Let's Know Things

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Let's Know Things

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

  1. Assad Overthrown

    2 DAYS AGO

    Assad Overthrown

    This week we talk about coups, the Arab Spring, and Bashar al-Assad. We also discuss militias, Al Qaeda, and Iran. Recommended Book: The Algebraist by Iain M. Banks Transcript In the early 2010s, a series of uprisings against unpopular, authoritarian governments spread across the Middle East—a wave of action that became known as the Arab Spring. Tunisia was where it started, a man setting himself on fire in protest against the nation’s brazenly corrupt government and all that he’d suffered under that government, and the spreading of this final gesture on social media, which was burgeoning at the time, amplified by the still relatively newfound availability and popularity of smartphones, the mobile internet, and the common capacity to share images and videos of things as they happen to folks around the world via social media, led to a bunch of protests and riots and uprisings in Jordan, Egypt, Yemen, and Algeria, initially, before then spreading to other, mostly Arab majority, mostly authoritarian-led nations. The impact of this cascade of unrest in this region was immediately felt; within just two years, by early 2012, those ruling Tunisia, Egypt, Libya, and Yemen had been toppled, there were attempts to topple the Bahraini and Syrian governments, there were massive protests in Iraq, Jordan, Kuwait, Morocco, Oman, Algeria, and Sudan, and relatively minor protests, which were still meaningful because of the potential punishments for folks who rocked the boat in these countries, smaller protests erupted in Djibouti, Western Sahara, Saudi Arabia, Palestine, and Mauritania. Several rulers and their ruling parties committed to stepping down soon, or to not run for reelection—some of them actually stuck with that commitment, though others rode out this period of tumult and then quietly backtracked. Some nations saw long-lasting periods of unrest following this eruption; Jordan had trouble keeping a government in office for years, for instance, while Yemen overthrew its government in 2012 and 2015, and that spun-out into a civil war between the official government and the Iran-backed Houthis, which continues today, gumming up the Red Sea and significantly disrupting global shipping as a consequence. What I’d like to talk about today, though, is another seriously disruptive sequence of events that have shaped the region, and a lot of things globally, as well, since the first sparks of what became the Arab Spring—namely, the Syrian Civil war—and some movement we’ve seen in this conflict over the past week that could result in a dramatically new state of affairs across the region. — In 1963, inspired by their brethren’s successful coup in nearby Iraq, the military wing of the Arab nationalist Ba’ath party of Syria launched a coup against the country’s post-colonial democratic government, installing in its stead a totalitarian party-run government. One of the leaders of this coup, Hafez al-Assad, became the country’s president in 1971, which basically meant he was the all-powerful leader of a military dictatorship, and he used those powers to even further consolidate his influence over the mechanisms of state, which meant he also had the ability to name his own successor. He initially planned to install his brother as leader when he stepped down or died, but that brother attempted to overthrow him when he was ill in 1983 and 1984, so when he got better, he exiled said brother and chose his eldest son, Bassel al-Assad, instead. Bassel died in a car accident in 1994, though, so Hafez was left with his third choice, Bashar al-Assad, which wasn’t a popular choice, in part because it was considered not ideal for him to choose a family member, rather than someone else from the leading party, but also because Bashar had no political experience at the time, so this was straight-up nepotism: the only reason he was selected was that he was family. In mid-2000, Hafez died, and Bashar stepped into the role of president. The next few years were tumultuous for the new leader, who faced heightened calls for more transparency in the government, and a return to democracy, or some form of it at least, in Syria. This, added to Bashar’s lack of influence with his fellow party members, led to a wave of retirements and purgings amongst the government and military higher-ups—those veteran politicians and generals replaced by loyalists with less experience and credibility. He then made a series of economic decisions that were really good for the Assad family and their allies, but really bad for pretty much everyone else in the country, which made him and his government even less popular with much of the Syrian population, even amongst those who formerly supported his ascension and ambitions. All of this pushback from the people nudged Bashar al-Assad into implementing an increasingly stern police state, which pitted various ethnic and religious groups against each other in order to keep them from unifying against the government, and which used terror and repression to slap down or kill anyone who stood up to the abuse. When the Arab Spring, which I mentioned in the intro, rippled across the Arab world beginning in 2011, protestors in Syria were treated horribly by the Assad government—the crackdown incredibly violent and punitive, even compared to that of other repressive, totalitarian governments in the region. This led to more pushback from Syrian citizens, who began to demand, with increasing intensity, that the Assad-run government step down, and that the Ba’athists running the dictatorship be replaced by democratically elected officials. This didn’t go over well with Assad, who launched a campaign of even more brutal, violent crackdowns, mass arrests, and the torture and execution of people who spoke out on this subject—leading to thousands of confirmed deaths, and tens of thousands of people wounded by government forces. This response didn’t go over super well with the people, and these protests and the pushback against them spiraled into a full-on civil uprising later in 2011, a bunch of people leaving the Syrian military to join the rebels, and the country breaking up into pieces, each chunk of land controlled by a different militia, some of these militias working well together, unifying against the government, while others also fought other militias—a remnant of the military government’s efforts to keep their potential opposition fighting each other, rather than them. This conflict was officially declared a civil war by the UN in mid-2012, and the UN and other such organizations have been fretting and speaking out about the human rights violations and other atrocities committed during this conflict ever since, though little has been done by external forces, practically, to end it—instead it’s become one of many proxy conflicts, various sides supported, mostly with weapons and other resources, though sometimes with training, and in rare instances with actual soldiers on the ground, by the US, Turkey, Russia, Iran, the Iran-backed group Hezbollah, Saudi Arabia, Qatar, Britain, France, Israel, and the Netherlands. This conflict has demanded the country’s full attention for more than a decade, then, and it’s had influence even beyond Syria’s borders, as groups like the Islamic State, or ISIS has been able to grow and flourish within Syria, due to all the chaos and lack of stability, refugees from Syria have flooded across borders, fleeing the violence and causing all sorts of unintended disruptions in neighboring and even some further-afield countries where, in some cases, millions of these refugees have had to be taken care of, which in turn has influenced immigration-related politics even as far away as the European Union. Also due to that lack of internal control, crime has flourished in Syria, including drug-related crime. And that’s lets to a huge production and distribution network for an illegal, almost everywhere, amphetamine called Captagon, which is addictive, and the pills often contain dangerous filler chemicals that are cheaper to produce. This has increased drug crime throughout the region, and the Syrian government derives a substantial amount of revenue from these illicit activities—it’s responsible for about 80% of global Captagon production, as of early 2024. All of which brings us to late-2024. By this point, Syria had been broken up into about seven or eight pieces, each controlled by some militia group or government, while other portions—which make up a substantial volume of the country’s total landmass—are considered to be up in the air, no dominant factions able to claim them. Al-Assad’s government has received a fair bit of support, both in terms of resources, and in terms of boots on the ground, from Iran and Russia, over the years, especially in the mid-20-teens. And due in large part to that assistance, his forces were able to retake most of the opposition’s strongholds by late 2018. There was a significant ceasefire at the tail-end of 2019, which lasted until March of 2020. This ceasefire stemmed from a successful operation launched by the Syrian government and its allies, especially Iran, Russia, and Hezbollah, against the main opposition and some of their allies—basically a group of different rebel factions that were working together against Assad, and this included groups backed by the Turkish government. On March 5, 2020, Turkish President Erdogan and Russian President Putin, which were backing opposite sides of this portion of the Syrian civil war, agreed on a ceasefire that began the following day, which among other things included a safety corridor along a major highway, separating the groups from each other, that corridor patrolled by soldiers from Turkey and Russia. This served to end most frontline fighting, as these groups didn’t want to start fighting these much larger, more powerful nations—Russia and Turkey—while tr

