Apple’s refreshed second-generation AirPods Pro are down to just $200 on Amazon in a discount almost as good as we saw during October’s Prime Day event. The deal cuts $50 off the normal price of $250. The second-generation AirPods Pro got an upgrade in September that brought improvements to durability and a USB-C port for charging the MagSafe case more conveniently, replacing the Lightning port. While the price could dip down even lower as Black Friday approaches, this is one of best deals we’ve seen as of late.
The upgraded second-generation AirPods Pro have an IP54 rating for better dust resistance than their predecessor. They also received new audio features with the release of iOS 17 that further improves upon the listening experience, including Adaptive Audio, Conversation Awareness, and Personalized Volume. The second-generation AirPods Pro get up to six hours of battery life, with up to 30 hours using the charging case. Even before the upgrade, we counted them among the best earbuds you can get today.
Apple also introduced lossless audio with Apple Vision Pro for the refreshed second-generation AirPods Pro, which buyers will get to appreciate once they finally have the headset in their hands. Otherwise, the AirPods Pro are a top choice for use with the Apple ecosystem of devices, with features like active noise cancellation and an impressive transparency mode. At $200 right now, they’re only $10 more than they were going for on Prime Day.
If you’re looking for something with fewer bells and whistles, Apple’s third-generation AirPods are discounted too. Right now, they’re just $150 on Amazon.
This article originally appeared on Engadget at https://www.engadget.com/apples-upgraded-2nd-gen-airpods-pro-with-usb-c-are-50-off-right-now-182421286.html?src=rss
DIY electric personal vehicles are a field where even hobbyists can meaningfully innovate, and that’s demonstrated by the Mega-Wheelie, a self-balancing one-wheeled skateboard constructed as an experiment in traversing off-road conditions.
[John Dingley] and [Nick Thatcher] have been building and testing self-balancing electric vehicles since 2008, with a beach being a common testing ground. They suspected that a larger wheel was the key to working better on rough ground and dry sand and tested this idea by creating a skateboard with a single wheel. A very big, very wide wheel, in fact.
The Mega-Wheelie houses a 24V LiFePO4 battery pack, 450 W gearmotor with chain and sprocket drive, SyRen motor controller from Dimension Engineering, Arduino microcontroller, and an inertial measurement unit to enable the self-balancing function. Steering is done by leaning, and the handheld controller is just a dead man’s switch that disables the vehicle if the person piloting it lets go.
Design-wise, a device like this has a few challenging constraints. A big wheel is essential for performance but takes up space that could otherwise be used for things like batteries. Also, the platform upon which the pilot stands needs to be as low to the ground as possible for maximum stability. Otherwise, it’s too easy to fall sideways. On the other hand, one must balance this against the need for sufficient ground clearance.
Beaches are rarely covered in perfectly smooth and firm sand, making them a good test area.
In the end, how well did it work? Well enough to warrant a future version, says [John]. We can’t wait to see what that looks like, considering their past 3000 W unicycle’s only limitation was “personal courage” and featured a slick mechanism that shifted the pilot’s weight subtly to aid steering. A video of the Mega-Wheelie (and a more recent unicycle design) is embedded just below the page break.
And just for reference, here is some of [John]’s previous work on a self-balancing unicycle design.
Apple’s 9th generation iPad is $80 off at Amazon right now. The discount brings the 64GB variant down to just $250 from its regular price of $330, a record low typically only seen on Prime Day. You can also snag the 9th-gen iPad with 256GB of storage for $80 off at Amazon, where it’s currently down to $400 from its usual $480.
The 9th-gen iPad came out in 2021, but it’s still a solid tablet especially if you’re on a budget. While its A13 Bionic chip isn’t the fastest or most powerful, it’s more than enough for basic productivity tasks, browsing and streaming. It earned a score of 86 when we reviewed it back at the time of its release, and it’s still one of the best iPads you can get that won’t break the bank.
It has a heftier build than the newer, sleeker models, with chunky bezels framing its 10.2-inch Retina Display, and a physical Home button with Touch ID. Apple’s 9th-gen iPad also still has a headphone jack and charges via lightning port. It has a 12MP ultrawide front camera and 8MP back camera, and supports Apple’s Center Stage video calling feature.
