Prática de Shadowing: The Gig Economy is Full - Aprenda a falar inglês com vídeo
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The gig economy has never exactly been flawless, but some recent trends are throwing off a delicate balancing act between workers and customers that might,
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accidentally, be exposing some much deeper underlying problems.
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On the surface, our unemployment rate looks really good right now.
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Sure, it has trended up slightly in the wake of a long string of mass layoffs, but by zooming out, we can see it is still hovering around all-time lows.
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Sounds great, right?
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Well, it's no secret anymore that jobs data has had some reliability problems in recent years.
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And one of the biggest blind spots is people moving into the gig economy instead of becoming formally unemployed.
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For a lot of people who have lost their jobs, it's quicker and often easier to register as an Uber driver than it is to register as unemployed.
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And you only really need to look at the numbers to see this is happening.
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According to the Bureau of Labor Statistics, over the last five years, the total number of people classified as unemployed has fallen by about 3 million,
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which sounds good, but over that same time frame, the number of active Uber drivers has increased by roughly 3 million people, according to company financials.
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Now, to play devil's advocate, even if these jobs aren't the best, having readily available and highly flexible work with low barriers to entry could actually be a good thing
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when the alternative is people going straight onto unemployment, and we will get to that.
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But the problem right now for the workers entering the gig economy is
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that there is a rapidly growing roster of drivers looking to chauffeur around a $50 burrito
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and a shrinking pool of customers that are still willing to pay for it.
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A lot of the data surrounding gig work and even just regular employment is disjointed, vague, and often downright counterintuitive.
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Even something as simple as what it means to be employed
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is probably going to be different for you than it is to the statisticians putting all of these numbers together.
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On top of that, when you think of gig work, you are probably thinking about people delivering food or driving people around in their fake taxis.
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But depending on which organization is collecting the data, it can also extend to renting out Airbnb properties or even people like little old me here running a YouTube channel.
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When a metric becomes a goal, it's no longer a useful measure, because the groups involved will try to influence it in a way that benefits them.
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The gig platform companies want to report
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that they have lots of people working for them to show how important they are to their stakeholders.
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The media wants big changes in job numbers one way or another to make for good headlines, and politicians want to ignore the industry altogether so that they can keep on pretending the job market is great.
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This just adds additional murkiness to an already very messy collection of numbers, which can make it really hard to see what is happening to a majority of the workers in these jobs,
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which is, let's be honest, mostly people who couldn't find a better job elsewhere.
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So to unravel this all, there are really three components that need to be understood.
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The fundamental platform economics that have facilitated the growth of the gig economy, what these services have covered up in the job market, and what happens when they can't cover it up anymore.
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Now that first point is really just about a balancing act.
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A platform like Uber Eats or DoorDash coordinates transactions between a service provider, the delivery driver, a goods provider, the restaurant, and a consumer,
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which is you poking at pictures of cheeseburgers on your phone at 11pm.
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If Uber Eats wants to make more money, it either needs to charge you more, pay their delivery drivers less, take a larger cut from the restaurant making the food, or just do more transactions in total.
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In the early days of these services, they were almost exclusively focused on that last point, expanding their user base.
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This meant that on any given transaction, they would actually pay more to the restaurant
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and delivery driver than they were taking off the customer just
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to attract as many of these three onto their platform as possible.
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Back around six years ago, when these businesses were still blitzscaling, customers would get subsidized food that was often cheaper than ordering in person, restaurants would get generous revenue splits,
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and drivers were paid well above minimum wage even after accounting for their hidden costs.
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But these good times were all financed by burning venture capital dollars.
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to play devil's advocate again, this is actually not that unusual when building out a marketplace.
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Customers won't spend money on the app if there are no restaurants to buy from, restaurants won't register on a platform that has no customers, and drivers won't make any money if there is no food to be delivered.
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So to get things started, the market maker usually does need to sweeten the deal in every direction.
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Nowadays, their mission has changed from growing the number of transactions to growing how much they make from those transactions,
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but how much they can push on each participant really depends on how hard they are to replace.
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All of these groups are still growing, but while total revenue from customers increased by around 35% in the last two years,
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the total number of active gig workers on the platform increased by around 80% over the same time period.
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This would mean that even if there were no changes in how this revenue was divided up, there is simply less money per driver even without considering inflation.
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But the real point here is that drivers have become the easiest group in this triangle to replace, So it's easier for Uber to cut costs from them without destabilizing their market.