    20 min
  2. COP 29

    DEC 3

    COP 29

    This week we talk about emissions, carbon credits, and climate reparations. We also discuss Baku, COP meetings, and petrostates. Recommended Book: The Struggle for Taiwan by Sulmaan Wasif Khan Transcript In 2016, a group of 195 nations signed the Paris Agreement under the United Nations Framework Convention on Climate Change, usually just called the Paris Agreement, which was negotiated the previous year, and which, among other things, formalized the idea of attempting to keep the global average temperature from increasing by 1.5 C, which is about 2.7 F, above pre-industrial levels. The really bad stuff, climate-wise, was expected to happen at around 2 degrees C above that pre-industrial level, so the 1.5 degrees cutoff made sense as sort of a breakwater meant to protect humanity and the natural world from the most devastating consequences of human-amplified climate change. This has served decently well as a call-to-arms for renewable energy projects and other efforts meant to reduce greenhouse gas emissions, and many nations have actually made really solid strides in that direction since this agreement was formalized, dramatically truncating their emissions in a variety of ways, while also laying the groundwork for long-term reductions by installing a whole lot of solar and wind, reviving old and building new nuclear power facilities, reinforcing and expanding their grids, including adding all sorts of large-scale battery storage, and figuring out ways to reduce energy consumption, which has allowed for the shut-down of coal and oil plants. Shorter-term solutions, like replacing more polluting and emitting sources of energy, like coal, with gas, have also put a big dent in overall global emissions, especially for entities like the US and Europe; this isn’t ideal as a permanent measure, because there are still a lot of emissions associated with gas, especially its transport, because of leakage, and gas itself, in the atmosphere, has really significant greenhouse properties, but in the short-term this has proven to be one of the most impactful solutions for some nations and large corporations, and it’s increasingly being seen as a transitionary measure, even by those who oppose the use of any fossil fuels long-term. Things have been going decently well, then, even if progress is still far short of where it needs to be for most countries to meet their Paris Agreement commitments, and far slower than many people who are watching this space, and analyzing whether we’ll be able to avoid triggering those much-worse climate outcomes, would prefer. One issue we’re running into, now, is that those original commitments were a little fuzzy, as the phrase “preindustrial period” could mean many different periods, even if it’s commonly assumed to be something like 1850 to 1900, in the lead-up to humanity’s full-on exploitation of fossil fuels and the emergence of what we might call the modern era—society empowered by things like coal and oil and gas, alongside the full deployment of electrical grids. Throughout this period, though, from the mid-19th century to today, the climate has experienced huge swings year to year, and decade to decade. The evidence showing that we humans are throwing natural systems way off their equilibrium are very clear at this point, and it isn’t a question of whether we’re changing the climate—it’s more a question of how much, how quickly, and compared to what; what baseline are we actually using, because even during that commonly used 1850 to 1900 span of time, the climate fluctuated a fair bit, so it’s possible to pick and choose baseline numbers from a range of them depending on what sort of picture you want to paint. Research from the World Meteorological Organization in 2022 found that, as of that year, we were probably already something like 1.15 degrees C above preindustrial levels, but that it was hard to tell because La Niña, a weather phenomenon that arises periodically, alongside its opposite, El Niño, had been cooling things down and dampening the earth-warming impacts of human civilization for about three years. They estimated, taking La Niña’s impact into consideration, that the world would probably bypass that breakwater 1.5 degrees C milestone sometime in the next four years—though this bypassing might be temporary, as global temperatures would increase for a few years because of the emergence of El Niño. Adding to the complexity of this calculation is that aforementioned variability in the climate, region to region, and globally. The WMO estimated that through 2027, the world is likely to fluctuate between 1.1 and 1.8 degrees C above preindustrial levels—and that at that higher range, El Niño might tip things into the especially dangerous 2 degree C territory the Paris Agreement was supposed to help us avoid. By late-2024, it was becoming increasingly obvious that the world had stepped past the 1.5 degrees threshold into unfamiliar climactic terrain. Three of the five leading research groups that keep tabs on this matter have said that in addition to 2024 being the warmest year on record, it will also be the first year we’ve ever surpassed that 1.5 degree level. Notably, simply popping up above 1.5 degrees doesn’t suggest we’re now permanently living in that long worried about climate nightmarish world: there are significant, normal fluctuations in this kind of thing, alongside those associated with the El Niño/La Niña patterns; there are a lot of variables acting upon our climate, in other words, in addition to the human variables that are pushing those averages and fluctuating ranges up, over time. The concern here, though, even if we drop back down below 1.5 degrees C for a while is that this temperature band opens up a whole new spectrum of weather-related consequences, ranging from substantial, persistent, crop-killing, barely survivable heat and drought in some parts of the world, to things like larger, more frequent, and more difficult to predict storm systems, like the ones we’ve already seen in abundance this and last year, but bigger and wilder and in more areas that don’t typically see such storms. What I’d like to talk about today is what happened at a recent climate-policy focused meeting, COP29, and the international response to that meeting. — The United Nations Conference of the Parties of the UN Climate Change Conference, or COP meetings, are held every year in a different host country, and they’re meant to serve as a formal space where governments can present their goals and boast of their climate-related accomplishments. They also serve as a platform for negotiations related to things like emissions standards and goal-setting, like that aforementioned 1.5 degrees C temperature level we’ve been trying to avoid hitting. The most recent of these meetings, COP29, was held in Baku, the capitol of Azerbaijan, in mid- to late-November of 2024. And that location was pretty controversial from the get-go because Azerbaijan is a petro-state: its authoritarian government basically funded and sustained by the sale of oil and gas, all of which flows through a state-owned, corruption-laden, local elite-profiting energy company. This isn’t the first time a full-on petro-state has hosted a COP meeting, as COP28 was held in Dubai, in the UAE, which was also controversial. But this one was seen as a step even further toward what might read as the appropriation or capture of the COP meetings for the benefit of fossil fuel entities, as the meeting was partly hosted by so-called official partners, which were fossil fuel business interests directly owned by the country’s president, while others weren’t directly owned, but were connected to his family’s other businesses, all of them thus linked to both authoritarian corruption, and the wealth associated with fossil fuel focused economics. As a result, there were allegations that this whole meeting was premised on providing a notorious source of greenhouse gas emissions, which has every reason to try to keep those emitting products available for as long as possible, a venue for greenwashing their efforts, while also giving them the power to moderate discussions related to global emissions targets and other climate change-oriented issues; a major conflict of interest, basically. The Azerbaijani president, leading up to the meeting, countered that critiques of his country’s government and human rights record and prominence as a fossil fuel exporter were all part of a smear campaign, and that these unwarranted, preemptive criticisms wouldn’t stop those running COP29 from achieving their goal of helping the world “cope with the negative impacts of climate change.” That statement, too, was criticized, as it implies fossil fuel are more interested in pushing the world to adapt to a climate change and its impacts, rather than attempting to halt the emissions that are causing said climate change; many such companies seem keen to keep pumping oil and burning coal and gas forever, in other words, and their efforts in this regard thus tend to orient around figuring out what the new, warmer, more chaotic world looks like, rather than entertaining the idea of changing their business model in any substantial way. So leading up to this meeting, expectations were low, and by some estimates and according to some analysis, those low expectations were met. Article 6 of the Paris Agreement was a big topic of discussion, for instance, as this article outlines how countries can cooperate with each other to reach their climate targets—and this collaboration is predicated on a carbon credit system. So if County A reduces their emissions by more than the targets set by this group, they can sell the gap, the amount of carbon equivalents not emitted into the atmosphere, to Country B, which failed to reach its targets, but which can bring its emissions into accord by acquiring those