The 9th generation iPad comes in Silver and Space Gray, and the discount applies to both color variants for the Wi-Fi only model. It’s a great option for the casual iPad user, and the price right now can’t be beat. But, if those specs aren't quite cutting it, Amazon is also running a deal on the 10th generation iPad, which is a step up. That model is currently $50 off.
This article originally appeared on Engadget at https://www.engadget.com/apples-9th-gen-ipad-is-back-to-its-all-time-low-price-of-250-ahead-of-black-friday-154710678.html?src=rss
The explosive growth in artificial intelligence in recent years — crowned with the meteoric rise of generative AI chatbots like ChatGPT — has seen the technology take on many tasks that, formerly, only human minds could handle. But despite their increasingly capable linguistic computations, these machine learning systems remain surprisingly inept at making the sorts of cognitive leaps and logical deductions that even the average teenager can consistently get right.
Focusing on the five evolutionary "breakthroughs," amidst myriad genetic dead ends and unsuccessful offshoots, that led our species to our modern minds, Bennett also shows that the same advancements that took humanity eons to evolve can be adapted to help guide development of the AI technologies of tomorrow. In the excerpt below, we take a look at how generative AI systems like GPT-3 are built to mimic the predictive functions of the neocortex, but still can't quite get a grasp on the vagaries of human speech.
GPT-3 is given word after word, sentence after sentence, paragraph after paragraph. During this long training process, it tries to predict the next word in any of these long streams of words. And with each prediction, the weights of its gargantuan neural network are nudged ever so slightly toward the right answer. Do this an astronomical number of times, and eventually GPT-3 can automatically predict the next word based on a prior sentence or paragraph. In principle, this captures at least some fundamental aspect of how language works in the human brain. Consider how automatic it is for you to predict the next symbol in the following phrases:
One plus one equals _____
Roses are red, violets are _____
You’ve seen similar sentences endless times, so your neocortical machinery automatically predicts what word comes next. What makes GPT-3 impressive, however, is not that it just predicts the next word of a sequence it has seen a million times — that could be accomplished with nothing more than memorizing sentences. What is impressive is that GPT-3 can be given a novel sequence that it has never seen before and still accurately predict the next word. This, too, clearly captures something that the human brain can _____.
Could you predict that the next word was do? I’m guessing you could, even though you had never seen that exact sentence before. The point is that both GPT-3 and the neocortical areas for language seem to be engaging in prediction. Both can generalize past experiences, apply them to new sentences, and guess what comes next.
GPT-3 and similar language models demonstrate how a web of neurons can reasonably capture the rules of grammar, syntax, and context if it is given sufficient time to learn. But while this shows that prediction is part of the mechanisms of language, does this mean that prediction is all there is to human language? Try to finish these four questions:
If 3x + 1 = 3, then x equals _____
I am in my windowless basement, and I look toward the sky, and I see _____
He threw the baseball 100 feet above my head, I reached my hand up to catch it, jumped, and _____
I am driving as fast as I can to LA from New York. One hour after passing through Chicago, I finally _____
Here something different happens. In the first question, you likely paused and performed some mental arithmetic before being able to answer the question. In the other questions, you probably, even for only a split second, paused to visualize yourself in a basement looking upward, and realized what you would see is the ceiling. Or you visualized yourself trying to catch a baseball a hundred feet above your head. Or you imagined yourself one hour past Chicago and tried to find where you would be on a mental map of America. With these types of questions, more is happening in your brain than merely the automatic prediction of words.
We have, of course, already explored this phenomenon—it is simulating. In these questions, you are rendering an inner simulation, either of shifting values in a series of algebraic operations or of a three-dimensional basement. And the answers to the questions are to be found only in the rules and structure of your inner simulated world.
I gave the same four questions to GPT-3; here are its responses (responses of GPT-3 are bolded and underlined):
If 3x + 1 = 3 , then x equals 1
I am in my windowless basement, and I look toward the sky, and I see a light, and I know that it is a star, and I am happy.
