AVC.com was apparently being hammered by an avalanche of traffic. Not long after the articles about the 2010s and 2020s were upvoted to the HN front page, the site was persistently returning database connection errors. So archive links were posted in both threads.
I very strongly disagree with #3. Machine learning has not yet proved its value outside of a handful of key areas (image recognition, voice recognition/synthesizing, etc). I think it's entirely possible we'll look back on today as a kind of machine learning mania. I certainly believe there is plenty of room to build sustainable businesses without a machine learning asset (and the associated data collection), and it's in no way "table stakes for every tech company, large and small".
You're wrong. Machine Learning is driving all of the ad placement on major ad platforms, personalization on all top social/media apps, search ranking for google, translation, speech recognition, finance, fraud detection.
In the next decade it will start taking over computer graphics, medicine, manufacturing, surveillance, hardware / software and every other aspect of our lives.
EDIT: for people who are downvoting me, here are some examples of how machine learning is and will transform our lives:
1. 3D graphics: https://www.youtube.com/watch?v=FlgLxSLsYWQ Andrew Price doesn't have a technical background but does a decent job summarizing some of the ways deep learning will transform computer graphics. Soon all of the mocap, character design and animation will be driven by deep learning systems.
2. Computer Architecture and Traditional Software: https://www.youtube.com/watch?v=TTpKWOuzOxc There's a ton of recent research showing that you can use machine learning to beat human crafted heuristics in hardware, scheduling, compiler design and query planning.
> Machine Learning is driving all of the ad placement on major ad platforms, personalization on all top social/media apps, search ranking for google,
Yep, and the results are hilarious or tragic depending on how you look at it:
We keep getting ads for the thing we bought yesterday, ads for dating sites after we got married and had kids (continously, for ten years despite my utter lack of interest), search results keep getting worse[0], obvious spammers keep on spamming in social media (seriously, it seems a simple regex filter could have done a better job to reduce crypto scamming in replies on Twitter than whatever was there last time I checked.)
[0]: some people will always claim it is because black hat SEO is so much worse, but that doesn't explain why Google sometimes can neither understand doublequotes nor the verbatim option anymore. That is not the result of black hat SEO but of sloppy maintenance, and I guess so is a number of other problems.
> We keep getting ads for the thing we bought yesterday, ads for dating sites after we got married and had kids (continously, for ten years despite my utter lack of interest), search results keep getting worse[0], obvious spammers keep on spamming in social media (seriously, it seems a simple regex filter could have done a better job to reduce crypto scamming in replies on Twitter than whatever was there last time I checked.)
That's because in a lot of cases they're optimizing for the wrong metrics, as in maximizing their revenue instead of your utility.
There's way more content on the web than there was in the early 2000s, most of it in form of "content marketing" and explicitly attempting to game the system.
If you look at recent results from TREC, it's pretty clear that machine learning provides a large boost over the traditional retrieval systems, on any metric that you want to optimize.
> That's because in a lot of cases they're optimizing for the wrong metrics, as in maximizing their revenue instead of your utility.
What? How do they maximize their revenue by spending money on ads the users actually laugh about for how bad their targeting is?
Based on what I know about machine learning - it almost always gives great short-term results, and it almost always fails to deliver the expected long-term results. What's worse is this comes with the weirdest most indecipherable bugs that pop up more and more over time. Unless you have a large enough database to show statistical errors that are negligible (something like 99.9999% precision or recall, depending on your metrics), you should assume it will break in ways you cannot possibly predict. And even then, you might be using the wrong training data without even realizing it.
I'm not saying ML is bad, although I am saying it is ridiculously overhyped. I'm saying ML is still nascent enough nobody really knows how a lot of edge cases will shake out, simply because there are too many edge cases to test before putting it into production.
It's not hard to find examples of ML algorithms gone wrong even for sites like Amazon.
Recommending something you bought before is probably a much better bet than showing you random items from their huge catalogue.
You can have a system that generates them a ton of cash while making mistakes in some cases, outliers are inevitable and feedback loops in recommender systems can lead to such issues. Amazon wouldn't deploy these systems if they didn't move the needle.
Nobody is saying the ML algorithm (or any algorithm) is expected to be perfect.
> Amazon wouldn't deploy these systems if they didn't move the needle.
