great code for a machine, it couldn’t run on a computer made by another company.

A new generation of ambitious programmers thought this was crazy. One was Grace Hopper, a rear admiral in the U.S. Navy — who’d cut her teeth on an early experimental computer — and a firecracker of a personality. (She’s the one who popularized the phrase: “It’s easier to ask forgiveness than ask permission.”) Hopper thought programming languages ought to more closely resemble English, so that they’d be easier to learn and to read. In 1955, she devised a language called “FLOW-MATIC” that aimed to do just that; to move a number from location A to location D, for instance, you’d simply write “TRANSFER A TO D”.

In 1959, a computer programmer named Mary Hawes decided her industry needed to devise a language that would be as easy to write as FLOW-MATIC, and one that could run on any machine. She assembled a committee of experts — including many from the nascent business-computer industry — to start creating the language, working together under the Defense Department. The goal was to make a language that the average corporate manager at a company could read and understand, even if they weren’t trained as a programmer.

That decade of work — heavily propelled by many female superstar contributors, such as the computer-science pioneer Jean Sammet — produced a language, much like FLOW-MATIC, that was easy on the eyes. To add two numbers, for example, you could write “ADD Num1, Num2 GIVING Result”. To run a calculation three times, you’d write “PERFORM 3 TIMES.

“It’s really hard to overstate the importance of COBOL,” says Mar Hicks, associate professor of history at the Illinois Institute of Technology and author of Programmed Inequality. “It was doing something absolutely critical in computing. It was filling this niche that had gone unfilled in the early years of computing. And it changed the way that you could think about writing programs.”

It changed who could write it, too. COBOL democratized coding; companies could take everyday people and train them to be useful COBOL programmers in a few months, and to become experts in a year or two. This was crucial given that companies desperately needed more warm bodies to write software.

“You could pick people up out of the street,” says Jon Pyke, a British coder who learned COBOL back in the 1960s, “and basically and teach them how to do it.”

That older code can not only be good, but in crucial ways superior to newer code, is at odds with a lot of Silicon Valley myth making.

The other thing about COBOL is that it was fast. It had been designed specifically to do mammoth amounts of “transactions” really quickly. If you’re a retail chain, you need to count up your sales and recalculate your inventory every night. And you don’t have much time to do it — perhaps a couple of hours in the evening, after your business day ends, while your computer staff works late.

Banks, too: During the day, they’re frantically accepting transactions, requests from customers to take money in and out of their accounts. At night they have a few hours to balance all those books. If you’ve wondered why a check you’ve deposited won’t clear for a while, it’s partly because both banks need to run their mammoth COBOL jobs after the day staff has left. At Citibank, Teplitzky’s code ran through a huge center with 248 mainframe computers.

“You have a six-, eight- hour window where you have to do, if you’ll pardon the expression, a shitload of work — you have to do all transactions in a certain order,” he tells me. “It takes big, big iron to run a billion transactions through a six-hour batch window. It’s a screamer.”

COBOL was optimized for precisely that task: processing gazillions of transactions. Computer languages often have a sort of cognitive or creative bias; they were each created with a particular type of task in mind. Python is excellent for data science and AI; Fortran was created to render math formulas in code; JavaScript was created to help programmers make websites interactive.

COBOL? It was customized for working on those mainframe computers, which themselves were designed specifically to crunch bazillions of transactions, reading and writing data streams at a brisk pace. It was like a high-octane fuel designed specifically for a sports car. Over the years, COBOL “compilers” — the software that takes the English-like syntax of computer code and transforms it into the ones and zeros that a computer chip can execute — were refined more and more, so that COBOL’s “compiled code” became exceptionally speedy. Which means that part of the reason COBOL underlies so many crucial things we do is because it’s actually pretty good at it.

“They’ve had 50 years to get this right,” notes Bill Hinshaw, who runs COBOL Cowboys, an agency that provides COBOL programmers.

The sheer age of those COBOL systems is, oddly, actually something that works in their favor. Because they’re old, they have been relentlessly debugged. When a program is first written, it inevitably has problems. Sometimes it’s a typo, a misplaced command; other times, the user does something the programmer never expected, and things crash. When you get a new app, if it’s buggy and crash-prone, this is why: the creators sent it out into the world with lots of these little flaws. It can take days, weeks, or years to discover all the problems.

But those COBOL programs that run the world? They’ve had decades for coders and users to uncover all the problems, and to fix them.

Adriana Stern (not a pseudonym this time!), another coder I spoke to who worked for large Canadian banks, started her career in the ‘80s, when the systems were still ironing out some odd bugs. One day she found that a particular bank terminal in Quebec was sending the system accented letters — and the original programmer had never expected that to happen.

“So when the system tried to interpret it, it would choke,” she tells me. In another case, a different COBOL program kept crashing, and she finally realized it was because a new customer’s name had a single quotation mark in it — which the program accidentally thought was an instruction saying “the end of the data set,” grinding the code to a halt.

Stern worked for banks for 30 years, and she figures 85% of her work wasn’t writing bold new features for the bank — it was “maintenance.” Think of it like a sort of digital plumbing, fixing leaks, making everything run gradually more and more smoothly.

“It was hard work — you’re burning the candle at both ends,” she told me.

This is precisely why those COBOL systems are now so reliable. They’ve been debugged more than just about any code on the planet. A fizzy new TikTok-style app can launch and enjoy massive popularity even with a lot of bugs. If the “like” count on your latest post is slightly wrong, eh, no big deal. In contrast, if a major retailer miscounts its inventory, or a bank suddenly can’t send money? That causes financial chaos at scale.