He threw the baseball 100 feet above my head, I reached my hand up to catch it, jumped, and caught it. It was a lot of fun!
I am driving as fast as I can to LA from New York. One hour after passing through Chicago, I finally get to see the Pacific Ocean.
All four of these responses demonstrate that GPT-3, as of June 2022, lacked an understanding of even simple aspects of how the world works. If 3x + 1 = 3, then x equals 2/3, not 1. If you were in a basement and looked toward the sky, you would see your ceiling, not stars. If you tried to catch a ball 100 feet above your head, you would not catch the ball. If you were driving to LA from New York and you’d passed through Chicago one hour ago, you would not yet be at the coast. GPT-3’s answers lacked common sense.
What I found was not surprising or novel; it is well known that modern AI systems, including these new supercharged language models, struggle with such questions. But that’s the point: Even a model trained on the entire corpus of the internet, running up millions of dollars in server costs — requiring acres of computers on some unknown server farm — still struggles to answer common sense questions, those presumably answerable by even a middle-school human.
Of course, reasoning about things by simulating also comes with problems. Suppose I asked you the following question:
Tom W. is meek and keeps to himself. He likes soft music and wears glasses. Which profession is Tom W. more likely to be?
1) Librarian
2) Construction worker
If you are like most people, you answered librarian. But this is wrong. Humans tend to ignore base rates—did you consider the base number of construction workers compared to librarians? There are probably one hundred times more construction workers than librarians. And because of this, even if 95 percent of librarians are meek and only 5 percent of construction workers are meek, there still will be far more meek construction workers than meek librarians. Thus, if Tom is meek, he is still more likely to be a construction worker than a librarian.
The idea that the neocortex works by rendering an inner simulation and that this is how humans tend to reason about things explains why humans consistently get questions like this wrong. We imagine a meek person and compare that to an imagined librarian and an imagined construction worker. Who does the meek person seem more like? The librarian. Behavioral economists call this the representative heuristic. This is the origin of many forms of unconscious bias. If you heard a story of someone robbing your friend, you can’t help but render an imagined scene of the robbery, and you can’t help but fill in the robbers. What do the robbers look like to you? What are they wearing? What race are they? How old are they? This is a downside of reasoning by simulating — we fill in characters and scenes, often missing the true causal and statistical relationships between things.
It is with questions that require simulation where language in the human brain diverges from language in GPT-3. Math is a great example of this. The foundation of math begins with declarative labeling. You hold up two fingers or two stones or two sticks, engage in shared attention with a student, and label it two. You do the same thing with three of each and label it three. Just as with verbs (e.g., running and sleeping), in math we label operations (e.g., add and subtract). We can thereby construct sentences representing mathematical operations: three add one.
Humans don’t learn math the way GPT-3 learns math. Indeed, humans don’t learn language the way GPT-3 learns language. Children do not simply listen to endless sequences of words until they can predict what comes next. They are shown an object, engage in a hardwired nonverbal mechanism of shared attention, and then the object is given a name. The foundation of language learning is not sequence learning but the tethering of symbols to components of a child’s already present inner simulation.
A human brain, but not GPT-3, can check the answers to mathematical operations using mental simulation. If you add one to three using your fingers, you notice that you always get the thing that was previously labeled four.
You don’t even need to check such things on your actual fingers; you can imagine these operations. This ability to find the answers to things by simulating relies on the fact that our inner simulation is an accurate rendering of reality. When I mentally imagine adding one finger to three fingers, then count the fingers in my head, I count four. There is no reason why that must be the case in my imaginary world. But it is. Similarly, when I ask you what you see when you look toward the ceiling in your basement, you answer correctly because the three-dimensional house you constructed in your head obeys the laws of physics (you can’t see through the ceiling), and hence it is obvious to you that the ceiling of the basement is necessarily between you and the sky. The neocortex evolved long before words, already wired to render a simulated world that captures an incredibly vast and accurate set of physical rules and attributes of the actual world.