    21 min
  3. Bluesky

    NOV 26

    Bluesky

    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 DiResta Transcript In 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 estimate released in November of 2024 suggests that X may have missed out on nearly $6 billion in lost ad revenue since Musk took over in 2022, mostly because of all the decisions he’s made—including basically going to war with many of the company’s top advertisers, publicly criticizing and threatening them for not paying more and buying more ads—but also his many foot-in-mouth statements and, at times, support of extremist causes and characters. He’s attempted to bring some of those advertisers back, with mixed success, as the ones that have returned after boycotts have usually invested far less than in the past, and most of the ad-buyers that have filled the gaps are paying a lot less per ad unit than before, and are generally of a lower quality: a lot of cheaply products from low-grade Chinese factories, scams of various sorts, and/or products sold by companies that are politically conservative culture-warriors, aimed at the network’s increasingly right-leaning and far-right audience; a bit like what we’ve seen on Fox News over the past decade or so, following waves of sexual assault and other scandals on that network, which led to similar advertiser exoduses. It’s also been estimated that the network lost a substantial portion of its total user-base following Musk’s takeover, including something like a third of all users in the UK and around a fifth in the United States, all just in 2024, up till the month of September. That loss of revenue and users was enough to cause Fidelity, which owns a multi-million-dollar stake in X, to write down the value of its investment by more than 75%; in July of 2024, it estimated the company, which was purchased for about $44 billion by Musk was only worth about $9.4 billion; a substantial loss for them and their investment, but also for all other shareholders. All of which leads up to what happened in the wake of the US’s most recent presidential election, during which Musk shelled out more than $100 million to support Trump’s campaign, while also pulling out all the stops to promote the former president on X—something that many users weren’t too keen on, as the owners of other social networks have been criticized and threatened in the past for showing any hint of political bias in their business decisions or personal life, and this was incredibly overt. This heavy-handed biasing of the network toward Trump, and that very public support of the candidate by X’s owner, sparked a new exodus from the platform, some people simply quitting social media entirely, at least for a while, but others looking for something of the same, and thus checking out the twitter-clones that have popped up over the past handful of years; the majority of which only actually gained real momentum in the wake of Musk’s takeover and rebranding of the network a few years ago. One of those twitter-alternatives, Mastodon, attracted a lot of early attention because of what it offered that twitter, and other mainstream social networks, did not: an open source platform based on the ActivityPub protocol, which means it can connected to other ActivityPub-capable social networks. So in theory at least, you can have a profile on a Mastodon instance—which a self-hosted Mastodon network, a social platform island of sorts that is connected to other such islands, the totality of the social network made up of a huge number of such instances, all interconnected in various ways, and each offering different rules and focuses—you can have that profile on that island function on other networks beyond Mastodon, as well. And that’s interesting because it means your work, your posts and conversations, are all more portable, allowing you to move to different networks if you choose, without losing your history and connections and credibility, because it’s all compatible with other networks. So it’s almost like having a Facebook profile that you can also use on Twitter and Instagram and YouTube, if all those networks played well together and shared information and post types between each other; that’s the promise of a protocol like ActivityPub and a network like Mastodon. Mastodon was made public in 2016 as a nonprofit, has basically the same feature-set as pre-Musk twitter, and while it had already gained a steady stream of users from previous upsets at networks like Twitter, Facebook, and Tumblr, among other more mainstream networks, it attained a huge number of new users in 2022 on the news that Musk would be taking over Twitter, hitting around 2.5 million monthly active users by the end of that year. That number has since dropped to just under a million as of September 2024, suggesting that the initial wave of enthusiasm has crested; though the platform continues to see a lot of support within some online communities, and its interactivity with other networks, including Meta’s Threads, which has also added ActivityPub functionality, means that its numbers will always