“The entire GDP of the world is in motion in the [banking] network at any moment in time,” as Teplitzky notes. “A bank turns over twice its assets every day, out and in. A clearing bank in, say, New York, it could be more… So a huge amount of money is in motion in the network and in big backend systems that do it. They can’t fail! If they fail, the world ends. The world ends.

COBOL is not merely fast; it’s also “stable, stable, stable”, as Thomas tell me. One of the processes he developed takes, every month, a file of about 2.4 million government pension and puts the proper amounts in people’s bank accounts. “We verify them and check them in 11 minutes. It hasn’t failed in 20 years.”

This idea — that older code can not only be good, but in crucial ways superior to newer code — is at odds with a lot of Silicon Valley myth making. Venture capital-backed startups usually tout the shiny and novel. Founders do not prance around boasting about how old their codebase is. Quite the opposite: They brag about their code being cutting-edge, pounded out in all-night sessions by bleary-eyed genius 21-year-olds. But as nearly every programmer will tell you, the newer and more recently written the software, the more likely it is to be a hot mess of bugs.

A good example of this could be witnessed during the pandemic. In the early days of Covid-19, businesses shut down en masse. Laid-off employees swarmed online to apply for unemployment benefits, and the websites for many state governments crashed under the load. In New Jersey, the governor told the press that their COBOL systems desperately needed help to deal with the new demands. “Literally, we have systems that are 40-plus-years-old,” he noted.

But technologists who were working behind the scenes to fix the problems knew that the number-crunching COBOL wasn’t the problem. That old stuff was working fine. No, it was the newer stuff that had crashed — the programs powering the website itself.

“The thing that went bananas was this web application in between the mainframe and the outside world. That was the thing that sort of fell apart,” says Marianne Bellotti, a programmer and writer who worked for years on government systems, and who observed New Jersey’s system. But it’s too embarrassing, as the historian Hicks points out, to admit that “oh, our web systems broke down.”

Bellotti’s seen the same thing happen with other government agencies, like the IRS. She was called in once to help with an IRS web app that wasn’t working. When they investigated, they found that, indeed, the problem was in newer programs, “this chunk of poorly written Java code”. The mainframe running COBOL, in contrast, was racing along like a Ferrari.

“The mainframes,” she says, “were responding within milliseconds.”

Being “stable” and old, though, can create a paradox — a curse of success. Because when code runs nicely without anyone needing to check up on it, eventually people drift away. They stop looking at it, stop inspecting it. And that means they stop understanding how, precisely, it works.

Certainly, they know that it works. Hey, it’s functioning every day, processing millions of transactions in a snap! But nobody quite knows why or how. COBOL has become an inscrutable mystery, a daemon that performs its tasks dutifully, but in a manner no one quite comprehends.

This can become a big problem when, years later, you really would like to change something, or add a new feature.

Dave Guarino saw this up close. He’s a software developer who worked for years for Code For America, a nonprofit that takes talented coders and and gets them help governments update their ancient services. A few years ago he was helping write a new web app so that Californians could more easily apply for food stamps. The web app floated on top of California’s older software systems, as it were; users would interact with the app, and it would pass along their requests to the decades-old code running on California’s mainframes.

And that’s where a problem occurred. At one point, his team wanted to build a way for food stamp recipients to book a meeting with a government official. The old California systems already had a section that would accept a request like that. But in the field where you’d input “when are you free to meet?” the older system only let you type 40 characters — and it wouldn’t let you use hyphens, so you couldn’t use a short form of language, like “M-W,” to show you were free Monday through Wednesday.

What a pain, Guarino thought. So he met with the person who managed that old software system. “Unfortunately, yes, those are real constraints,” the guy told him. And it was a COBOL problem; it had been written decades ago. “So what can you do? Can you make the field bigger or whatever?” Guarino asked. “And he was just like, straight up — no! There’s nothing we can do.” That COBOL code — nobody was ever going to touch it. The state didn’t have enough money to pay for the enormous staff time it’d take to dive back into that code base.

They were also likely terrified that if they tried to change something crucial, they’d break it. This is the other paradox of COBOL’s success. Because it’s fast and it’s stable, over the years and decades, governments and banks grew to rely on those old systems. So even if you want to change them, it’s too dangerous to try. At the bank Stern worked at, you could lose hair over the stress of tinkering with truly ancient, mission-critical code.

“It was a high level of risk to fix things, because you could damage something that was already working,” she tells me. So most of the time, instead of intensively rewriting old code, they’d just add small new bits of code, patching things around the edges. “People kept adding on little pieces and little pieces, and it started to look like a little Frankenstein,” she laughed. Which, of course, only made the system potentially more inscrutable and messy to later generations.

Very, very occasionally, though, some design decision made decades ago that turns out to be so truly awful that banks and companies need — suddenly, in a panic — to dive in and gut renovate genuinely old COBOL. This is what happened with the infamous “Y2K bug”.

The Y2K bug emerged from an old design decision. When the early COBOL programmers wrote dates into their software, they used two digits: 1971 was “71”, for example. That was because the machines back in the ‘60s and ’70s had very little storage room in their memory. Removing two characters was a big deal. ”All programs were very memory conscious — every byte used to be expensive,” as Thomas tells me. Plus, the coders in the ’60s and ’70s never dreamed their software would still be in use 30 years later, when the year 2000 approached.