To be fair, GPT-3 can, in fact, answer many math questions correctly. GPT-3 will be able to answer 1 + 1 =___ because it has seen that sequence a billion times. When you answer the same question without thinking, you are answering it the way GPT-3 would. But when you think about why 1 + 1 =, when you prove it to yourself again by mentally imagining the operation of adding one thing to another thing and getting back two things, then you know that 1 + 1 = 2 in a way that GPT-3 does not.
The human brain contains both a language prediction system and an inner simulation. The best evidence for the idea that we have both these systems are experiments pitting one system against the other. Consider the cognitive reflection test, designed to evaluate someone’s ability to inhibit her reflexive response (e.g., habitual word predictions) and instead actively think about the answer (e.g., invoke an inner simulation to reason about it):
Question 1: A bat and a ball cost $1.10 in total. The bat costs $1.00 more than the ball. How much does the ball cost?
If you are like most people, your instinct, without thinking about it, is to answer ten cents. But if you thought about this question, you would realize this is wrong; the answer is five cents. Similarly:
Question 2: If it takes 5 machines 5 minutes to make 5 widgets, how long would it take 100 machines to make 100 widgets?
Here again, if you are like most people, your instinct is to say “One hundred minutes,” but if you think about it, you would realize the answer is still five minutes.
And indeed, as of December 2022, GPT-3 got both of these questions wrong in exactly the same way people do, GPT-3 answered ten cents to the first question, and one hundred minutes to the second question.
The point is that human brains have an automatic system for predicting words (one probably similar, at least in principle, to models like GPT-3) and an inner simulation. Much of what makes human language powerful is not the syntax of it, but its ability to give us the necessary information to render a simulation about it and, crucially, to use these sequences of words to render the same inner simulation as other humans around us.
This article originally appeared on Engadget at https://www.engadget.com/hitting-the-books-a-brief-history-of-intelligence-max-bennett-mariner-books-143058118.html?src=rss
A sounding rocket toting a special imaging and spectroscopy instrument will take a brief trip to space Sunday night to try and capture as much data as it can on a long-admired supernova remnant in the Cygnus constellation. Its target, a massive cloud of dust and gas known as the Cygnus Loop or the Veil Nebula, was created after the explosive death of a star an estimated 20,000 years ago — and it’s still expanding.
NASA plans to launch the mission at 11:35 PM ET on Sunday October 29 from the White Sands Missile Range in New Mexico. The Integral Field Ultraviolet Spectroscopic Experiment, or INFUSE, will observe the Cygnus Loop for only a few minutes, capturing light in the far-ultraviolet wavelengths to illuminate gasses as hot as 90,000-540,000 degrees Fahrenheit. It’s expected to fly to an altitude of about 150 miles before parachuting back to Earth.
The Cygnus Loop sits about 2,600 light-years away, and was formed by the collapse of a star thought to be 20 times the size of our sun. Since the aftermath of the event is still playing out, with the cloud currently expanding at a rate of 930,000 miles per hour, it’s a good candidate for studying how supernovae affect the formation of new star systems. “Supernovae like the one that created the Cygnus Loop have a huge impact on how galaxies form,” said Brian Fleming, principal investigator for the INFUSE mission.
“INFUSE will observe how the supernova dumps energy into the Milky Way by catching light given off just as the blast wave crashes into pockets of cold gas floating around the galaxy,” Fleming said. Once INFUSE is back on the ground and its data has been collected, the team plans to fix it up and eventually launch it again.
This article originally appeared on Engadget at https://www.engadget.com/nasa-is-launching-a-rocket-on-sunday-to-study-a-20000-year-old-supernova-193009477.html?src=rss
Threads was missing a lot of features users would expect from a service similar to Twitter's (now X's) when it launched. Over the past few months, however, it has been been rolling out more and more new features to give users a more robust experience, including polls, an easy way to post GIFs and the ability to quote posts on the web. Still, since it doesn't have an API, third-party developers can't conjure features specific to their services that would make the social network a more integral part of people's everyday lives. An example of that is local transportation agencies being able to automatically post service alerts when a train is delayed. According to Instagram chief Adam Mosseri, though, Threads is working on an API for developers — he just has concerns about how it's going to be used.