This is an appeal to authority that Amazon executives are immune to making mistakes. They could have bad metrics. They could have bad incentives encouraging managers to make poor decisions (basically this describes everything wrong with Google today). They could be incompetent. They could be focused on short-term gains at the expense of long-term gains because it maximizes their personal net wealth and they can just jump ship in a few years.
Again, nobody is saying ML algorithms are worthless. I just don't believe (and have lots of reasons based on personal experience I won't go into) that they are 10% as useful as the industry wants you to believe.
I'm saying that they have systems in place to measure the impact of the code that they put in production on their bottom line and if those recommendations didn't move their profit margins in the right direction they wouldn't be using them.
> I'm saying that they have systems in place to measure the impact of the code that they put in production on their bottom line
There's a story, and I think it was (re)posted here recently about a series of MBAs all optimizing the cost of the burger bums by removing seeds until there are three seeds neatly laid out at the top.
It might be measurable all the way but at some point it becomes ridiculous. For me that time was some months ago. For the rest of Internet they might manage to reduce the quality once or twice more before it becomes obvious.
This is my way of fighting back. By posting here and on my blog and getting upvoted a lot for pointing out what many can already feel. By letting people who read HN know that yes, that feeling they have that a lot of their ads are wasted because of bad targeting might very well be true.
That's definitely true, maximizing shareholder value is all about extracting as much value as possible from your customers, until it no longer works.
I'm against most forms of recommendations as well, that doesn't mean that they're not valuable to the businesses deploying them. In most cases the end users of these systems are not the real customers of these platforms, and they're there to serve the advertisers who keep these businesses running.
I know what you're saying. I don't think you understand what I'm saying, which is those systems can be wrong or they might not be optimizing for the bottom line. Why didn't their systems catch that bug that was discounting expensive equipment for more than 90% that I linked in my previous post? People make mistakes. And for a frame of reference, Google's corporate policies definitely don't optimize for the bottom line, and it took a minimum of 5 years for it to affect them negatively (I'd guess closer to 10+).
> Recommending something you bought before is probably a much better bet
Not if it's something I only buy once a year, for example. That's where "learning" part should come in. You don't need any learning to just parrot me back my inputs.
> Amazon wouldn't deploy these systems if they didn't move the needle.
I don't know about Amazon, but I've recently read on HN some articles strongly suggesting almost nobody is properly measuring the impact of ads, let alone the impact of "targeting". In most cases, people more or less just stuff money into ads budgets, because that's what you do, and they get customers - because people still need to buy things, regardless of any targeting - but the casual link between the former and the latter is not really very well established.
> in maximizing their revenue instead of your utility.
I am not sure how these are contradictory. My interest is buying things I want. Their interest is selling me things I'd buy. How showing dating site ads to a married person promotes any of those? Where the revenue would come from? Or do you mean it's ad agency revenue, not advertiser revenue? In that case we clearly have a case of agent problem.
2% of households returning their refrigerators and buying a new one seems pretty high, although I don't have any data to back this up. How many people reading this have made multiple (separate) refrigerator purchases in a week?
With arbitrary assumptions about statistics, you can "statistically prove" pretty much anything. Until the 2% figure is substantiated, it's all baloney.
You are assuming there's a way to do it well and not well, but what if nobody actually knows how it is - doing it well? What if marketing is just given a budget of X million dollars to spend on advertising, but nobody there actually knows whether doing X or Y works, but they know everybody does X, so if they do X, there's no chance they'd be fired for that?
Machine learning finally came of age in the 2010s and is now table stakes for every tech company, large and small.
I would say it's table stakes for large companies in certain areas, but not small (see my other reply).
Machine learning seems to be good at squeezing percentage points out of monopoly with a business that already works. That's very different than "table stakes for small companies". Small companies need optimize their product with fundamental changes.
For example, in the early days of AirBNB there was a more inconsistent user experience, which you heard about in the media. I think they addressed those problems with policies, incentives, and some elbow grease (kicking bad users off the platform, etc.). Not machine learning. Machine learning doesn't help you expand into Europe or Asia, etc.
ML is certainly valuable for speech recognition, translation, and fraud detection, but those are somewhat niche applications. It's a long way from "every tech company, large and small".
As for ad placement, social media, and search ranking: is that actually effective? The one constant with ads and social media and web search, IME, is that it just never gets any better. From the Lycos/AltaVista days to Google there was a huge jump in search result quality, and then it plateaued.