    21 min
  4. AI Scaling Walls

    NOV 19

    AI Scaling Walls

    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 Transcript Depending 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 systems could take on, subbing in for human beings who would almost always be more expensive, but who—this AI being a generalist and pretty good at everything—wouldn’t necessarily do any better than these snazzy new AI systems. So AGI is a big deal because of what it would represent in terms of us suddenly having a potentially equivalent intelligence, an equivalent non-human intelligence, to deal with and theorize over, but it would also be a big deal because it could more or less put everyone out of work, which would no doubt be immensely disruptive, but it would also be really, really great for the pocketbooks of all the companies that are currently burdened with all those paychecks they have to sign each month. The general theory of neural network-based AI systems, which basically means software that is based in some way on the neural networks that biological entities, like mice and fruit flies and humans have in our brains and throughout our bodies, is that these networks should continue to scale as the number of factors that go into making them scale: and usually those factors include the size of the model—which in the case of most of these systems means the number of parameters it includes—the size of the dataset it trains on—which is the amount of data, written, visual, audio, and otherwise, that it’s fed as it’s being trained—and the amount of time and resources invested in its training—which is a variable sort of thing, as there are faster and slower methods for training, and there are more efficient ways to train that use less energy—but in general, more time and more resources will equal a more girthy, capable AI system. So scale those things up and you’ll tend to get a bigger, up-scaled AI on the other side, which will tend to be more capable in a variety of ways; this is similar, in a way, to biological neural networks gaining more neurons, more connections between those neurons, and more life experience training those neurons and connections to help us understand the world, and be more capable of operating within it. That’s been the theory for a long while, but the results from recent training sessions seem to be pouring cold water on that assumption, at least a bit, and at least in some circles. One existing scaling concern in this space is that we, as a civilization, will simply run out of novel data to train these things on within a couple of years. The pace at which modern models are being trained is extraordinary, and this is a big part of why the larger players, here, don’t even seriously talk about compensating the people and entities that created the writings and TV shows and music they scrape from the web and other archives of such things to train their systems: they are using basically all of it, and even the smallest payout would represent a significant portion of their total resources and future revenues; this might not be fair or even legal, then, but that’s a necessary sacrifice to build these models, according to the logic of this industry at the moment. The concern that is emerging, here, is that because they’ve already basically scooped up all of the stuff we’ve ever made as a species, we’re on the verge of running out of new stuff, and that means future models won’t have more music and writing and whatnot to use—it’ll have to rely on more of the same, or, and this could be even worse, it’ll have to rely on the increasing volume of AI-generated content for future iterations, which could result in what’s sometimes called a “Habsburg AI,” referring to the consequences of inbreeding over the course of generations: and future models using AI-generated content as their source materials may produce distorted end-products that are less and less useful (and even intelligible) to humans, which in turn will make them less useful overall, despite technically being more powerful. Another concern is related to the issue of physical infrastructure. In short, global data centers, which run the internet, but also AI systems, are already using something like 1.5-2% of all the energy produced, globally, and AI, which use an estimated 33% more power to generate a paragraph of writing or an image, than task-specific software would consume to do the same, is expected to double that figure by 2025, due in part to the energetic costs of training new models, and in part to the cost of delivering results, like those produced by the ChatGPTs of the world, and those increasingly generated in lieu of traditional search results, like by Google’s AI offerings that’re often plastered at the top of their search results pages, these days. There’s a chance that AI could also be used to reduce overall energy consumption in a variety of ways, and to increase the efficiency of energy grids and energy production facilities, by figuring out the optimal locations for solar panels and coming up with new materials that will increase the efficiency of