As first reported by TechCrunch, Mosseri responded to a conversation on the platform about having a TweetDeck-like experience for Threads. In a response to a user saying that Threads has no API yet, the executive said: "We're working on it." He added that he's concerned that the API's launch could mean "a lot more publisher content and not much more creator content," but he's aware that it "seems like something [the company needs] to get done."
Mosseri previously said that Threads won't amplify news, which may have been disappointing to hear for publishers and readers looking to leave X. Instead, he said, Threads wants to "empower creators in general." More recently, in an AMA he posted on the platform, Mosseri said that that his team's long-term aspiration is for Threads to become "the de facto platform for public conversations online," which means being both culturally relevant and big in terms of user size. He said he believes Threads has a chance of surpassing X, but he knows that his service has a long way to go. For now, he keeps his team focused on making people's experience better week by week.
Mark Zuckerberg recently announced that Threads has "just under" 100 million monthly active users. Like Mosseri, he is optimistic about its future and said that there's a "good chance" it could reach 1 billion users over the next couple of years.
This article originally appeared on Engadget at https://www.engadget.com/instagram-head-says-threads-is-working-on-an-api-for-developers-140049094.html?src=rss
The iRobot Roomba Combo j7+ is the top 2-in-1 pick in our guide to the best robot vacuums, as it adds a retractable mop for cleaning hard floors onto a powerful robovac for carpets and other surfaces. It's expensive at its usual list price of $1,000, but right now you can get it for $699 at Wellbots. Just use the code ENGROOMBA300 at checkout. While that's still far from cheap, it does mark the largest discount we've tracked.
The Roomba Combo j7+ is undoubtedly a luxury purchase, but we found its vacuuming and mopping capabilities to mostly work as advertised. As a robot vacuum, it offers strong suction power, accurate home mapping and intelligent obstacle avoidance, including a strong knack for avoiding pet waste. In its "vacuum and mop" mode, it's smart enough to know when it's rolling over hard floors instead of carpet, then only mop the former. The battery generally lasts between 90 to 180 minutes depending on how often you mop, and the whole thing works with Alexa and the Google Assistant. This model also comes with a (noisy) self-emptying base station, which the vacuum automatically retreats to when it's done cleaning.
It's not perfect: You'll have to refill the water tank fairly often, there's no mop-only mode and the mopping functionality isn't as efficient as just using a Swiffer. iRobot's Home app remains easy to use, but all robovacs require the occasional maintenance and intervention. Still, a device like this makes cleaning more hands-off than it'd be otherwise, so the Combo j7+ could be worth it if your home has a mix of carpet, hardwood, laminate and other surfaces. For more details, check out Engadget Senior Editor Daniel Cooper's write-up of his experience with the device. Just note that, like many robot vacuums with obstacle avoidance, the j7+ comes with a built-in camera. That may raise privacy concerns for some, particularly with Amazon in the process of acquiring iRobot.
A couple of less expensive Roomba 2-in-1s are also on sale. The Roomba Combo j5+ is down to $499 with the same ENGROOMBA300 code, while the standard Roomba Combo j5 is available for $349 with the code ENGROOMBA250. Those are $300 and $250 discounts, respectively, and both represent all-time lows. The Combo j5+ is a newer midrange alternative to the Combo j7+: It has most of the same features, but it lacks the j7+'s retractable mop arm and ability to automatically avoid carpets while mopping. Instead, you have to swap in a vacuum-only bin (or just move your rugs) when you want to clean a carpeted room. You also have to empty its vacuum/mop combo bin manually. The standard j5 is essentially the same device but doesn't come with a self-emptying base station.
This article originally appeared on Engadget at https://www.engadget.com/irobots-roomba-combo-vacuum-and-mops-are-up-to-300-off-right-now-130031081.html?src=rss
Vice president Prabhakar Raghavan testified Friday that Google paid $26.3 billion in 2021 for the purpose of maintaining default search engine status and acquiring traffic, Bloombergreports. It's likely the lion’s share of that sum went to Apple, which it has showered with exorbitant sums for many years in order to remain the default search option on iPhone, iPad and Mac.