The ads I see online are still laughably bad. They're more professionally produced than 10 or 20 years ago, but they're still never for any category of product that I'd ever buy. Targeted and tracked advertising seems like a big scam.
I cannot recall ever buying anything from any ad targeted at me through a website I visit. Am I a unique snowflake in that the AI always guesses wrong for just me, or is AI based advertising simply a total failure?
I have bought many things from ads related to the media I am reading. For example, I read muscle car magazines, and buy parts/tools from the ads in those magazines. Back in the era of print mags, my company would do well placing ads for compilers next to relevant programming articles.
> ML is certainly valuable for speech recognition, translation, and fraud detection, but those are somewhat niche applications. It's a long way from "every tech company, large and small".
Those were just some of the most visible examples. OCR, image recognition, NLP and speech recognition alone are enough to revolutionize most data entry jobs and we'll see a ton of startups using them to do just that for every industry.
There are countless other examples of machine learning applications, including drug discovery, radiology, production line quality assurance, etc.
> The ads I see online are still laughably bad.
Have ads ever been good? You see the ads that you're seeing because someone is paying a lot of money to put them there.
Bloomberg spent 120 million on ads last month, ad platforms had to find eyeballs for them.
IMO, you can get a pretty good ad experience by sticking to content-targeted ads.
If I'm looking at, for example, "how to programmatically control my model railway with a Raspberry Pi", there's a large number of easy to target, relevant products you can advertise against that. You don't need a gigabyte of creepy backstory on me to figure out "hey, show me ads for the products mentioned in the article, and there's a good chance I'll buy them because it's convenient."
Yeah, Mike Bloomberg might be willing to pay $1 to show his ugly mug next to that page, but the ad from a modeler's supply shop, who would only have paid 95 cents, would have felt much more appropriate to the consumer. I know some ad networks try to measure quality via clickthrough rate, but I'm not sure it was weighted in a way to nerf this issue.
Another problem is that a pure-content model doesn't extend to all types of site. There's no suitable product to advertise against "six die in New Year's party gone horribly wrong." and nobody wanted to run low-yield CPM branding ads, so they had to backfill wth retargeting and profile-based ads instead.
Because the fault here doesn't lie with ML, but with errors of judgment made by humans when setting up the targeting demographics for their ads.
There's still a lot of anti-data sentiment in the paid ad industry, where media buyers will guide themselves by what they think their customer looks like, and not what data tells them it is.
And until this is a fixed behavior, you'll keep on seeing untargeted ads.
> In the next decade it will start taking over computer graphics, medicine, manufacturing, surveillance, hardware / software and every other aspect of our lives.
The counter-example to your point is that deep learning maniacs were predicting self driving to be a done deal by 2020 and we now realize we are so far away from that goal. Machine learning will only work well for one-pony tricks problems that can be clearly isolated. Nothing like "every other aspect of our lives".
I think people are downvoting you because of the word you are wrong, might be I disagree would be a better phase.
I do think in general Machine learning is only just started with respect to All industry, not just tech. There is so much shown what could be done, the next 10 years will be very interesting.
This guy is downvoted but he's right. A huge amount of Facebook's and Google's value growth comes from machine learning driving their various algorithms.
> Machine Learning is driving all of the ad placement on major ad platforms
And the results are, as far as I can see, horrible. I mean yes, if I make a mistake to search for shoes on Google, half of the internet will be showing me shoe ads for the next 6 months (how many feet do you think I have? Do you think I buy shoes in dozens?) - but presenting it as the triumph of ML is IMHO an overreach.
> personalization on all top social/media apps
From what I see, the same apps struggle to not get sued because of the said personalization regularly pushes sex content on kids, triggers on snowflakes and election interference on potential voters. Or at least that's what I hear every time next round of censorship is introduced into the social media. Somehow I am still not seeing a cause for celebration here.
> search ranking for google
Same as above, plus one shouldn't use Google search anyway. Use DuckDuckGo.
> translation, speech recognition
OK, here it got pretty good results, though sometimes it is as good as a very drunk chimp who got into a dictionary store, but in many other times it's decent. You get this one.
> finance, fraud detection
As a consumer, haven't noticed it. 100% of fraud on my cards have been detected by me reviewing my credit card statements. 100% of fraud alerts by banks have been false positives. I do not begrudge that specifically, I'd better have false positives than more fraud, but not seeing much ML-driven progress there tbh.