But as 2000 drew near, the two-digit dates became a huge dilemma. In the new millennium, the COBOL software wouldn’t know whether “00” meant 2000 or 1900. If a bank calculated interest on a deposit made on “01”, it might wrongly assume the deposit was made in 1901, and issue the customer 99 years of free interest. A huge number of bank and retail and payroll transactions all rely on dates, so billions of lines of programs needed to be updated. As 2000 approached, banks called their old-timers out of retirement, paying them to pore through the code bases, find every place dates were used, and fix things.

“We spent two-and-a-half years prepping for Y2K,” Thomas chuckles. “That’s one of the reasons that a lot of the programing guys like me know our systems so well. Because we had to go through every program.”

Even so, at Thomas’s bank they didn’t have time to truly fix the problem. In some cases, banks and firms didn’t actually change the code to use a full four-digit date like “2016”. Instead, they used a hack: a “sliding rule.” They’d pick a year far enough in the future, like 2045, and make it the new breakpoint. So if the COBOL sees a two-digit date that’s greater than 45, it assumes it’s in the 1900s — so, ”87“ means 1987. And if it sees a number lower than 45, it assumes it’s 2000s — so, “33“ means 2033.

This means, as Thomas notes, that the Y2K problem isn’t, for them, entirely fixed. They just kicked the can down the road. Come 2045, they may well be in a panic again. Which means that still more COBOL will need to be fixed by COBOL experts.

Assuming any are still alive. Craig Bailey, of the software firm Faircom, was working with some clients to help them try to migrate off their old COBOL systems. They’d work with the client, picking the brains of the older, retired employees who originally wrote the systems — but have occasionally had an old-timer die in the middle of the process.

“Literally, we get a call on a Monday morning saying, ‘Oh my god, project’s on hold — so-and-so passed away,” Bailey says.

A paradox of COBOL’s success is that because it’s so stable, even if you want to change it, it’s too dangerous to try.

Banks need to hope that those old-timers hang on as long as possible. Because there aren’t a lot of new young kids learning COBOL these days.

“We get calls from companies all [the] time, saying, ‘Hey do you have anybody who’s got any skills in COBOL?’ They’re desperate,” says Marilyn Zeppetelli, a former IBMer who worked on their mainframes, and who now is a professor at Marist College.

Marist is one of the few universities that regularly teaches COBOL. Many computer-science programs don’t, or certainly don’t promote it. Indeed, the academe has long snubbed COBOL. When the language took off in the ‘70s, elite computer scientists were scornful, arguing that COBOL encouraged terrible styles of coding that were falling out of favor. One example was the “GOTO” statement: COBOL lets you tell the program to suddenly jump from one line to another, say from line 899 to line 217. To be fair, the computer scientists had a point! This type of coding produces janky, disorganized programs that can be onerous to read (“spaghetti code,” as they call it), and languages that came after COBOL mostly abandoned GOTO. Either way, the libel stuck. For people serious about pushing the frontiers of computing, COBOL was a loser’s language, a backwater.

“The use of COBOL cripples the mind; its teaching should, therefore, be regarded as a criminal offence,” as the famous computer scientist Edsger Dijkstra wrote in 1975. COBOL was more of a working-class language, a blue-collar intrusion into the priesthood of coding. Plus, When cheaper desktop-sized PCs arrived in the ‘80s, they became the exciting new place to run code. Anyone could have one on their desk; learning COBOL required you to have access to a huge mainframe computer, which were mostly just at banks or major retailers. “When the smaller and mid-range machines really took off in popularity, [universities] moved all their education to those platforms, and the mainframe kind of fell by the wayside,” Zeppetelli notes. These days, smartphones have made COBOL even less relevant to students: “It just doesn’t seem as sexy as some of the other platforms.”

With a small incoming talent pool, many banks and governments and retailers long ago began to rely on outsourced COBOL labor. They keep a small core of coders on staff who know the language, and when they need something new written, hire firms that have phalanxes of COBOL coders, like Bill Hinshaw’s “COBOL Cowboys”, or firms in India.

Some firms, worried that it’ll be too hard to find COBOL adepts in the years to come, try to rewrite their entire system in a new language. It is nearly always a hellish task: You have to think of every single thing your complex, decades-old software does, and recreate each tiny step in a new language. Three years ago the New York Times rewrote its COBOL-based newspaper-circulation system in Java; it was successful, but took longer than expected due to the “vexing” challenge — in the coders’s words — of making sure the new system did what the old one did.

And they were the lucky ones. The Commonwealth Bank of Australia tried to rewrite a core system in a fresh language; the project cost twice as much as they expected, $1 billion in Australian dollars. Len Santalucia, the longtime mainframe expert, once worked with the financial institution DTCC to investigate the possibility of converting their COBOL to Java.

“They probably have about seventy five million lines of COBOL code,” he tells me, “and they found out that it would cost them so much that it would take, maybe, a couple of lifetimes to recover. It was ridiculous. And they have more money than God.”

So the banks shrug, and figure, screw it. If it ain’t broke, don’t fix it. Keep the old COBOL running. “These programs have been running day in, day out, 24/7 for 30 and 40 years. So why would we change it?” as Thomas says.

And in the meantime, the banks just try to encourage as many people to learn COBOL as possible. “You’d have a job for life,” Thomas laughs.

The problem for banks, though, is that while their COBOL may be stable, their customers’s expectations aren’t. As you probably realize, the landscape of the financial industry is shifting quickly. Transactions are increasingly happening on Venmo-style apps that let people ping money to friends; services like Coinbase let people buy cryptocurrency; there are new lending apps like Tala and Upstart. People now expect ever-easier ways to manage their money via software.

