• Let's Know Things

  • 著者： Colin Wright
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Let's Know Things

著者： Colin Wright
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  • A calm, non-shouty, non-polemical, weekly news analysis podcast for folks of all stripes and leanings who want to know more about what's happening in the world around them. Hosted by analytic journalist Colin Wright since 2016.

    letsknowthings.substack.com
    Colin Wright
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A calm, non-shouty, non-polemical, weekly news analysis podcast for folks of all stripes and leanings who want to know more about what's happening in the world around them. Hosted by analytic journalist Colin Wright since 2016.

letsknowthings.substack.com
Colin Wright
政治・政府
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  • Bluesky

    Bluesky
    2024/11/26
    This week we talk about Mastodon, Threads, and twttr.We also discuss social platform clones, user exoduses, and communication fractures.Recommended Book: Invisible Rulers by Renée DiRestaTranscriptIn 2006, a prototype of a software project called twttr, t-w-t-t-r, was developed by Jack Dorsey and Florian Weber, that name used because the full twitter.com domain, the word with all its vowels, was already owned and in use, and because the vowel-less version of the word only had five letters, which aligned with SMS short codes for the US, which were basically shorthand versions of telephone numbers that were used in lieu of such numbers by mobile network operators at the time.Going without vowels was also super trendy in Silicon Valley back then, due to the flourishing of online success stories like Flickr.Twitter, in that early incarnation, was meant to be a one-to-many SMS service, which means sending text messages from one phone to multiple phones, rather than one to one, which was the default.This early prototype was used internally at Odeo, which was an early-2000s web-based media directory, founded by some of the same people who eventually founded Twitter as a company, and random fun fact, Kevin Systrom who eventually cofounded Instagram, was an intern at Odeo one summer, back in 2005, before the company was sold in 2007.Twitter was spun out as its own company the same year Odeo was sold, and by 2009 it had become the hot new thing in the burgeoning world of the web—folks were sending tens of thousands of tweets, messages that were shared one-to-many, though online, on the web, instead of via SMS, by the end of 2007, and that was up to 50 million a day by early 2010.The whole concept of Twitter, then, from its name, which was initially predicated on SMS short codes, to its famous 140-character limit, was based on earlier technology, that of text messages, and that sort of limitation—which has in the years since been messed with a bit, the company slowly adding more capabilities, including the sharing of images and videos and other media types—but those limitations have in part helped define this platform from its peers, as while Facebook expanded and expanded and expanded to gobble up all of its general-purpose social networking competitors, Instagram dominated the photo-posting space, and YouTube has locked down the long-form video world for more than a decade, twitter held its own as a less-sprawling, less successful by most metrics, but arguably more influential network because it was a place that was optimized for concision and up-to-the-minute conversation, as opposed to every other possible thing it could be.This meant that while it didn’t have the same billion-plus user base, and it didn’t have the ever-growing ad-revenue that Meta’s platforms could claim, it was almost always the more culturally relevant network, its users sharing more up-to-date information, its communities generating more memes, which were then spread to other networks days or weeks later, and it became a hotbed of debate and exclusive information from journalists, politicians, and business owners.A lot changed when Tesla and SpaceX owner Elon Musk bought the network in October of 2022, changing the name to X in mid-2023, and pivoting the company dramatically in basically every way: removing a lot of those earlier limitations, cutting the number of employees by something like 80%, and losing a lot of advertisers because of his many ideological statements and political stances—including his backing of former president and now president-elect Donald Trump in the 2024 election.What I’d like to talk about today are the twitter clones that have popped up in recent years, and one in particular that, despite its still-small size and arguable underdog status, is being heralded as the possible successor of Twitter—in that original, influential and scrappy sense—and what makes this network, Bluesky, different from other would-be successors in this space.—The leadership at X, including owner Musk, recently promoted a new feature on the app that refocuses attention away from buttons like likes and shares in favor of views—a metric of engagement that some analysts have claimed is meant to conceal the fact that the network is seeing a lot less actual, human engagement, and because it feeds people posts it wants them to see, this change allows them to artificially inflate the seeming activity on these posts for advertising purposes: they can say, hey look how much attention these posts are getting, please buy some ads, and that allows them to charge a higher price than if they were using those more conventional engagement metrics, which are apparently collapsing.As a company, X has been hemorrhaging money since Musk took over, its ad revenue, which makes up the majority of its income, dropping by nearly half from 2022 to 2023, and it lost another 24% from the first half of 2023 to the first half of 2024.One ...
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    21 分
  • AI Scaling Walls