    22 min
  5. Online Tutoring

    NOV 12

    Online Tutoring

    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 Applebaum Transcript In 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 each month. In late-October of 2024, the Chinese government, while not formally acknowledging any change to this policy, eased pressure on private tutoring services—the regulators in charge of keeping them operating in accordance with nonprofit structures apparently giving them a nudge and a wink, telling them surreptitiously that they’re allowed to expand again—possibly because China has been suffering a wave of economic issues over the past several years, and the truncation of the tutoring industry led to a lot of mass-firings, tens of thousands of people suddenly without jobs, and a substantial drop in tax revenue, as well, as the country’s stock market lost billions of dollars worth of value basically overnight. It’s also worth noting here that China’s youth unemployment rate recently hit 18.8%, which is a bogglingly high number, and something that’s not great for stability, in the sense that a lot of young people, even very well educated young people, can’t find a job, which means they have to occupy themselves with other, perhaps less productive things. But high youth unemployment is also not great for the country’s economic future, as that means these are people who aren’t attaining new skills and experience—and they can’t do that because the companies that might otherwise hire them can’t afford to pay more employees because folks aren’t spending enough on their offerings. So while it was determined that this industry was hurting children and their families who had to pay these near-compulsory tutoring fees, they also seemed to realize that lacking this industry, their unemployment and broader economic woes would be further inflamed—and allowing for this gray area in the rules seems to be an attempt to have the best of both worlds, though it may leave them burdened with the worst of both worlds, as well. What I’d like to talk about today is another facet of the global tutoring industry, and how new technologies seem to be flooding into this zone even more rapidly than in other spaces, killing off some of the biggest players and potentially portending the sort of collapse we might also see in other industries in the coming years. — Chegg, spelled c-h-e-g-g, is a US-based, education-focused tech company that has provided all sorts of learning-related services to customers since 2006. It went public on the NYSE in 2013, and in 2021 it was called the “most valuable edtech company in America” by Forbes, due in part to the boom in long-distance education services in the early days of the Covid-19 pandemic; like Peloton and Zoom, Chegg was considered to be a great investment for a future in which more stuff is done remotely, as seemed likely to be the case for a good long while, considering all the distancing and shut-downs we were doing at the time. In early 2020, before that boom, the company was already reporting that it had 2.9 million subscribers to its Chegg Services offering, which gave users access to all sorts of school-related benefits, including help with homework, access to Q&As with experts, and a huge database of solutions for tests and assignments. The company then released a sort of social-publishing platform called Uversity in mid-2021, giving educators a place to share their own content, and they acquired a language-learning software company called Busuu, which is a bit like Duolingo, that same year for $436 million. In May of 2023, though, the company’s CEO said, on an earnings call, that ChatGPT—the incredibly popular, basically overnight-popular large-language-model-powered AI chatbot created by OpenAI—might hinder Chegg’s near-future growth. The day after that call, Chegg’s stock price dropped by about 48%, cutting the company’s market value nearly in half, and though later that same month he announced that Chegg would partner with OpenAI to launch its own AI platform called Cheggmate, which was launched as a beta in June, by early November the following year, 2024, the company had lost about 99% of its market valuation, dropping from a 2021 high of nearly $100 per share, down to less than $2 per share as of early November. This isn’t a unique story: LLM-based AI tools, those made by OpenAI but also its competitors, including big tech companies like Google and Microsoft, which have really leaned into this seeming transition, have been messing with market valuations left and right, as this collection of tools and technologies have been evolving really fast—a recent five-year plan for Chegg indicated they didn’t believe something like ChatGPT would exist until 2025 at the earliest, for instance, which turned out to be way off—but they’ve also been killing off high-flying company valuations because these sorts of tools are by definition multi-purpose, and a lot of the low-hanging fruit in any industry is basically just providing information that’s already available somewhere in a more intuitive and accessible fas

    21 min
  6. British Coal

    NOV 5

    British Coal

    This week we talk about peat, pig iron, and sulphuric acid. We also discuss the Industrial Revolution, natural gas, and offshore wind turbines. Recommended Book: Deep Utopia by Nick Bostrom Transcript This episode is going live on election day here in the US; and this has been quite a remarkable election season for many reasons, among them that there’s been just a boggling amount of money spent on advertisements and events and other efforts to claim attention and mindshare, and in part because the vitriol and tribalism of the past several elections—an evolved, intensified version of those things—has almost completely dominated all those messages. And as someone who’s based in a swing-state, Wisconsin, I can tell you that it’s been a lot. It’s been a lot everywhere, as US elections also claim more than their fair-share of news reportage in other countries, but in the US, and in the relatively few states that are assumed to be the kingmakers in this election, it’s been just overwhelming for months, for basically a year, actually. So instead of doing anything on the election, or anything overtly political—there’ll no doubt be time for that in the coming weeks, once the dust has settled on all this—let’s talk about coal. And more specifically, British coal. Coal has been used throughout the British Isles for a long time, with early groups burning unrefined lumps of the substance to heat their homes, though generally only when their local, close-enough-to-the-surface-to-be-gathered source for the stuff was pure enough to beat-out other options, like peat and wood, which was seldom the case in most of these areas. It was also used to create lime from limestone, the lime used for construction purposes, to make mortar, and it was used for metal-shaping purposes by blacksmiths. Beyond that, though, it was generally avoided in favor of cleaner-burning options, as coal is often accompanied by sulphur and other such substances, which means when burned in its natural form, it absolutely reeks, and it can make anyone unlucky enough to be caught in the smoke it creates tear-up, because the resulting sulfurous gas would react with their eye-moisture to create sulphuric acid; not pleasant, and even though it was generally better than peat and wood in terms of the energy it contained, it was worse in basically every other way. Earlier