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Now I know that this is a lot of numbers to confirm what you probably already knew.
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Pay and conditions for the most common type of gig work is getting worse.
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But by understanding these numbers you can see how this can very quickly become a dangerous feedback loop.
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Especially if these customers can no longer justify spending twice as much on their food to have it hand delivered across town.
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Now maybe this could be written off as pure speculation from
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people who have spent too long looking at company financials except for the fact
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that in a lot of markets it's already happening
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and the results have not been pretty
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so it's time to learn how many works to find out what happens
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when there is no room left in the gig economy this
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so as the gig economy platforms are playing this little game
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the perfect outcome for the company in the middle here trying
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to maximize their own bottom line would be a large group
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of people with a lot of disposable cash who are not very price sensitive
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and an even bigger group of people over here without much negotiating power who just need whatever money they can get.
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And oh, would you look at that?
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It's a statistic you are probably all getting sick of hearing me repeat.
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The top 10% of households are now doing a record 50% of the spending, while the bottom 60% of households are doing a record low of just 20%.
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This growing divide is not great.
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But for a company like Uber, it's been a golden opportunity to squeeze more money for a worse service out of customers
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while simultaneously squeezing more service for less money out of its contractors.
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Also, let's be honest with ourselves, a lot of that gap is being bridged by pushing for tips harder as well.
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For the first three years of company they didn't even have a direct tipping option
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because the company itself called the practice tacky.
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Today it's the expectation, and it's pushed very hard.
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First-hand reports from drivers and restaurants have backed up what the big picture numbers are suggesting.
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A lot of locations are doing record volumes of deliveries, but individual drivers are finding it increasingly hard to find consistent deliveries
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or rides which means they can spend hours a day just waiting for an opportunity to work.
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When they do get jobs, it's clear that the platform is taking a larger share of the pie,
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like this example of a $64 ride here in LA paying out just $12.43 to the driver.
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Of course, this is just one example, but it is a pretty damning one.
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Another factor that has allowed these companies to squeeze harder from every direction is interest rates.
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For a while, competition was actually quite fierce in the delivery gig workspace
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as dozens of regional startups tried to take market share away from the primary players.
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Today, most of them have either become the incumbents, been acquired by the incumbents, or they have gone out of business.
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The blitzscaling business model of burning billions of investor dollars until
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cementing market share only really worked in a low interest rate
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environment where it didn't cost much to just keep on piling in more cash.
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If a competitor wanted to enter the market today to offer a better service or pay gig workers more competitively, they would not only need to cover their operating costs while taking market share,
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they would also need to cover interest payments
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that would be three times higher than they were back when a lot of today's incumbents were doing the same thing.
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Now where this could get really bad is what could happen if job losses continue.
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If even more people are forced into the gig economy, that would mean that even more people would end up competing for the same gigs,
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and that's without even considering that if people lose their regular jobs and end up delivering for Uber Eats, they won't have as much money to spend on Uber Eats.
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Now this process alone is actually not that unusual.
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It's basically how every demand-side economic downturn ever happens.
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Businesses lay off people, so households spend less money, so revenue falls, so businesses lay people off.
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If anything, it could almost be argued
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that gig work like this offers an additional layer of protection to people who lose their jobs, which is kind of what is happening right now.
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The problem is that if this becomes widespread enough, it can cover up the problem for a lot longer than it would otherwise take to realize something might be going wrong.
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And if things do go very wrong, the gig economy offers far less friction for this loop to spin.
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Typically, businesses need to fill out a lot of paperwork to lay people off.
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It makes them look bad to investors, and they will also need to rework internal processes to operate with fewer staff.
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Yeah, sure, they still do it.
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But it's not an instantaneous or painless process.
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This means we at least have time to see something going wrong before it's a full-blown crisis.
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For gig workers, all of this can happen instantaneously and automatically through the algorithms.
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If the apps are getting fewer orders, drivers get fewer jobs and less surge pricing.
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The automated nature of their employment throws a bottle of baby oil into the mix anytime the economy loses its footing.
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Uber, Lyft, DoorDash, and companies like it are now collectively some of the largest employers in America.
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And if Uber drivers were counted as actual employees, it would individually be the largest employer in the world,
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easily beating out the largest defense departments and more traditional employers like Walmart.
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This is also just counting the people working in the largest and most obvious sectors of the gig economy.