    AI Scaling Walls
    2024/11/19
    This week we talk about neural networks, AGI, and scaling laws.We also discuss training data, user acquisition, and energy consumption.Recommended Book: Through the Grapevine by Taylor N. Carlson TranscriptDepending on whose numbers you use, and which industries and types of investment those numbers include, the global AI industry—that is, the industry focused on producing and selling artificial intelligence-based tools—is valued at something like a fifth to a quarter of a trillion dollars, as of halfway through 2024, and is expected to grow to several times that over the next handful of years, that estimate ranging from two or three times, to upward of ten or twenty-times the current value—again, depending on what numbers you track and how you extrapolate outward from those numbers.That existing valuation, and that projected (or in some cases, hoped-for growth) is predicated in part on the explosive success of this industry, already.It went from around $10 billion in global annual revenue in 2018 to nearly $100 billion in global revenue in 2024, and the big players in this space—among them OpenAI, which kicked off the most recent AI-related race, the one focusing on large-language models, or LLMs, when it released its ChatGPT tool at the tail-end of 2022—have been attracting customers at a remarkable rate, OpenAI hitting a million users in just five days, and pulling in more than 100 million monthly users by early 2023; a rate of customer acquisition that broke all sorts of records.This industry’s compound annual growth rate is approaching 40%, and is expected to maintain a rate of something like 37% through 2030, which basically means it has a highly desirable rate of return on investment, especially compared to other potential investment targets.And the market itself, separate from the income derived from that market, is expected to grow astonishingly fast due to the wide variety of applications that’re being found for AI tools; that market expanded by something like 50% year over year for the past five years, and is anticipated to continue growing by about 25% for at least the next several years, as more entities incorporate these tools into their setups, and as more, and more powerful tools are developed.All of which paints a pretty flowery picture for AI-based tools, which justifies, in the minds of some analysts, at least, the high valuations many AI companies are receiving: just like many other types of tech companies, like social networks, crypto startups, and until recently at least, metaverse-oriented entities, AI companies are valued primarily based on their future potential outcomes, not what they’re doing today.So while many such companies are already showing impressive numbers, their numbers five and ten years from now could be even higher, perhaps ridiculously so, if some predictions about their utility and use come to fruition, and that’s a big part of why their valuations are so astronomical compared to their current performance metrics.The idea, then, is that basically every company on the planet, not to mention governments and militaries and other agencies and organizations will be able to amp-up their offerings, and deploy entirely new ones, saving all kinds of money while producing more of whatever it is they produce, by using these AI tools. And that could mean this becomes the industry to replace all other industries, or bare-minimum upon which all other industries become reliant; a bit like power companies, or increasingly, those that build and operate data centers.There’s a burgeoning counter-narrative to this narrative, though, that suggests we might soon run into a wall with all of this, and that, consequently, some of these expectations, and thus, these future-facing valuations, might not be as solid as many players in this space hope or expect.And that’s what I’d like to talk about today: AI scaling walls—what they are, and what they might mean for this industry, and all those other industries and entities that it touches.—In the world of artificial intelligence, artificial general intelligence, or AGI, is considered by many to be the ultimate end-goal of all the investment and application in and of these systems that we’re doing today.The specifics of what AGI means varies based on who you talk to, but the idea is that an artificial general intelligence would be “generally” smart and capable in the same, or in a similar way, to human beings: not just great at doing math and not just great at folding proteins, or folding clothes, but pretty solid at most things, and trainable to be decent, or better than decent at potentially everything.If you could develop such a model, that would allow you, in theory, to push humans out of the loop for just about every job: an AI bot could work the cash register at the grocery store, could drive all the taxis, and could do all of our astronomy research, to name just a few of the great many jobs these ...
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    22 分
  • Online Tutoring