> In the next decade it will start taking over computer graphics, medicine, manufacturing, surveillance, hardware / software and every other aspect of our lives.
Not sure what "computer graphics" means, medicine probably not, surveillance maybe, but that's exactly the opposite of what I'd want, software definitely not even close, for the rest I'm not even sure what you're talking about.
> here's a ton of recent research showing that you can use machine learning to beat human crafted heuristics in hardware, scheduling, compiler design and query planning.
I'll believe it when I see ML-driven code generator doing something useful without tight supervision by humans. I can believe ML doing specific heuristics better (heck, doing exactly that has been part of my job recently) but there's a huge difference between "figure out exactly how much sugar makes the specific cookie recipe taste the best" and "invent whole cookie recipe from scratch and bake the cookie". I am sure ML would be useful for the former, for the latter... I'll believe it when I see it.
Machine learning is interesting in computer graphics for denoising path traced images för example. There also some uses in game developmemt where graphicsl artefacts might be detected during automated test runs instead of having an army of QA people.
Pretty sure ML have shown promising results in cancer classification in images (such as xray, etc). Not sure how this will pan out but the limited scope seems ideal .
Having worked in this industry for a while I just want to point out that the vast majority of fraud detection is done for the merchant, not the customer.
I think machine learning masquerading as AI is definitely over-hyped. The real revolution is open source predictive modeling and statistical techniques becoming commonplace. Creating statistical models is much more useful and applicable to 99% of the businesses that have a need for analytics, but it seems to be overshadowed by the AI hype.
AI and machine learning have no business being mentioned in the same sentence. The AI hype is pushed by marketers and journalists because it gets clicks and invites the futurist crowd to chime in. We're decades away from anything that will resemble an AI system and any talk of it now is a waste of time.
Machine Learning has made amazing strides in the past decade and will change all aspects of our lives. There will be a ton of startups in the next decade using speech recognition, image recognition, natural language processing and etc to automate all of our repetitive tasks. If hardware keeps up we'll probably see a boom in robotics in the next 5-15 years that will allow us to automate most manufacturing and food production.
> We're decades away from anything that will resemble an AI system
I use my Google Home Mini every day and it's just like the computer in Star Trek. If you could show it to someone from the early 90s they would certainly say it resembles an AI system.
It's not, it's a toolchain that has voice recognition, a NLP parser, and a big model to give you answers back that fails for anything remotely like a complex sentence. It's decent, but nothing like an intelligent being that can answer all types of questions like in most SF stories.
This is just pushing the meaning of "artificial intelligence" farther up the chain because we understand it better. If you told someone in 1950 computers would be able to win 100 times out of 100 against the best humans in the world, they would call that artificial intelligence. Now we just say it's a MCTS system based on heavily tuned heuristics.
I have travelled here from the early 90s and I gotta say it seems more like a parlor trick (voice recognition plus a much larger, more sophisticated, networked Eliza) than true AI. But that's the AI problem in a nutshell: Once we've figured out how to do something it no longer seems like an AI problem.
The computer in Star Trek is just a smart speaker. Lt. Cmdr. Data is AI.
Sure there will be a (ton??) of startups using these things, but my point is that it will not be nearly as prevalent as general statistics and predictive modeling. Which one do you think will be more common:
1) companies using speech recognition, NLP, Image recognition, etc.
2) companies using R/Python to help guide their business decisions through predictive models / simpler statistics?
I believe the demand for companies to actually use the fancier parts of machine learning are going to be much fewer than people think. Number 2, on the other hand, is much more generalizable, and is having a much larger effect on the 'average' business than NLP and image recognition.
Operations research and business analytics will obviously continue to be important but we'll also start seeing machine learning replacing a lot of labor and augmenting a large portion of our daily tasks. It will change how we interact with computers.
I used to work at an image recognition API startup and went through an AI incubator with my own company so I got to see the huge range of applications of this technology. At the incubator we had a company automating customer support (acquired by google), another one automating appointment booking and scheduling, sales call analytics using speech recognition, automatic reading comprehension quizzes for K-12, deep learning for radiology, patient tracking/monitoring at retirement homes and hospitals, imitation learning based robotics, deep learning based hedge fund, amazon go like vending machines, fashion analytics, social media analytics/engagement for brands.