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We have covered the story before, but starting around 5 years ago, there was a massive increase in the number of new businesses being registered in America.
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Now spoiler alert, this was not because people suddenly became more entrepreneurial.
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It was mostly because people had lost their regular employment and were registering a company to do gig work.
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Fiverr, Etsy, Amazon Flex, and the naughtier platforms also saw a massive uptick in people selling their services.
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Even the number of videos uploaded to YouTube expanded significantly at
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this time according to research published in the Journal of Quantitative Description, as people were looking for new ways to make money.
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And yes, this was just counting long-form content, as short-form would have skewed the results.
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The challenges faced by individuals working at the mercy of the gig economy are now well understood and well documented.
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But what we may soon figure out is that even for the people who never individually have to experience those conditions, they could still become a big problem for all of us.
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In fact, it's kind of already happened.
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China's gig economy might represent a glimpse into our own future, and that future is bleak.
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As a result of various factors like headwinds from the real estate
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and financial industries as well as a glutton recent graduates the youth unemployment rate in china got
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so bad that the government simply stopped posting
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that data uh yeah two years ago
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when they did this we were actually in a position to judge them for it
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but anyway these young workers with few other options have flooded
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into the gig economy completely oversupplying the system with drivers today
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according to estimates from the economist compiled from available company financials there are 200 million gig workers in China, representing as much as 40% of their total urban workforce.
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This massive number is even scarier when you realize that just 15 years ago, these platforms didn't really exist at all.
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This has become a real problem in the last three years in particular, as already frugal Chinese households are cutting back on service spending due to faltering economic conditions.
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The end result is
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that gig workers have reported spending 10 hours a day waiting
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to earn as much money as what would have taken them just three hours in the past.
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groups of brightly colored riders waiting outside shopping malls have become
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a very visible sign of bigger issues impacting average people in an economy that, by the headline numbers, still looks like it's doing very well.
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Even workers who are active for 10 hours a day, 7 days a week, are still not earning enough to afford a dignified lifestyle in
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the country's major urban centers where most of this work is available.
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In some cases, competition for work has become so fierce
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that groups from opposing apps have gotten into altercations with one another on the street, and that is clearly not a sign of things going well.
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Now, we are still a little ways behind a similar situation in most western countries, but there are not major structural differences that mean we couldn't.
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The point is that this should be a scary story with a lot of things that sound very familiar, which is not great.
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But it gets worse.
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So far, all of this has been ignoring the big push to automate gig work as quickly as possible.
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Drivers replaced by robotaxis, delivery drivers replaced with little fridges on wheels, and Etsy creatives replaced with an avalanche of AI-generated slop.
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Unfortunately, as this technology progresses, there is really no way it ends well for the most
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vulnerable workers depending on these kinds of gigs to pay their bills.
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If the AI bubble collapses, then it's going to take a lot of the economy down with it, and the first thing that people are going to sacrifice are expensive Uber Eats orders.
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If the AI bubble doesn't collapse, it will most likely be
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because the technology gets to a point where it is completely able to replace workers in labor-heavy industries, like food delivery.
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No matter which way this all goes, it's probably not going to end well for people who are already not doing well.
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Now, I will actually be making an entire video next week about this dismal endgame, so make sure to subscribe for that.
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But shameless plug aside, the point is
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that these platforms know the best way to get the most
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out of this game is to remove this piece entirely and just hope it doesn't take this one out with it.
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It's a dangerous gambit that might not pay off, but even if it doesn't, it won't really matter.
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Go
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and watch this video next to find out why the the
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stock market is still booming even in a terrible economy to find out how.
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And don't forget to like and subscribe to keep on learning how money works.
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Vocabulário e dicas de fala para esta lição
Esta aula de conversação de nível B2 usa o vídeo “The Gig Economy is Full”. As palavras que mais se repetem: gig, driver, economy, workers. Este vídeo tem 183 frases e 3045 palavras para praticar shadowing. A fala dura 15:22. O falante fala rápido, cerca de 198 palavras por minuto, então espere sons ligados e reduzidos. 84% das palavras estão entre as 3.000 mais comuns do inglês; vale a pena estudar o restante antes de começar.