groups of people had figured out the same: there were folks in China as early as 1000 BC, for instance, who used these rocks as fuel for copper smelting, and people in these same early-use areas, where coal veins were exploitable, were really leaning into the stuff by the 13th century AD, when Marco Polo visited and remarked that the locals were burning these weird black stones, which granted them wild luxuries, like being able to take “three hot baths a week.” Groups in Roman Britain were also surface mining, using, and trading coal at a fairly reasonable level by around 200 AD, though it was still primarily used to process things like grain, which needed to be dried, and to work with iron—as with those Chinese groups, coal has long been appreciated for its smelting capabilities, because of its high energy density compared to other options. In the British Isles, though, coal was largely imported to major cities by sea, until around the 13th century when the easily accessed deposits were used up, and shaft mining, which granted access to deeper deposits via at times long tunnels that had to be dug and reinforced, was developed and became common, including in areas that hadn’t previously had surface sources that could be exploited. In the 16th century, this and similar innovations led to a reliable enough supply of coal that folks living in the city of London were able to largely replace their wood- and peat-burning infrastructure with coal-burning versions of the same. It’s thought that this transition was partly the consequence of widespread deforestation that resulted from a population boom in the city—more lumber was needed to build more buildings, but they also required more burnable wood fuel—though some historians have argued that what actually pushed coal to the forefront, despite its many downsides compared to wood and peat, is the expansion of iron smelting and the increasing necessity of iron for Britain’s many wars during this period, alongside England’s burgeoning glass-making industry. Both of these manufacturing processes, making iron and glass, required just a silly amount of fuel—making just one ton of the lowest-grade cast iron, so-called pig iron, consumed something like 28 tons of seasoned wood, and glass was similarly wood-hungry. What’s more, that combination of city expansion and the King’s desire to massively build-out his Navy meant timber resources were continuously being strained anywhere industry popped up and flourished, so those industries would then expand to areas where wood was still cheap, over time making wood it more expensive there, too. Eventually, wood was costly pretty much everywhere, and coal thus became comparably cheap in these regions, and you could use a lot less of it to achieve the same ends. Even if that subbing-in led to bad smells and burning eyes and clouds of dense, black smoke wherever it was burned, then, the cost differential was substantial enough to make using coal the better option in many such cases and areas. This boom in coal usage was amplified still further by the rapid clearing of forests due to the expansion of farm- and pastureland. It was determined, by the late 17th century, that an acre of farm- or pastureland was worth a lot more than woodland used for timber or other purposes—around three-times as valuable—so there was a large-scale deforestation effort to basically claim as much value from these forested lands as possible, dramatically changing the landscape of the British Isles over the course of just a few decades; this transition in part enabled and powered by coal. Around the year 1700, about five-sixths of all coal that was mined, globally, was mined in Britain, and that helped power the empire’s industrial revolution later that century, beginning in something like 1760, as the majority of clever devices that arose during that period were powered by coal, and the global industrial revolution that eventually created what we might consider technological modernity arose, initially—at least in this manifestation of the concept—from coal-powered Britain. What I’d like to talk about today is a remarkable coal-related milestone, considering that history, that Britain recently marked, and what it might mean for this and other fuel-types, moving forward. — In 1882, the first-ever coal-fired power station opened in London—a thermal power station that uses coal as its fuel, which basically means you refine the stuff, break it into tiny, semi-uniform pieces, and then feed those pieces into a coal-fired boiler. In that boiler the coal is burned to generate heat, and that heat boils water, the resulting steam spinning turbines which turn generators that produce electricity. Coal-fired power stations are massively inefficient, with modern versions of the model only boasting a 34-ish% efficiency, meaning about 34% of the total energy contained in the fuel source is ultimately converted into electricity—the rest, about 66% of the energy contained in the coal that’s burned, is lost along the way. That’s not uncommon for power plants, though other fossil fuel-burning plants are somewhat more efficient on average, with oil-powered plants weighing in at about 37% efficiency, and gas-powered versions managing something like 50-60% at their most modern and sophisticated, though simpler variations of the design only achieve about the same as coal. All fossil fuel-powered power stations emit greenhouse gases into the atmosphere as a byproduct of their operation, which has been shown to stoke climate change, and they all have pollutant-related byproducts, as well, though there’s a spectrum: gas is relatively clean-burning compared to its kin, while coal is the absolute worst, releasing all sorts of pollutants into the air with at times severe health consequences for anyone in the general vicinity; oil plants are somewhere in between those two extremes, depending on the type of oil used and the nature of the plant. Those downsides are part of why newer technologies like large-scale wind turbines and solar panel arrays have been replacing fossil fuel-based power plants in many locales, and quite rapidly, though the infrastructure in many areas is optimized for these older-school options, which means there are the plants themselves, which are often quite large and real-estate-spanning, but there’s also all the mines, there’s the shipping facilities, the processing capacity for the coal or oil or whatnot—it’s a nation-spanning network of buildings and machinery and businesses, not to mention all the people who work jobs related to these vital, energy-creating industries. Coal was already beginning to decline in the UK 100 years after that first plant was built, so by the 1990s, as gas, often called natural gas as a sort of branding effort by gas companies to make it sound cleaner and more desirable, was at that point already beginning to replace coal in many electricity-generating facilities. Gas has done the same in many countries—especially those with vast natural sources of it, and the US has opened up a lot of new markets for this fuel type in recent decades, and in the past decade in particular, as it mastered the means of compressing gas into a liquid, often called LNG, and shipping it to ports in Europe around the same time Russia’s invasion of Ukraine was fundamentally rewiring the energy mix on the continent. So gas has played a role in disrupting coal’s hold in many previously coal-happy areas, including the US. But it was renewables that really turned th