This is just semantics. What you're referring to as AI is often given the name AGI (Artificial General Intelligence), while many (most?) would agree that AI includes nowadays common technologies such as maps pathfinding and grammar checking.
I've been doing machine learning for almost 10 years now and can tell you from experience that when a layman hears AI they think AGI, a magic pixie dust that can solve any problem that they throw it at.
AI is a branch of computer science, but outside of A* search it's practically all machine learning.
What are some examples of companies that hinged on machine learning?
DATA is crucial, but that's very different than "sophistiated machine learning models". For example, do Uber and Lyft need machine learning?
As far as I can tell, they need data about drivers, users, ride completion, maps, and a lot of people doing simple statistics on that data and improving the product. Machine learning might give them a few percentage points in some specific subsystem, but it isn't going to make or break the product as a whole. It's not "table stakes".
Pick another industry and do the same thought experiment. What are some other breakout companies? Did snapchat succeed (in its day) because of machine learning? What about AirBNB? I think they are optimizing the product with "data science", not machine learning. Certainly not deep learning.
Machine learning finally came of age in the 2010s and is now table stakes for every tech company, large and small. Accumulating a data asset around your product and service and using sophisticated machine learning models to personalize and improve your product is not a nice to have.
You would be astonished at some of the non-public (not that they are secret) machine learning things that Uber does that you wouldn't even be aware of.
My favorite is that they do pothole detection, prediction and routing around based on what they classified based on driver accelerometer data. They've also reduced traffic incidents with insights from the same dataset that drove product changes.
That's interesting, and in line with what I perceived as Uber's extreme focus on operations.
To me, the accelerometer DATA is the new and important thing -- it's the thing that taxi companies don't have.
Whether the machine learning model is "sophisticated" doesn't seem critical on the face of it.
I guess it depends whether there are a lot of non-pothole events that look like pothole events to "naive" signal processing. Do you need deep learning for this problem? I don't know but I would be skeptical without a citation.
But I am interested in more info and examples. If someone can give a bunch of examples I would adjust my view.
This is just nitpicking phrases at this point, a popular past time for us nerds, so I get it but it misses the point. "Sophisticated" is enough of a suitcase term that it's effectively worthless.
More importantly it's worth understanding that ML allows us to do things we couldn't do before, because it's cheaper and scales better than previous optimization methods, when you have the data.
So yes, the data infrastructure is the critical part, however the ML pipelines and Data Science work is what gives the massive multiplier at a cost/scale you can't get with traditional optimization methods.
Data is obviously crucial, but it's useless if it's just sitting on hard drive. Machine learning allows them to squeeze out as much juice from it as possible, and a few % point lift makes a huge difference at scale, especially in high volume businesses with low margins per transaction.
Machine Learning is a technology the same way blockchain is. Customers buy products and solutions, not your tech stack so it doesn't make sense for any company to sell themselves as a machine learning company, unless you're selling APIs.
Alexa / Siri / Google Assistant would not be possible without machine learning. Same goes for Snapchat's filters and any other form of AR. Google's Pixel phones wouldn't have such a great camera without computational photography / machine learning.
Those of us doing it mostly roll our eyes at how VCs and (especially) marketers describe it, so I agree with you, but the "is it ML or stats?" conversation has been going nowhere for as long as this round of AI hype has been here.
That said, uber and lyft certainly benefit from much more interesting ML and stats than just "simple statistics." For example, setting prices and incentives to properly balance the driver side against the rider side of the marketplace.
Yeah I agree the argument is mostly about terminology. Anyway you slice it you have a bunch of data and you have a team that's improving the product with the data. That is for sure "table stakes".
And yes I probably should have said "data science" or "statistics" rather than "simple statistics", definitely for Uber and Lyft (less so for smaller companies).
I worked on a data science team 10 years ago when it was called "statistics". I even remember a 2009 NYTimes article that talked about data science without using the word "data science", because it hadn't come into use yet.
If people want to redefine "machine learning" to mean "data science", OK fine. I guess that's what's happening now. The term "data science" already annoyed some people, but I guess now it's "machine learning" and "deep learning".
> For example, do Uber and Lyft need machine learning?
Anyone doing large scale ETA prediction and routing these days is using ML models at various phases in the pipeline, from GPS noise filtering to road network data generation. You might get past the early stages of a rideshare or delivery service with more traditional naive models, but there are still significant efficiency gains to be made in even the most cutting edge systems today. For maps and navigation, this is especially true when you go global and need models that adapt to subtle cultural and behavioral changes across markets.