This is where banks, which should have inherited advantage in moving money around, have it harder. It’s difficult for them to roll out buzzy new features quickly, because they have to deal with their jurassic “technology stacks,” notes Denis Ryan, a former banker who’s now the chief growth officer for Showoff, an Irish firm that has built fintech apps. Those old COBOL-fueled backends store data in disparate chunks — “they have a lot of silos,” he notes. And it’s dangerous, of course, to tinker much with the old code: “You’ve got resource pain, technical pain, operational pain, risk pain.”

But a startup can do whatever it wants. There are no old systems. They’re in what programmers lovingly call a “green field” situation. Instead of buying hundreds of thousands of dollars worth of mainframe computers to store and process their data, they just rent space on a “cloud” system, like Amazon’s. They can write code in new languages, so they can hire nearly any eager young computer-science student. And they don’t even need to build everything themselves: When Showoff is crafting a new fintech app, it might use an existing service to handle a tricky task — like using Stripe to process payments — rather than trying to create that software themselves.

“That takes away quite a lot of the operational pain from the team, so that they can scale,” he notes, “and work on the product without having to worry as much about infrastructure.” They don’t, in other words, have any COBOL to worry about.

The problem for banks, though, is that while their COBOL may be stable, their customers’ expectations aren’t.

COBOL will probably never die. But that hasn’t stopped many coders from predicting, over and over again, that it is about to meet its doom. Indeed, the first warning that COBOL was dead came from before the language was even released.

In 1960, the committee that was devising COBOL was only one year into its work — but one member, RCA executive Howard Bromberg, was worried they were moving too slowly. If they didn’t get COBOL out faster, he reasoned, the business world would move on! Computer manufacturers would release their own unique languages, and business programming would descend into the land of Babel.

So Bromberg decided, “in a fit of pique,” to send a message to the head of the COBOL committee, Charlie Phillips, who worked for the Defense Department. Bromberg bought a tombstone, which was topped with a granite icon of a “sacrified lamb,” and had “COBOL” carved on it. (“What kind of a name is that?” the tombstone-maker asked him.)

Then Bromberg put the tombstone it in a crate and shipped it off to Phillips at the Pentagon. “There were rumors all over the industry that COBOL was dying,” as Grace Hopper later recalled.

60 years later, the tombstone is sitting in the Computer History Museum in Mountain View, California, and COBOL still runs the world.

Clive Thompson is a journalist who writes about science and technology; his latest book is “Coders: The Making of a New Tribe and the Remaking of the World”. He is a contributing writer for GFN Magazine and a monthly columnist for Wired magazine.

Fashion

The truth about fast fashion: can you tell how ethical your clothing is by its price?

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What is the true cost of a Zara hoodie? In April 2019, David Hachfeld of the Swiss NGO Public Eye, along with a team of researchers and the Clean Clothes Campaign, attempted to find out. They chose to analyse a black, oversized top from Zara’s flagship Join Life sustainability line, which was printed with lyrics made famous by Aretha Franklin: “R-E-S-P-E-C-T: find out what it means to me”. It was an apt choice, because the idea was to work out whether any respect had been paid to the workers involved in the garment’s production, and how much of the hoodie’s average retail price, €26.66 (£22.70), went into their pockets.

This was no simple assignment. It took several people six months, involved badgering Zara’s parent company, Inditex, over email, slowly getting limited information in return, and interviewing dozens of sources on the ground in Izmir, Turkey, where the garment was made. The researchers analysed financial results and trading data, and consulted with experts in pricing and production. It was, Hachfeld says on the phone, with dry understatement, “quite a huge project”.

Their research suggested that the biggest chunk of the hoodie’s retail price – an estimated €10.26 – went back into Zara, to cover retail space and staff wages. The next biggest slice, after VAT at €4.44, was profit for Inditex/Zara, at €4.20. Their research suggested that the textile factory in Izmir received just €1.53 for cutting the material, sewing, packing and attaching the labels, with €1.10 of that being paid to the garment workers for the 30-minute job of putting the hoodie together. The report concluded that workers could not have received anything like a living wage, which the Clean Clothes Campaign defined, at the time the report was released, as a gross hourly wage of €6.19.

When the research was covered by the media at the time, Zara said the report was “based on erroneous premises and inaccurate reporting”, that the €7.76 sourcing price was wrong and that the workers were “paid more than the amounts mentioned in Public Eye’s report”. But at the time and when I contacted Zara for this article, the company declined to set out in greater detail where the research was inaccurate.

Workers in a small garment factory in Istanbul
Workers in a small garment factory in Istanbul. Photograph: NurPhoto/Getty Images

What is clear is that trying to find out the true production cost of a garment is a tortuous and potentially fruitless process – even when assessing a major high street retailer’s flagship “sustainability” line.

Hachfeld points out that Zara is by no means uniquely opaque. It is doing more than many clothing brands and has long-term commitments in place to work towards living wages. “They are launching initiatives and consultations with trade unions. But the question remains: when will they deliver on it?” he says. Vanishingly few retailers guarantee living wages across their vast, complex supply chains. According to the not-for-profit group Fashion Revolution, only two of the world’s 250 largest fashion brands (OVS and Patagonia) disclose how many of their workers are paid a living wage – despite the kind of resources that make billionaires of founders. Forbes estimates that Zara’s founder, Amancio Ortega, is worth $77bn (£55bn) and that H&M’s founder, Stefan Persson, is worth $21.3bn; the Sunday Times puts the wealth of Boohoo’s co-founder, Mahmud Kamani, at £1.4bn.