    19 min
  7. Politics and Podcasts

    OCT 29

    Politics and Podcasts

    This week we talk about Joe Rogan, Call Her Daddy, and podcast monetization. We also discuss Kamala Harris, Donald Trump, and double-haters. Recommended Book: You Sexy Thing by Cat Rambo Transcript In the world of US politics, double-haters are potential voters who really just don’t like the candidate from either major political party, and thus they decide whether and how to vote based on who they dislike least—or in some cases who they would like to hurt, the most. This isn’t a uniquely American concept, as voters in many global democracies face similar situations, but it seems to be an especially pressing issue in this year’s upcoming US Presidential election—and election day is a week away as of the day this episode goes live—because the race is just so, so close, according to most trusted polls. In that same context, swing states are states that could swing either way, theoretically at least, in terms of who their votes go to, and because these swing states contain enough electoral college votes to allow even the candidate who doesn’t win the popular vote to win the presidency, that makes them especially vital battlegrounds. So there’s a scramble going on right now, for both parties, to muster their existing bases, to shore-up some of the demographic groups they’re relying upon in this election, and to get their messaging in front of as many of those double-haters and other undecideds as possible so as to maybe, possibly swing this neck-and-neck race in their direction. Toward that end, we’ve seen simply staggering sums of money pulled in and spent by both major parties’ campaigns: it’s looking likely that this will be the most expensive election season in US history, with just under $16 billion in spending across federal races, alone—which is up from just over $15 billion in 2020, according to nonpartisan group Open Secrets; that actually means this election will probably end up being just a smidgeon cheaper than 2020’s election, if you adjust for inflation, rather than comparing in absolute dollar terms, but both of these races will have been several times as expensive as previous elections, weighing in at about double 2016’s cost, and triple what these races tended to cost previously, in the early 2000s. For perspective, too, US elections were already quite a lot more expensive than elections held in other wealthy countries. According to a rundown by the Wall Street Journal, Canada’s 2021 election only cost something like $69 million in inflated-adjusted dollars, and US elections tend to cost about 40-times more, per person—so this is a population-scaled figure—than elections in the UK and Germany. The cost of local elections in the US have been increasing, as well, in some cases substantially, and that’s part of why unpaid exposure and promotion is becoming increasingly valuable: it takes a lot of communications oomph to puncture the hubbub of commercial marketing messages in the US, and while pulling in a lot of money to buy ads and fund other promotional efforts is one way to do that, it’s also possible to approach the problem asymmetrically, going to people where they already are, basically, and getting some of that valuable face-time without having to spend a cent on it. And that’s what I’d like to talk about today—specifically, efforts by candidates to get on popular podcasts, and why this medium in particular seems to be the go-to for campaigns at a moment in which the electoral stakes are historically high. — Podcasts, by traditional definition, are audio files delivered using an old-school, open technology called RSS. In the years since they first emerged, beginning in the early days of the 2000s, the transmission mechanisms for these audio files have become a bit more sophisticated, despite being based on essentially the same technology. They’ve been joined, though, by utilities that allow folks to stream undownloaded audio content, to ping the servers where these audio files are stored more regularly, and to attach all kinds of interesting and useful metadata to these files, which add more context to them, while also providing the fundaments of basic micropayment schemes and the capacity to include video versions of an episode, alongside audio. That video component has been pushed forward in part by the success of content-makers on YouTube, where for a long while podcasters have promoted their audio shows with visualized snippets, behind-the-scenes videos, and other such add-on content. Over the past handful of years, though, it has also become a hotbed of original video podcast content, some podcasters even using YouTube as their native distribution client—and that, combined with Spotify’s decision to start offering video podcasting content alongside audio podcasting content, in part to compete with YouTube, has pushed video-podders to the forefront of many charts. Multi-person conversational and interview shows have maybe benefitted most from that shift toward video, as being able to see the people recording these shows, and to watch their body language, all the little microexpressions and other components of conversation and social dynamics that are left out of pure audio shows, has helped them attract more listeners / viewers, while also making these shows an even more potent source of parasocial camaraderie—which was especially valuable during the lockdown-heavy phase of the covid-19 pandemic, but which is also arguably a valuable thing to provide at a period in which a lot of people across all demographics are suffering from intense loneliness and a perceived lack of connection; the sense of familiarity that folks felt listening to a familiar voice in their ear on a regular basis has been emphasized still-further by the ability to see those people on their phones, TVs, and laptops in the same way, and at the same regular cadence. The business model of podcasting has also contributed to the expansion of this type of show, as while podcasting has never been as big and spendy an industry as comparable broadcast mediums, it has been growing, with most shows leaning on some combination of ads, sponsorships, memberships, patronage models, and subscriptions to keep their operations in the black. Some shows make use of many or all of these income-generation approaches, and many of them have varied their business models based on the boom and bust phases the industry has seen over the years; so when ad revenue plummets, formerly ad-heavy shows will pivot to memberships, and when the listener membership well grows shallow, they might shift to some kind of featured sponsor model. As of early 2024, there are more than half a billion regular podcast listeners, globally, and ad spending in this space, globally, reached over $4 billion for the first time this year. That aforementioned shift toward video has tilted a lot of listening in that direction, with about a third of all podcast listeners in the US also watching at least one podcast, rather than just listening to it. That watchability component has also nudged YouTube and Spotify into the lead in terms of podcast delivery, alongside Apple, which didn’t invent the podcast, even though the medium is named after their iPod product, but they did bring it to the forefront and make it widely available—Apple’s relative lack of investment in this space, for years, left the doors open for those other competitors, and again, their decision to feature video podcast content alongside pure audio shows has shifted the landscape of this industry substantially, raising questions about what a podcast even is, if any old YouTube show could also theoretically be categorized as such; it’s a blurry distinction at this point, a bit like the debate over whether audiobooks should be considered books, or if only written, visual versions should bear that label. Also worth noting here is that nearly half, about 47%, of all US citizens ages 12 and up listen to a podcast at least once a month, and 34% listen every week. 11% of that demographic’s daily audio-time is spent listening to podcasts, which is quadruple the figure a decade ago, in 2014, and 23% of weekly podcast listeners in the US spend 10 or more hours with these shows each week, though the average listening time each week is also pretty high, weighing in at 7.4 hours. Podcasts have diverse audiences and hit a range of economic classes and people of varying education levels—though it leans slightly higher than the average in terms of both educational attainment and income—and interestingly, folks seem to be especially influenced by podcast recommendations, 46% of weekly podcast listeners reporting that they purchased something based on a recommendation or advertisement they heard on a show. All of which points at why podcasts, and especially interview podcasts, and even more especially video-heavy interview podcasts, have become such highly desired media real estate in this year’s US presidential election; these sorts of shows aren’t always the most desired medium for brands, because tracking return on investment, money earned per dollar spent, is difficult with podcasts compared to, for instance, buying ads on streaming TV shows or social media, but they’re great for raising awareness and general brand-building efforts, which is exactly what these candidates and their parties are aiming for. So more people are listening to these things, people tend to trust what they hear on podcasts more than on other types of media, and the demographics these shows reach are highly desirable, politically. This is why, over the past few weeks, candidates Kamala Harris and Donald Trump have appeared on some of the biggest podcasts in existence, right now: Call Her Daddy for the former, and the Joe Rogan Experience for the latter. Both of these appearances were ostensibly pretty risky, as podcast interviewers tend to color outside the lines compared to