Strongly dis-disagree. Google's AlphaZero and AlphaGo look awfully fearsome. Generalizing from that to other expert endeavors seems inevitable rather than some sort of mania. Moreover, saying that chess and go aren't benchmark hard problems strikes me as a technical No True Scotsman because they were always seen as that before Alpha obliterated them.
Chess and Go are constrained problem spaces. The space may be huge, but it's still finite. The win condition is unambiguous. The rules can fit on a postcard. There are only a finite number of possible board configurations.
Real-world win conditions tend to be more complex. For non-general AI tasks, this is much less of a problem. "Identify hot dog vs. not-hot-dog accurately 99% of the time" is measurable and verifiable. But a simple goal for a complex general-AI task is likely to build a Paperclip Optimizer. I suspect we don't even have the right language to build a good win condition for a lot of real world problems-- we may end up ignoring "insignificant" factors only to discover they're huge over tine or at scale.
Games also tend to provide near-perfect or perfect information. At a minimum, there's constrained state transitions. I can't pull out my Oyster card in the middle of a poker game, add it to my hand, and declare it's actually a four of clubs. Real problems can have surprise off-the-wall, or outright externally triggered transitions, that completely throw the model off.
"AI, build me a market-beating stock portfolio to cash out 2040-01-01" is a lot harder than "AI, checkmate this king."
What an interesting thing to say when practically all user interactions in apps and devices are now driven by data science and machine learning. In fact you’d have to work real hard to use a device or service that hasn’t been tuned or driven by ml.
In medicine, the 2010s may be remember as a period of explosive growth of engineered therapeutics. This is driven primarily by better understanding of diseases (and their molecular targets) and also by an expanding reach of bio-engineering platforms. Monoclonal antibody approvals especially have accelerated briskly for inflammatory conditions (there are 13 or more for the skin condition psoriasis alone) and perhaps more famously redefined many cancer treatment regimens with checkpoint inhibitors. Chimeric antigen receptor T cell (CAR T cells) are (very) expensive ex vivo gene-edited cell-based treatments, and other platforms for gene editing with are nearing or entering trials. This trend does not appear to be decelerating or plateauing.
I think one of the big tech developments (hinted at in the article but not called out) is on demand video streaming and what that has done to how we consume media. It has changed the type of things we watch.
Not on-demand, rather, channel-ADAPTIVE video streaming as embodied in the ubiquitous 3GPP2 DASH protocols, and used by html 5, took the world by storm 2010-2014, when YouTube and Netflix dropped Silverlight to use the newer, open standard.
Adaptive streaming originated as a Microsoft pilot project for the Beijing 2008 olympics. Microsoft's prototype had so many hacks and so little documentation that they publically admitted to giving up on standardizing it and threw their support behind mpeg DASH in ~2011.
Also, YouTube reached profitability in 2015 when it was ported to Google's lowest-TCO infrastructure. YouTube monetization practically invented the 10 minute video and made millionaires out of many online personalities.
On demand video streaming in the 90s was completely different.
I was talking about the effect on our culture, and the shift that has occurred in the last decade has changed how we consume media in a way that did not happen with what we had in the 90s.
All that happened is that media companies came to their senses and figured out that renting out the same content over and over again is more lucrative than selling it on physical media. It also was the one chance they had to put the piracy genie back in the box: a single SD card will now hold so much data that you could store all the music and movies you'd want to consume in a lifetime on one or two of them.
No, bandwidth and codec improvements made it able to compete with cable TV in terms of quality, while destroying cable in terms of user experience.
I remember the 90s video streaming, with Real Player and the like.... it was not a viable platform for watching tv quality video... the quality was awful, and it would take minutes before you could start playing a thirty second video.
> a single SD card will now hold so much data that you could store all the music and movies you'd want to consume in a lifetime on one or two of them.
You can store a whopping five 4K equivalent movies on your 512gb SD card. You can shoehorn 10 to 20 movies on there at approximate Blu-ray quality. If you're willing to sink to Netflix equivalent pseudo HD quality you can get 100.