Raghavan, who was testifying as part of the DOJ's ongoing antitrust suit against the company, said Google’s search advertising made $146.4 billion in revenue in 2021, which puts the $26 billion it paid for default status in perspective. The executive clarified that default status was the most costly part of what it pays to acquire traffic.
Raghavan didn’t mention how much of the $26.3 billion went to Apple. But CNBCreports that an estimate from private wealth management firm Bernstein ballparked that Google could pay Apple up to $19 billion this year for the default privilege.
A slide shown in court revealed that, in 2014, Google brought in $47 billion in search revenue while paying $7.1 billion for default status. Raghavan testified that Google’s overall default search engine payments nearly quadrupled from 2014 to 2021, while its search advertising revenue (roughly) tripled.
Google objected to making the figures public, arguing it would hurt its ability to negotiate future contracts. Judge Amit Mehta, overseeing the case, disagreed.
This article originally appeared on Engadget at https://www.engadget.com/google-paid-26-billion-in-2021-for-default-search-engine-status-203129384.html?src=rss
Barbarian is one of the more memorable horror movies of the last few years. It tells a tale of a young woman who finds that someone is already staying at her rental home. She has little choice but to stay there since nearby hotels are all booked up. That alone is a nightmare scenario but the film goes into some truly wild directions from there. On the surface, it seems like an odd choice to turn into a video game, but that's exactly what's happening.
New Regency Pictures and Friday the 13th: The Game and Evil Dead: The Gamedeveloper Diversion3 Entertainment have teamed up to bring Barbarian to PC and consoles. Despite the multiplayer format of the studio's previous projects, this will be a single-player, narrative-focused title which will "expand on the settings, characters and creatures of Barbarian." There's no release timeframe as yet.
“We’re very excited to work with the team at New Regency to expand on the settings, characters and creatures of Barbarian,” Tim Hesse, an executive producer at Diversion3 Entertainment, told Variety in a statement. “The film did a magnificent job of not only scaring audiences with its unexpected and horrifying twists and turns, but also in establishing strong characters thrown into terrifying situations. We look forward to exploring these themes further in the game.”
A straight adaptation of the film's story probably wouldn't work as a game. But there's certainly potential for it to work as a tension-filled survival horror title given (mild spoilers) how much of the Barbarian takes place in terrifying underground tunnels.
For the uninitiated, here's the trailer for Barbarian. Happy Halloween.
This article originally appeared on Engadget at https://www.engadget.com/horror-movie-barbarian-is-getting-a-video-game-adaptation-200727185.html?src=rss
When Netflix first unveiled its streaming video service in 2007, it felt like a miracle. Netflix's DVD customers in the US, who were paying between $5.99 to $17.99 a month, instantly had access to 1,000 movies over a web browser. No more waiting for DVDs in the mail, no ads like TV – just hit a button and watch. Instantly! Now that seems like ages ago. Netflix's most premium 4K streaming plan now costs $23 a month, while its standard subscription without ads costs $15.49 a month. (There is a standard plan with ads for $6.99 a month, but that doesn't support offline downloads and also doesn't include some content.)
Netflix has also been cracking down on account sharing recently, which is great for its overall earnings and subscriber count, but bad for anyone trying to save a buck. You'll have to pay an extra $7.99 a month to add more member slots to the standard and premium plans.
And it’s not just Netflix. Over the past year, just about every major streaming service has raised its prices considerably. Apple TV+ is doubling its original price to $10 a month ($99 annually). Disney+ saw a hefty increase as well to $14 a month for its ad-free premium tier. For those who subscribe to multiple services, it's easy to think we're back in the bad old days of cable TV, where we ended up spending gobs of money for hundreds of channels.
But let's not get dramatic. Subscribing to the streaming services you use the most is still far cheaper than going for a typical cable plan. In my area, Comcast's most popular plan with over 125 channels is listed at $60 a month, but the company hides the additional $27.80 broadcast network fee and $13.40 regional sport licensing fee. My actual monthly cost starts at $101.20, and that doesn't include taxes, equipment rental fees (at least $10 a month) and other additions Comcast may coax you into. (Want 300 hours of Cloud DVR? That's another $20 monthly!)