    Online Tutoring
    2024/11/12
    This week we talk about the Double Reduction Policy, gaokao, and Chegg.We also discuss GPTs, cheating, and disruption.Recommended Book: Autocracy, Inc by Anne ApplebaumTranscriptIn July of 2021, the Chinese government implemented a new education rule called the Double Reduction Policy.This Policy was meant, among other things, to reduce the stress students in the country felt related to their educational attainment, while also imposing sterner regulations on businesses operating in education and education-adjacent industries.Chinese students spend a lot of time studying—nearly 10 hours per day for kids ages 12-14—and the average weekly study time for students is tallied at 55 hours, which is substantially higher than in most other countries, and quite a lot higher than the international average of 45 hours per week.This fixation on education is partly cultural, but it’s also partly the result of China’s education system, which has long served to train children to take very high-stakes tests, those tests then determining what sorts of educational and, ultimately, employment futures they can expect. These tests are the pathway to a better life, essentially, so the kids face a whole lot of pressure from society and their families to do well, because if they don’t, they’ve sentenced themselves to low-paying jobs and concomitantly low-status lives; it’s a fairly brutal setup, looked at from elsewhere around the world, but it’s something that’s kind of taken for granted in modern China.On top of all that in-class schoolwork, there’s abundant homework, and that’s led to a thriving private tutoring industry. Families invest heavily in ensuring their kids have a leg-up over everyone else, and that often means paying people to prepare them for those tests, even beyond school hours and well into the weekend.Because of all this, kids in China suffer abnormally high levels of physical and mental health issues, many of them directly linked to stress, including a chronic lack of sleep, high levels of anxiety, rampant obesity and everything that comes with that, and high levels of suicide, as well; suicide is actually the most common cause of death amongst Chinese teenagers, and the majority of these suicides occur in the lead-up to the gaokao, or National College Entrance Exam, which is the biggest of big important exams that determine how teens will be economically and socially sorted basically for the rest of their lives.This recent Double Reduction Policy, then, was intended to help temper some of those negative, education-related consequences, reducing the volume of homework kids had to tackle each week, freeing up time for sleep and relaxation, while also putting a cap on the ability of private tutoring companies to influence parents into paying for a bunch of tutoring services; something they’d long done via finger-wagging marketing messages, shaming parents who failed to invest heavily in their child’s educational future, making them feel like they aren’t being good parents because they’re not spending enough on these offerings.This policy pursued these ends, first, by putting a cap on how much homework could be sent home with students, limiting it to 60 minutes for youngsters, and 90 minutes for middle schoolers.It also provided resources and rules for non-homework-related after-school services, did away with bad rankings due to poor test performance that might stigmatize students in the future, and killed off some of those fear-inducing, ever-so-important exams altogether.It also provided some new resources and frameworks for pilot programs that could help their school system evolve in the future, allowing them to try some new things, which could, in theory, then be disseminated to the nation’s larger network of schools if these experiments go well.And then on the tutoring front, they went nuclear on those private tutoring businesses that were shaming parents into paying large sums of money to train their children beyond school hours.The government instituted a new system of regulators for this industry, ceased offering new business licenses for tutoring companies, and forced all existing for-profit businesses in this space to become non-profits.This market was worth about $100 billion when this new policy came into effect, which is a simply staggering sum, but the government basically said you’re not businesses anymore, you can’t operate if you try to make a profit.This is just one of many industries the current Chinese leadership has clamped-down on over the past handful of years, often on cultural grounds, as was the case with limiting the amount of time children can play video games each day. But like that video game ban, which has apparently shown mixed results, the tutoring ban seems to have led to the creation of a flourishing black market for tutoring services, forcing these sorts of business dealings underground, and thus increasing the fee parents pay for them ...
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    21 分

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