    17 min
  8. Political Betting Markets

    OCT 22

    Political Betting Markets

    This week we talk about DJT, Polymarket, and Kalshi. We also discuss sports betting, gambling, and PredictIt. Recommended Book: Build, Baby, Build by Bryan Caplan Transcript Trump Media & Technology Group, which trades under the stock ticker DJT, has seen some wild swings since it became a publicly tradable business entity in late-March of 2024. The Florida-based holding company for Truth Social, a Twitter-clone that was released in early 2022 following former President Donald Trump’s ousting from Twitter—that ousting the result of his denial of his loss in the 2020 presidential election—is a bit of an odd-bird in the technology and media space, as while it’s ostensibly an umbrella corporation for many possible Trump-themed business entities, Truth Social is the only one that’s gotten off the ground so far, and that platform hasn’t done well in traditional business or even aspirational tech-business terms: a financial disclosure in November of 2023 indicated that the network had tallied a cumulative loss of at least $31.5 million since it was launched, and the holding company’s numbers were even worse: when they filed their regulatory paperwork in March of 2024, they noted that Trump Media & Technology Group had lost $327.6 million, while making a mere $770,000 in revenue. Those kinds of numbers, the company hemorrhaging money, would be a huge problem if DJT was a typical media business, or business of any kind, really. But for most people who invest in the company’s stock, this entity seems to be less a traditional stock holding, like you might buy shares of NVIDIA or Coca-Cola, hoping to earn dividends or see the value of the stock increase over time based on the performance and assumed future performance of the company in question, but instead it seems to operate as a means of betting on Trump and his political aspirations: many people who have been asked why they’re buying the stock of a clearly fumbling company say that they do it because they like Trump and what he stands for, and some have suggested they assume the stock will do much better if and when he’s back in office. Other entities, especially those who oppose Trump and his politics, have pointed out that this publicly traded business provides foreign and US entities an easy, and easily deniable means of basically bribing Trump—or getting on his good side, if you want to use less charged language—as they could simply, and legally pick up a large number of shares, raising the price of the stock, which in turn increases the size of Trump’s fortune, which he could then, if he so chooses, cash out of at some point, but in the mean time this allows him to do the more typical rich person thing and just borrow money against the non-money, stock assets he owns. All of which would be difficult to prove, which is part of why this would, in theory, be an excellent means of funneling money to someone who might hold the reins of power in the near-future, if one were so inclined to do so. But at the moment that’s all speculation, and with ongoing investigations into other purported bribery schemes on the part of Trump and his campaign, it’s not clear that Trump would need DJT in order to get money into his coffers, as more direct approaches—like simply depositing ten million dollars into his campaign account from Egypt’s state-run bank, seem more straightforward, and just as unlikely to result in any kind of pushback from the US’s oversight panels, based on how they’ve addressed that particular accusation so far, at least. Of course, some people are simply looking for points of leverage anywhere they can find it, not for political or regulatory manipulation purposes, but to earn money by gambling on assets that change value in dramatic and seemingly predictable ways. For day traders and other arbitrage-seekers, then, a stock that goes up and down based on the perceived successes and failures of a public figure who’s constantly saying and doing things that can be construed in different ways by different people is an appealing target, even lacking a political motivation for tracking (and perhaps even influencing, to a limited degree) those numbers. What I’d like to talk about today is another type of political betting, and how a recent court case may make politics in the US a lot more tumultuous, maybe more measurable, and possibly more profitable, for some. — In mid-2021, a New York-based online prediction market called Kalshi launched in the US, and this service was meant to serve as a platform through which users could place bets—in the form of trades—on all sorts of things, ranging from when the Fed would next cut interest rates, and by how much, to who would win various global awards, like the Nobel in chemistry. Bets can only be placed on yes or no questions, which shapes the nature of said questions, and delineates the sorts of questions that can be asked, and in general the platform pays out a dollar for each winning contract—so if you buy one contract saying the Republican party will control the House after November’s election, and they do, you would win a dollar, but if they don’t, you would lose whatever money you spent to buy that contract—and these contracts can be purchased for sums that are based on how likely the event is currently expected to be: so if there’s a low chance, based on all available variables, that the Republicans will take the House, that contract might cost substantially less than a dollar to purchase, whereas if it’s likely they’ll take it, it would cost close to a dollar—so the payout is larger for events considered to be unlikely. The original idea behind Kalshi, and similar platforms, of which there are many, operating in many different places around the world, was to provide investors with a hedge against events that are otherwise difficult to work into one’s asset portfolio. It’s relatively simple to have a bunch of bets that will pay out big time if the US economy does well, for instance, and simple enough to buy counter-bets that will pay out decently well if it does badly—many investors buying some of each, so they’re not wiped out, no matter what happens—but there are all sorts of things that can mess with one’s otherwise well-balanced investment strategies, like the emergence of global pandemics and the surprise decision of the UK to leave the European Union. If you can place bets that will pay out big-time when unlikely things happen, though, that can help re-balance a financial loss that arises from the occurrence of said unlikely events; if you lose a bunch of money from your stock portfolio because the UK voted for Brexit, but you also bought a bunch of contracts on this kind of market that would pay out substantially if Brexit was successful, you’ll reach a kind of equilibrium that isn’t as simple to achieve using other markets, because of how difficult it can be to directly link a stock or bond with that kind of not-directly-financial event. So Kalshi pitched itself as that kind of alternative asset market, predicated on bets, but while they had a license from the US Commodities Futures Trading Commission, or CFTC, to function as a contract market in the States, acquired the year before they launched, their proposal to start a political prediction market, which would allow folks to bet on which party would control the US congress, was denied by the CFTC in September of 2023, the agency claiming that allowing such bets would create bad incentives in the electoral process, and that offering these sorts of contracts would violate US market regulations for derivatives. A judge ruled in Kalshi’s favor a year later, in September of 2024, saying that the agency had exceeded its authority in banning this type of contract-issuance by Kalshi, and while the CFTC attempted to stall that component of their market’s implementation, on October 2 of this year, a federal appeals court ruled in Kalshi’s favor, and the platform was thus formally allowed to offer contracts that served as a betting market for US politics on which actual money could be lost and earned. That last point is important, as throughout this process, and even before Kalshi was launched, other betting markets have been common, including those that have allowed bets on US political happenings. It’s just that the majority of them, and the ones that have persisted and grown in the US in particular, haven’t allowed folks to bet actual money on these things: they’ve allowed, in some cases, the betting of on-platform tokens, which represent credibility, not money, though a few money-trading entities, like PredictIt, have been on the agency’s radar, but in PredictiIt’s case, it was granted what amounts to a “we won’t take action against you, despite what you’re doing being questionable” letter from the CFTC, which until Kalshi’s case turned out in their favor, meant PredictIt was one of the few, large-scale, reputable real-money political prediction markets available in the US. Not all such markets have been so lucky, but that luck has been highly correlated with their approach to handling money, the structure of the company, and the degree to which they’ve been willing to play ball with the CFTC and other interested agencies. All that said, we’ve reached an interesting point in which these markets have conceivably become more serious and useful, because rather than relying on not-real tokens that have no actual value to anyone—so you could create an account on one of these sites, bet all your tokens on a silly position that makes no sense, and suffer no consequences for that bet—we now have platforms that allow folks to put their money where their beliefs are, which in turn should theoretically make these markets more reliable in terms of showing what a certain segment of the population actually believes; how likely different candidates are to win, di

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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

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