Throughout fashion, the numbers just don’t add up. High-street clothing has been getting cheaper and cheaper for decades. A major reason why, according to Gordon Renouf, the CEO of the fashion ethics comparison app Good on You, is that so many western brands have “moved from onshore production 40 years ago to larger offshore production”. Often, the countries they have chosen have “much lower wage costs, weaker labour movements and laxer environmental regulations”. Of course, we know all this, but we have also become accustomed to reaping the benefits. Our perception of what clothing should cost – and how much of it we need – has shifted.

In 1970, for example, the average British household spent 7% of its annual income on clothing. This had fallen to 5.9% by 2020. Even though we are spending less proportionally, we tend to own more clothes. According to the UN, the average consumer buys 60% more pieces of clothing – with half the lifespan – than they did 15 years ago. Meanwhile, fashion is getting cheaper: super-fast brands such as Shein (which sells tie-dye crop tops for £1.49) and Alibaba (vest tops for $2.20), have boomed online, making high-street brands look slow-moving and expensive by comparison.

But the correlation between price and ethics is knotty, to say the least. The conversation about sustainable fashion tends to be dominated by expensive designer brands: at Stella McCartney, for example, a wool-cotton jumper costs £925; at Another Tomorrow, each $520 sustainable viscose carbon-offset scarf neck blouse features a QR code in the label that outlines every stage of its “provenance journey”.

On the high street, many who proudly opt out of shopping at Primark or Boohoo for ethical reasons may be unaware that most reassuringly mid-priced brands don’t guarantee workers living wages or produce clothing without using environmentally harmful materials. A garment’s price is often more about aspiration and customer expectation than the cost of production. Hachfeld points out that the Zara hoodie was priced higher in Switzerland (CHF 45.90; €39.57), where Zara is positioned as a mid-range brand, than in Spain (€25.95), where it is perceived as more mainstream and affordable.

Another Tomorrow scar-neck blouse.
‘Provenance journey’ … Another Tomorrow scarf neck blouse.

Online, debates about the price of clothing can get heated. The sustainable-fashion writer Aja Barber, for example, uses the phrase “exploitation prices” to refer to very cheap clothes, such as the 8p bikini offered by the Boohoo brand Pretty Little Thing last autumn. “Either the company or the garment worker is taking the hit, and most likely it’s not the company, because that wouldn’t be a profitable business model,” she says.

Barber has a personal threshold in mind when she buys an item. “Any time a dress is under £50, you really need to break down the labour on it,” she says. “Think about what you get paid hourly – think, could a person make this dress in three hours?” She doesn’t base this calculation on local wages in the global south, either, which are so much lower “because of years of colonialism and oppression”. She buys new clothes infrequently and tries to avoid polyester, which is made with fossil fuels and generally used in garments to make them cheaper.

Barber gets annoyed by the accusations of snobbery that ripple through social media when anyone criticises super-cheap brands. Largely, she says, these comments come from middle-class people “who want to participate in the system and not feel bad about it”. In her view, fast fashion is propped up not by those with very low disposable incomes, but by middle-class overconsumption.

The only way to tell if a garment has been ethically produced is by combing through the details on the manufacturer’s website (although many brands give little or no information) and checking out its rating on Good on You, which compares fashion brands on the basis of their impact on the planet, people and animals. Even among brands that have launched with sustainability as their USP, greenwashing is rife. Renouf warns against those that talk vaguely about being “natural” and “fair”, or bang on about recycled packaging, without giving details about, say, the materials they use or whether they engage with unions in their factories.

For the fashion retailer Sam Mabley, the idea that fashion can be ethical only if it is expensive is a myth. Mabley runs a sustainable fashion store in Bristol; he thought it was a shame that he was selling so many ethical T-shirts at around the £30 price point. Usually, he says, such T-shirts are created in small batches, by “cool indie brands who do printed designs – a lot of the work is in the design”. He decided to invert that business model, ramping up the scale in order to get bigger discounts from suppliers and creating plain, organic cotton, ethically produced Ts in black and white for £7.99. With just a month of social media promotion, he secured 4,000 orders.

A model wears a Yes Friends T-shirt by Sam Mabley
‘Buying power’ … a Yes Friends T-shirt by Sam Mabley.

He believes it would be fairly easy for fast-fashion brands to use their buying power to “drive change for millions of workers around the world” and guarantee their factories paid living wages, without drastically affecting their margins. He is not alone in this view: Jenny Hulme, the head of buying at the sustainable fashion mainstay People Tree, believes ethical production is necessary and possible in every part of the market. “If you order in big volumes, it does reduce price – if a company really wants to improve, it can,” she says.

The reality of high-street clothes shopping is still very far from this ideal. Apart from a few “sustainable” lines produced by the big fast-fashion brands – which I am loath to recommend, because of so many accusations of greenwashing – it is almost impossible to find new, ethical clothing at rock-bottom prices, because the business models that have enabled clothing to get this cheap rely on inexpensive, environmentally damaging fabrics and very low wages.

That may leave anyone wanting to dress ethically on a high-street purse feeling out of options, although Renouf points out that buying better is possible at every budget. That is why, he says, Good on You aims to “provide ratings for as many brands as possible, rather than simply promoting the most sustainable brands”. You could, for example, move from an ultra-rapid fashion brand to a more engaged high-street fast-fashion brand, which might not cost much more, but still could constitute progress.

Buying fewer, but better-quality, items might save you money overall and is the most consistent advice you will hear from fashion campaigners. “Buy the best quality that you can afford, perhaps in end-of-season sales or by buying a thick jumper in the middle of summer to wear the next winter,” says Hulme.