According to the Bureau of Labor Statistics, the average urban consumer spends an eye-watering $575 a month on cable, satellite or live streaming TV service. To be clear, those numbers reflect some customers spending a ton more on sports and other packages compared to others. But still, even the prospect of spending $370 a month on cable (the BLS's consumer average from 2010) feels unfathomable. All of a sudden, Netflix creeping toward $25 doesn't seem so bad — especially since cable customers also have to subscribe to streaming services to see their original shows.
Netflix
While some have argued that streaming price hikes signal the end of the cord-cutting dream, that's far from true. Cable prices were already high a decade ago, and they've risen considerably since then. (Broadcast fees alone were estimated to jump between 8 to 10 percent between 2016 and 2019.) If anything, the case for cord-cutting is even stronger now. With the wealth of content available on streaming services, do you really need to pay hundreds to sit through another HGTV marathon? Especially when you can find some HGTV content on Max, and similar shows on other streamers?
Nobody likes to see their favorite services getting more expensive. You could easily argue that streaming prices hikes fall firmly within Corey Doctorow's concept of internet enshittification, wherein companies provide cheap and useful services to grow their userbase, but inevitably make the experience worse to squeeze out more money and appease their investors. Unless an online service is being run as a non-profit or completely free side project, enshittification seems inevitable.
But it's worth acknowledging why streaming services were so cheap to begin with. Netflix's streaming service was practically an experiment early on — it was rolled into existing subscription plans, and you could only watch up to 18 hours a month. When Netflix launched its standalone streaming subscription in 2010, it was only $7.99 a month — a price that held true until its basic plan jumped a whole dollar in 2019. While the company introduced more expensive standard and premium plans along the way, the entry plan always seemed like a tremendous deal. Who wouldn't want instant access to thousands of movies and TV shows for the price of two coffees?
Like many startups during the 2010s, Netflix continually raised tons of money (around $5 billion) without making enormous profit — or at least, not profit in line with the tens of billions the company has spent on original content over the last decade. Enticing new subscribers and keeping them was far more important to Netflix than actually being a sustainable business. So it wasn't too surprising when other services like HBO Max, Disney+ and Apple TV+ launched with low prices competitive with Netflix.
According to Janko Roettgers, author of the newsletter Lowpass, and a former media and technology reporter at Variety, Netflix had an advantage over the competition because its legacy DVD business could fund its streaming ambitions. Other companies like Disney and Warner Bros. had to decide how streaming fit within their existing TV channels and movie studios.
"Now [Netflix is] making money with streaming across the world, and they're starting to get into gaming," Roettgers noted on the Engadget Podcast this week. "So they're pretty quick at following up. And if you look at some of these legacy media companies, well, they still have linear networks. And those are declining slowly and slowly, and it's taking them a long time to figure out [...] Should we get out of this? How many can we keep running? How many of those do we need to shut down?"
With interest rates on the rise and investors worried about the economy, raising prices was the inevitable next step for every streaming provider. And unfortunately, that trend won't be reversed anytime soon. At best, we can only hope that the threat of losing users and pressure from competition will keep Netflix and others from reaching the dreaded highs of cable.
But don't forget, there's one thing you can do with streaming services that's far more difficult with cable companies: You can cancel and subscribe easily online. You don't need to set aside time and emotional energy to deal with a customer service rep on the phone, or block out a morning for a technician to visit. That potential for churn hangs over every streaming provider. So if their prices get too high, or they're not actually providing enough valuable content to watch, just leave.
Still, it’s worth remembering that access to media is cheaper than ever. You don’t have to worry about spending a ton to rent movies from Blockbuster or your local video store. There aren’t any late fees to worry about. And while I miss the heyday of DVDs, buying just one of those discs could cover a month of service across two streaming services today (sometimes three!).
So sure, it stinks that Netflix is getting more expensive. But, personally, I’d easily take these higher prices over life before the streaming era.
This article originally appeared on Engadget at https://www.engadget.com/is-streaming-video-even-still-worth-it-192651141.html?src=rss