The piracy genie is still being very strongly held back by the massive size of the files involved. The average consumer isn't going to buy and manage ten hard drives to store everything. They're not going to spend the thousand dollars on the drives, and they're also not spending a couple thousand dollars in blown bandwidth caps to download it all. There is no scenario where the average consumer has any interest in that scenario, they'll always choose Netflix + Disney + Prime for sub $30 / month. It's a no-brainer for them.
Also, in the not very distant future those lower quality movies - at eg 5gb file sizes - will noticeably look like trash on modern TVs.
I was just thinking about the whole "what happened in the last decade". While the 90s ended with the rise of the web, and the 00s saw the rise of what we can do with the web, the 10s felt like an experiment of what NOT to do with the web.
I am surprised with no mention of Smartphone. Which is arguably the most important innovation in modern history.
2010s, Smartphone ( iPhone 3GS at the time ) went from niche to 4B users ( iOS, Androids and KaiOS ), that is nearly every person on earth above age 14 in developed countries. I dont think there has ever been a product or technology innovation as important that spread faster than Smartphone. And it changes everyone's life. The post mentioned of Google, Facebook and Amazon empire, all partly grows to this point because of Smartphones. Technology companies together now worth close to 10 Trillions. The whole manufacturing supply chain exists and became huge in Shenzhen because of Smartphone. It was the reason why TSMC managed to catch up to Intel in both capacity and leading edge node. It was the reason why we went from 3G to 5G in mere 10 years because of all the investment kept pouring it. It was the reason why everyone went on to the Internet and had Internet economy. It brought a handheld PC and Internet to a much wider audience.
I would even argue it was the Smartphone innovation that saved us from the post 2008 Financial Crisis doom as it created so much wealth, innovation and opportunities.
And the 2nd most important thing not mentioned in the article. ( May be it is not important to others... )
We lost the man who bought us the Smartphone era; Steve Jobs.
> subscriptions better align the interests of the users and the developers of mobile and web applications and avoid many of the negative aspects of the free/ad supported business model
The most significant negative aspect that subscriptions avoid is ephemeral spambot accounts. If a user account costs money, there's way more disincentive for behavior that will get that account banned.
It's sad, the point about the bay area. We could have done a lot more but nobody wants to make any sacrifices. I hope in the next decade the state will take a larger role and force local communities to drastically increase housing, density, and transit rights of way.
I've lived in the Bay Area for most of my life and I have zero confidence the situation will improve. It would have happened by now if it was going to.
The transit situation in particular will not improve because people prefer cars. People have little experience with public transit, and the experience they do have was bad, so they don't see themselves using it on a regular basis. They want to live in suburbs and drive cars, and they will try very hard to prevent anyone from increasing the density of housing or transit anywhere near their houses.
Those people vote, so a state government that tries to push for more public transit and housing density would not do well in the next election.
Houston doesn’t have a housing problem, nor particularly good transit. It also doesn’t have zoning and a local government that pulverized developers by charging them fees for things already paid for by property taxes like building schools or forcing them to have “low income” units which end up making housing more expensive. Texas also doesn’t have rent control, nor excessively reactionary building height restrictions, nor does Texas have Prop 13 incentives that reduce housing market liquidity.
“Transit” and “Density” isn’t the problem in the Bay Area — it’s that it’s excessively punitive to attempt to build anything there. If you were to be able to afford available land, cities such as Mountain View want to milk developers to pay for things that the city should be paying for themselves out of normal tax revenue. I remember when Steve Jobs wanted to build Apple Park and some fool on city council wanted Apple to provide WiFi for the city for free: Jobs said that it isn’t Apple’s job to provide city services — Apple pays their property taxes, if Cupertino wanted free WiFi, then Cupertino should do it. There are also cases where if a developer wanted to build some houses; they’d also have to give the city a free park and dedicate a certain number of houses for “low income.” That isn’t a developer’s problem. If the city wants low income housing, the city should use some of its own land and budget to build it. You don’t have that sort of extortion happening in places like Houston — and coincidentally, you don’t have affordability problems in Houston either. A developer also is shy about investing hundreds of millions into a project only to get it derailed by NIMBYs or other activists or worse, be a single election away from having severe rent control or confiscatory tax policies enacted that would destroy any hope of a profit from the substantial risk of property development.