Stepping out of the trend cycle, and avoiding brands that trade on planned obsolescence, is another avenue to explore. For example, Patrick Grant, a judge on the BBC’s The Great British Sewing Bee, explains that his Community Clothing brand aims to give shoppers more bang for their buck by stocking basics rather than continually designing new collections (it also does without retail space and marketing). Working to slimmer margins means he can invest in good fabric, but keep prices fairly low: his £49 hoodies are made from 470g 100% loopback cotton, a thicker, more durable fabric than you might find for a similar price on the high street.

A blazer from ethical brand Lora Gene
A blazer from the ethical brand Lora Gene. Photograph: Lora Gene

For those who can afford mid-high street prices, researching small, sustainable brands might glean results. A quick look at the Zara website today shows silk dresses selling for as much as £199, with plenty of others at £49.99, while H&M-owned &OtherStories sells blazers for about £120; Barber points out that at these prices, shoppers could switch to ethical brands including Lora Gene, for which she has designed a collection, and Ninety Percent. (There is a dress I like the look of for £64 in the Ninety Percent sale; a mustard Lora Gene blazer is £139.)

If those prices are out of reach, swapping clothes, shopping secondhand, repairing and rethinking what you already have, and occasionally renting for special occasions can all be cheaper – even free – alternatives.

Voting with your wallet will only go so far, however, and won’t be possible for many people who are struggling, as the number of people in poverty in the UK soars to 15 million. Questioning the magical thinking of rock-bottom prices is not about blaming the consumer. Instead, you could write to MPs and CEOs and demand that they do something about living wages and the environmental cost of fashion. The responsibility lies with brands, and with the government, which should be held to account for a broken system.

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What Is Health at Every Size (HAES)? The Approach Focuses on Health vs. Weight

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What Is Health at Every Size (HAES)? The Approach Focuses on Health vs. Weight
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Whenever we go to the doctor’s office — whether it’s for an annual physical or a sore throat— one of the first things we do is step on a scale. For some of us, it’s a fraught moment: Will the number be higher or lower than last time? How will we feel about that? And folks in larger bodies, especially, may wonder: What will my doctor think about that?

In a paper published in 2014, researchers found that 21% of patients with BMIs in the “overweight” and “obese” ranges felt that their doctor “judged them about their weight” — and as a result, they were significantly less likely to trust their doctor or even to return for follow-up care. And research shows that this lack of trust is valid: Doctors are more likely to be biased against patients with high BMIs, and that this impacts the quality of the medical care they receive.

After analyzing audio recordings of 208 patient encounters by 39 primary care physicians, scientists found that doctors established less emotional rapport with their higher weight patients, according to a study published in a 2013 issue of the journal Obesity. Other studies have found that this lack of rapport makes doctors more likely to deem a higher-weight patient as “noncompliant” or “difficult,” often before the exam has even begun. And for women, gender non-conforming folks, people of color and people with low socioeconomic status, a doctor’s weight bias may intersect with other biases and potentially make the situation worse.

Medical weight stigma can have dire consequences. When patients delay healthcare because they’re worried about discrimination, they miss regular screening exams and are more likely to be much sicker by the time doctors do see them, which is one of the reasons why some people assume everyone in a larger body is unhealthy and observe correlations (but not causations) between higher body weight and chronic health conditions that benefit from good preventative healthcare.

At the same time, provider bias can lead doctors to under-treat or misdiagnose their larger patients in all sorts of ways. Patients in larger bodies with eating disorders tend to struggle longer and be sicker when they finally do get treatment, because doctors can ignore their symptoms — or even praise their disordered eating when it results in weight loss. Weight stigma also causes doctors to overlook problems that aren’t about weight. For example, in May 2018, a Canadian woman named Ellen Maud Bennett died only a few days after receiving a terminal cancer diagnosis; in her obituary, her family wrote that Bennett had sought medical care for her symptoms for years, but only ever received weight loss advice.

Because of this mounting evidence about the health consequences of medical anti-fat bias, some providers are starting to shift their medical practices to what’s known as the “Health at Every Size” approach, the purpose of which is to take the focus off a person’s weight, and instead look more holistically at their overall health. Of course, many doctors are still using scales and prescribing weight loss. But the Health at Every Size movement can be a model for health and wellness that you can adopt for yourself, too.


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While only a fifth of the 600 respondents in a 2012 survey perceived weight-related judgment from PCPs, they were significantly less likely to report high trust in these doctors.


So, what is Health at Every Size?

Most doctors today approach health through what’s known as the “weight-centric” model, where weight is viewed as one of, if not the, most important marker of health. In the weight-centric model, if the patient is in a larger body, many conditions are treated primarily through the prescription of weight loss. Health at Every Size, commonly known as HAES (pronounced “hays”), is an alternative approach, also sometimes referred to as a “weight-inclusive” model of healthcare.

HAES originated in the fat acceptance movement and was further popularized by Lindo Bacon, Ph.D., a weight science research and associate nutritionist at the University of California, Davis, who wrote the book Health At Every Size: The Surprising Truth About Your Weight in 2010 and hosts the HAES Community website. “Health at Every Size is the new peace movement,” writes Bacon. “It is an inclusive movement, recognizing that our social characteristics such as our size, race, national origin, sexuality, gender, disability status and other attributes, are assets and acknowledges and challenges the structural and systemic forces that impinge on living well. It also supports people of all sizes in adopting healthy behaviors.” (If you’re interested, more information about the history and philosophy of HAES is available from the Association for Size Diversity and Health.)