Vocabulário principal deste vídeo
As 15 palavras mais avançadas do vídeo, com pronúncia e significado:
| Palavra | Pronúncia | Significado |
|---|---|---|
| unemployed adjetivo | /ˌʌnɪmˈplɔɪd/ | desempregado |
| proton substantivo | /ˈpɹoʊ.tɑn/ | próton, protão |
| individually advérbio | /ˌɪndɪˈvɪd͡ʒ(u)əli/ | individualmente |
| fierce adjetivo | /fɪɹs/ | feroz |
| squeeze verbo | /skwiːz/ | espremer, apertar |
| investor substantivo | /ɪnˈvɛs.tə(ɹ)/ | investidor |
| gig substantivo | /ɡɪɡ/ | apresentação, show |
| subscribe verbo | /səbˈskɹaɪb/ | assinar |
| headline substantivo | /ˈhɛd.laɪn/ | manchete |
| provider substantivo | /pɹəˈvaɪ.də(ɹ)/ | fornecedor |
| divide verbo | /dɪˈvaɪd/ | dividir, separar |
| bubble substantivo | /ˈbʌbəl/ | bolha, borbulha |
| automate verbo | /ˈɔ.təˌmeɪt/ | automatizar |
| classify verbo | /ˈklæs.əˌfaɪ/ | classificar |
| compile verbo | /kəmˈpaɪl/ | compilar |
Phrasal verbs que você vai ouvir
| Palavra | Pronúncia | Significado |
|---|---|---|
| find out verbo | descobrir | |
| end up verbo | terminar, acabar | |
| cover up verbo | encobrir | |
| figure out verbo | descobrir, deduzir | |
| fill out verbo | /fɪl ˈaʊt/ | preencher |
| lay off verbo | despedir | |
| lock down verbo | confinar | |
| pay off verbo | compensar |
Gramática neste vídeo
As estruturas que o falante mais usa, com as palavras exatas do vídeo:
| Estrutura | No vídeo |
|---|---|
| Frases condicionais if + oração, will/would + verbo — uma condição e seu resultado | if it doesn't, it won't really |
| Present perfect have/has + particípio passado — uma ação passada que ainda importa agora | has never exactly been · has trended · has had |
| Voz passiva be + particípio passado — o foco está no que acontece, não em quem faz | is disjointed · be employed · be understood |
| Orações relativas who / which + oração — informação extra sobre uma pessoa ou coisa | people who have · numbers, which can · people who couldn |
Pronúncia para ficar de olho
O falante usa 17 contrações e formas reduzidas, como won't, doesn't, didn't. Diga-as na forma curta, do jeito que você ouve.
- Os sons de “th”: algorithm /ˈælɡəɹɪðm̩/, altogether /ɔl.tuˈɡɛð.ɚ/
- Os sons de “sh” e “zh”: friction /ˈfɹɪkʃən/, speculation /ˌspɛk.jəˈleɪ.ʃən/, negotiate /nəˈɡəʊ.ʃi.eɪt/, expectation /ˌɛk.spɛkˈteɪ.ʃən/, nationwide /ˌneɪ.ʃənˈwaɪd/
- Palavras longas — acerte a sílaba tônica: individually /ˌɪndɪˈvɪd͡ʒ(u)əli/, participant /pɑːɹˈtɪsəpənt/, competitor /kəmˈpɛt.ɪ.tɚ/, coordinate /koʊˈɔɹ.dəˌneɪt/, economist /iˈkɑ.nə.mɪst/
Sons difíceis para falantes de português:
- Consoante final — sem acrescentar um “i” depois: incumbent /ɪnˈkʌmbənt/, unemployed /ˌʌnɪmˈplɔɪd/, gig /ɡɪɡ/, subscribe /səbˈskɹaɪb/, divide /dɪˈvaɪd/
- /l/ final — a língua toca o céu da boca, não vira “u”: bubble /ˈbʌbəl/, compile /kəmˈpaɪl/, triangle /ˈtɹaɪˌæŋɡəl/, flexible /ˈflɛk.sɪ.bəl/, structural /ˈstɹʌk(t͡)ʃəɹəl/
- /h/ — um sopro suave, diferente do “r”: headline /ˈhɛd.laɪn/, enhance /ɪnˈhæns/
Como praticar com este vídeo
- Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
- Comece na velocidade 0,75×, faça shadowing frase por frase e volte à velocidade normal quando ficar fácil.
- Grave a sua voz e compare com o original, prestando atenção a palavras como unemployed, proton, individually.
O que é a Técnica de Shadowing?
Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.