I don’t disagree with allowing high density where it is appropriate, but density isn’t the issue, it’s the extreme high costs, both political and monetary of doing business in the region. There is plenty of land, but given than almost every empty cow pasture ends up getting claimed as “open space,” makes it really hard to build anything. Case in point: the “preservation” of North Coyote Valley. I appreciate open space, however, they literally preserved 900 acres of flat farmland to be used for hiking despite the region being chock full of thousands of acres of open space already. You can’t claim a housing shortage while simultaneously taking away 900 prime acres that would be perfect for housing developments. Clearly there isn’t a housing problem when so much easily buildable land can be sequestered away so vegan hipsters can have yet another thousand acres to roam with their rescue dogs. The Bay Area has no shortage of open space, yet they keep adding more to it while people literally sleep in RVs in El Camino and a young family has no hope of ever owning a house in the area.
> 1/ The emergence of the big four web/mobile monopolies; Apple, Google, Amazon, and Facebook
I'm not sure you could class Apple alongside some of the others for monopoly power either. They're still less popular than Microsoft for desktop operating systems, Google for mobile ones and virtually every other major service in their other areas of business.
Apple is a very profitable business, but they're arguably more of a luxury goods maker than a monopoly right now, especially outside of tech circles.
> 2/ The massive experiment in using capital as a moat to build startups into sustainable businesses has now played out and we can call it a failure for the most part.
This is an interesting point, and I definitely wonder how it'll affect the tech industry in future.
> 4/ Subscriptions became the second scaled business model for web and mobile businesses, following advertising which emerged at scale in the previous decade.
Have they? They've done really well in some markets, but also done really poorly in others. People are definitely interested in them for music, films, TV, etc, and people + companies are interested in for them for certain products and services, but there are still many areas where they haven't done nearly as well as expected.
For instance, subscriptions still haven't worked in the media industry for news/journalism, as much as certain publications are hoping they would. They also haven't done too hot in the games industry either, if Google Stadia's performance is to be believed. Jury's still out for YouTube/Twitch creators too, with Patreon type services being the de facto monetisation method right now. Free with ads has usually beat out subscriptions when in competition with it.
> 7/ Technology inserted itself right in the middle of society this decade.
It sure did, though it's more like people took notice this decade, since the political tides went one way rather than the other.
> And the stagnation of earning power in the lower and middle class is absolutely the result of technology automation, a trend that will only accelerate in coming years.
It's effects on politics will accelerate too, as shown by this decade's move towards more extreme political parties and policies worldwide. This might not end well, especially with environmental issues caused by global warming/climate change at the same time.
> Have they? They've done really well in some markets, but also done really poorly in others. People are definitely interested in them for music, films, TV, etc, and people + companies are interested in for them for certain products and services, but there are still many areas where they haven't done nearly as well as expected.
A pattern I identified was that subscriptions work really well when property rights are strong and enforced strongly (TV shows, movies, music, books etc). It doesn't really work well when property rights can be bypassed easily or have a convenient alternative (you want to read NYT story without a subscription? read that story on some other outlet).
Very few new capabilities for mankind were discovered in the last 10Y mainly CRISPR and CAR-T therapy and two level neural networks (Google translate, alphago). Lithium-Ion batteries got 50% stronger and 6x cheaper (see photo in https://drive.google.com/open?id=1FwCzOaig88r79FC7wM7RqtZ72c...). Most scientists focused on confirming what was already there (gravity waves, higgs boson, water on moon, water on Mars, etc ). Carbon fiber increased passenger air travel efficiency and became commonplace in bicycle frame design. The 2010s were not a creative decade ...
"...the fact that the tech sector has such a powerful role means that it will be highly regulated by society."
I think this reality will arrive hard and fast in the 2020's. The breadth and depth of regulation globally will depend on election outcomes. But it will happen either way. GDPR and CCPA are just a taste of what's to come. We'll see regulation in several key areas including:
- Social media's goal of increased engagement vs mental health.
- Cross border cybersecurity.
- More jurisdictions adopting GDPR/CCPA-like privacy laws.
- Cloud providers (consumer in particular) and vendor lock-in vs portability.
Some is already happening, but expect to see a ramp up. Don't interpret this as me being pro/anti regulation. Just a reality that's coming IMO.
In my opinion, machine learning is going to replace all of us, now or in the future. We don't know but it will eventually happen, not in this decade...
>the US becomes increasingly internally focused and isolationist in its world view.
There is a lot of industry built around supplying the half a trillion a year military. They are well practiced in resisting reductions in funding. If the US becomes isolationist does that military might get turned inward.