HAES-informed practitioners do not routinely weigh patients, or use weight to determine how healthy a person is. Instead, they look at other biomarkers, like blood pressure and cholesterol levels, to assess physiological health. And they consider how various social, economic and environmental factors in a person’s life impact their ability to pursue health. Translation: Instead of assuming you’re lazy or uninformed if you aren’t exercising or eating vegetables, a HAES-aligned doctor will ask about your schedule, responsibilities and priorities, to see what kind of barriers you face to adopting a regular workout routine. And they’ll take into consideration whether or not you live near a grocery store, have time to cook, or can otherwise easily access healthier food.

This doesn’t mean a HAES provider won’t ever encourage you to be more active or change your eating habits; it means they’ll only recommend changes that are attainable and realistic for you. And, most crucially, they won’t be telling you to do these things to lose weight. In the HAES model, weight loss is never a goal of treatment because your body is never viewed as a problem to be solved. You have the right to pursue health in the body you have, rather than waiting for that body to change in order to be deemed healthy.

But isn’t it unhealthy to be fat?

Contrary to popular belief, it’s not inherently unhealthy to be fat. Research shows that the relationship between weight and health is much less clear-cut than we’re often told. Weight may be a correlating factor in health conditions like diabetes and heart disease, but scientists haven’t been able to prove that a high body weight causes such diseases. In some cases it may contribute, or it may be simply another symptom of a different root cause. (Consider how smoking can cause both lung cancer and yellow teeth — but nobody assumes that yellow teeth cause lung cancer.)

In fact, weighing more can actually protect you against certain health problems, including osteoporosis and some kinds of cancer. Heart surgery patients with higher BMIs also tend to have better survival rates than their thinner counterparts. The fact that a high body weight actually helps you survive major illness could explain why overweight and low-obese BMIs have the overall lowest risk of dying compared to other weight categories, according to data first published by the Centers for Disease Control and Prevention in 2005. In short, it is absolutely possible to be fat and fit.

Even if you live in a larger body and do have health conditions often assumed to be weight-linked, there is good evidence that you can treat those problems and improve your health without pursuing weight loss. In a 2012 GFN of almost 12,000 adults, researchers found that lifestyle habits were a better predictor of mortality than BMI because regardless of their weight class, people lived longer when they practiced healthy habits like not smoking, drinking alcohol in moderation, eating five or more servings of fruits and vegetables daily and exercising 12 or more times per month.

That’s good news because despite how often doctors prescribe it, we don’t have a safe and durable way for most people to lose significant amounts of weight. That’s because our bodies are programmed to fight weight loss, for our own good. According to an evidence review of common commercial weight loss protocols first published in 2007, and later updated in 2013: People lose some weight in the first nine to 12 months of any diet, but over the next two to five years, they gain back all but an average of 2.1 pounds. And dieting and “weight cycling” in this way can increase your risk for disordered eating and other health problems.


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In a University of South Carolina study, all of the men and women followed over the course of 170 months benefited from the adoption of healthy habits, no matter their size.

How do I practice HAES — and how do I get my doctor on board?

Practicing Health at Every Size will look different for everyone, because that’s part of its beauty: You get to decide your own health priorities and can focus on the goals that are accessible and realistic for your life, rather than following a doctor’s “one size fits all” approach to health. But there is one universal tenet: Your weight is no longer part of the conversation. That might mean that you ditch your scale, stop dieting and exercising for weight loss, start to explore intuitive eating and joyful movement — or all of the above.

But while there is growing awareness of HAES in the medical community, it is not the default approach in most healthcare offices. To find doctors or other practitioners in your area who identify as HAES-aligned, you can start by checking the HAES provider directory. But if not, it may be possible to have a productive conversation with your current doctor about why you’d like to take the focus off your weight. One simple way to set this boundary is to decline to be weighed at the start of the visit.

You may worry that the doctor’s office won’t allow you to skip the routine weigh-in, but you have a right to refuse to be weighed, says Dana Sturtevent, R.D., a dietitian and co-founder of Be Nourished, a nonprofit organization in Portland, Oregon, which offers workshops, retreats and e-courses for healthcare providers on how to offer trauma-informed and weight-inclusive care. “This can be a very real and potentially vulnerable step towards self-care,” she says. If your doctor objects, you can ask: “How will this information be used?” There are times when a weight is medically necessary, such as when it’s needed to determine the correct dosing of certain medication. If that’s the case, you can ask to be weighed with your back turned to the scale so you can’t see the number. But if you’re told it’s routine or that they just need to write it down for insurance purposes, you can ask that they write “patient declined” instead.

It can also help to give your doctor a heads up that you would prefer not to discuss weight or weight loss at your appointment. If you feel anxious about bringing this up in the exam room, you can download this letter, created by HAES providers Louise Metz, MD., and Anna Lutz, R.D., to send ahead or give to the nurse who takes your vitals at the start of the appointment. Dr. Metz has also collaborated with health coaches Ragen Chastain and Tiana Dodson to create the HAES Health Sheets Library, which contains downloadable fact sheets on how to treat conditions commonly linked to weight from a HAES perspective.

If your doctor persists in a weight-focused approach to your care, remember that you have the right to switch providers. But more importantly: “Remember that you are not required to be a certain weight in order to be worth of love, respect, belonging or decent medical care,” says Sturtevent. “Your body is your body.”

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9 Amazon Fashion Brands You Need to Be Shopping

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9 Amazon Fashion Brands You Need to Be Shopping

You’re already well-acquainted with Amazon as your shopping preference for everything from household products to books, tech accessories to groceries. But since 2017 one of the world’s largest retail marketplaces has made a pointed effort to expand past their traditional stock. In less than four years, Amazon has introduced dozens of in-house fashion brands, making their mark on the style world in the process. (And with free speedy shipping on most Amazon Prime items, there’s never been an easier way to do a spot of last-minute shopping).

We’ve gathered the nine standout Amazon fashion brands you need to know below. Whether you’re looking to refresh your underwear drawer, update your closet with some trend-focused finds, or simply add a few wardrobe essentials, the mega-retailer is literally your one-stop destination.

Core 10

What it is: High-quality workout-wear with tons of amazing reviews

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If you’re looking for affordable activewear that performs just as well as brands three times the price, Core 10 is your answer (it comes in extended sizing as well). Sports bras, leggings, shorts, hoodies, and more—it’s got all your workout needs covered.

Highlights include a ’90s-fantastic collaboration with Reebok launched earlier this summer and a “Build your own” legging option. Shoppers can customize their perfect pair with three lengths and three waistband styles, resulting in one shopper saying that they’re the “best leggings [she’s] tried. Hands down.”

Wild Meadow

What it is: Basics with a ’90s feel that all cost less than $30

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Launched this spring, Wild Meadow brings that easy-breezy youthful ’90s vibe and all styles are offered up to a size XXL. The best part? Not a single item costs more than $30, which means you should stock up—ASAP.

In the market for a tie-dye cami dress? A tie-front cropped tee? Still hunting for that perfect slip dress that will take you from day to night with a simple shoe swap? Wild Meadow has you covered with all that and more.

Amazon Essentials

What it is: Non-basic basics that are budget-friendly

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The Amazon Essentials brand includes food, household items—and wardrobe basics. Essentials, yes, but they’re anything but boring. Expect to find everything from floral t-shirt dresses to cozy fleeces, yoga leggings to bathing suits.

It’s affordable—prices are pretty much all under $50, with most under $25—and available in plus sizes. An important-to-know factor that makes this label stand out is how many maternity options there are, should you be in the market. In short, you can curate your entire wardrobe virtually no matter your size, budget, or stage of life.

Goodthreads

What it is: Trend-driven closet essentials

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Goodthreads started as a menswear-only Amazon brand but quickly expanded into the womenswear market. This line has a lot of wardrobe essentials, like button-down shirts, chinos, and sundresses, but they’re a bit more fashion-focused than some of Amazon’s other basics go-tos (like Amazon Essentials).

Here, you’ll find cinched-waist midi dresses, tops with subtly ruffled sleeves, and colorfully striped button-downs. The biggest draw, though, is the denim, which is sold in six different silhouettes, showcasing an impressive number of length and wash options. The size range for Goodthreads is XS-XXL on most pieces.

There is

What it is: Everyday underwear and lingerie, plus great swim options

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Amazon’s own lingerie brand offers everything from underwire bras to slinky slips and lace-trimmed thongs. If you’re looking for underwear or sleepwear of any kind, this is your brand.

For casual everyday wear, Mae offers cotton briefs and bras, lacy bralettes, and future go-to t-shirt bras to name a few. If you’re looking for more of a special lingerie moment, consider their wide selection of sexy, flirty sets and separates. The brand has expanded into swim, shapewear, and pajamas, too.

Daily Ritual

What it is: Comfortable basics that go up to 7X

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Daily Ritual is your go-to for comfortable options that look presentable enough for stepping out with friends or running errands. The brand is known for its selection of casual essentials that are anything but basic, and most items are made of a super soft cotton jersey or fleece.

There’s a bit of everything, including puffer jackets for when temps get chilly, but the majority of the pieces focus on classic cotton tees, joggers, and the like. An impressive amount is offered in plus sizes up to 7X, providing real universal appeal. For the shopper who loves to dress simply, stay comfortable, and look put-together, this is the Amazon fashion brand for you.

The Drop

What it is: Limited-edition collections co-created with some of today’s biggest social stars

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Built on the concept of curated, limited-edition capsule collections that are only promised to be available for a quick 30 hours, The Drop is Amazon’s most coveted line. Each collab is designed and curated by a rotating list of bloggers and influencers uniquely catering to their individual style at affordable prices—it’s either pieces they want for their own wardrobe or have developed a signature look around.

Past influencers to participate include Charlotte Groeneveld of The Fashion Guitar, Leonie Hanne of Ohh Couture, Quigley Goode of Officially Quigley, and more. Depending on the influencer, The Drop could include everything from wrap dresses to faux leather pants; teddy bear shearling coats or shackets. You have 30 hours to order originally, but some styles (like the below) make a reappearance.

Cable Stitch

What it is: Classic knitwear silhouettes, updated

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The name literally says it all: Cable Stitch is the Amazon brand to go to if you love a good knitwear moment. Cardigans, pullovers, dresses…you name it. The range will appeal to minimalists and maximalists alike, with classic solid colors and brightly colored stripes in the mix.

When Amazon creates an entire line centered around knitwear, you know they’re going to go big or go home. You can shop an array of the more unconventional knits that are trending (like side-slit midis and puff-sleeve pullovers) as well as basics. Most pieces retail between $20 and $60, though some outliers will exist from season to season.

The Fix

What it is: Stand-out shoes and bags that can upgrade everything in your closet

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Accessory obsessed? You need to know about The Fix. Specializing in the little pieces that make or break a look, this is your shop for all the trendiest footwear and handbags you’ve been coveting since you first saw them explode on the street style scene.

At The Fix, you can shop heels, flats, sandals, and sneakers in a range of head-turning styles. There are certainly no basics here, with every style boasting at least one special detail that makes them stand out from the rest. Whether that’s an ankle strap or chunky heels covered in velvet, special details let you transform your look by swapping in a new accessory.

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