Let's Know Things Podcast Por Colin Wright arte de portada

Let's Know Things

Let's Know Things

De: 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.comColin Wright Política y Gobierno
Episodios
  • Sports Betting

    Sports Betting
    Jan 6 2026
    This week we talk about prediction markets, incentives, and gambling addiction.We also discuss insider trading, spot-fixing, and Gatorade.Recommended Book: The Kingdom, the Power, and the Glory by Tim AlbertaTranscriptPrediction markets are hundreds of years old, and have historically been used to determine the likelihood of something happening.In 1503, for instance, there was a market to determine who would become the next pope, and from the earliest days of commercial markets, there were associated prediction markets that were used to gauge how folks thought a given business would do during an upcoming economic quarter.The theory here is that while you can just ask people how well they think a political candidate will fare in an election or who they think will become the next pope, often their guesses, their assumptions, or their analysis will be swayed by things like political affiliation or maybe even what they think they’re meant to say—the popular papal candidate, for instance, or the non-obvious, asymmetric position on a big commercial enterprise that might help an analyst reinforce their brand as a contrarian.If you introduce money into the equation, though, forcing people to put down real currency on their suspicions and predictions, and give them the chance to earn money if they get things right, that will sometimes nudge these markets away from those other incentives, making the markets commercial enterprises of their own. It can shift the bias away from posturing and toward monetization, and that in turn, in theory at least, should make prediction markets more accurate because people will try to align themselves with the actual, real-deal outcome, rather than the popular—with their social tribe, at least—or compellingly unpopular view.This is the theory that underpins entities like Polymarket, Kalshi, Manifold Markets, and many other online prediction markets that have arisen over the past handful of years as regulations on these types of businesses have been eased, and as they’ve begun to establish themselves as credible players in the predicting-everything space.In politics in particular, these markets have semi-regularly shown themselves to be better gauges of who will actually win elections than conventional polls and surveys, and though their records are far from perfect and still heavily biased in some cases, such community-driven predictions from money-motivated markets are gaining credibility because of their capacity to incentivize people to put their money where their mouths are, and to try to profit from accurate preordination.The flip-side of these markets, and some might even say a built-in flaw with no obvious solution, is that they are rife with insider trading: people who are in the position to know things ahead of time making in some cases millions of dollars by placing big bets that, for them, aren’t bets at all, because they know what will or what is likely to happen.This seems to have occurred at least a few times with big political events in 2025, and it’s anticipated that it could become an even bigger issue in the future, especially for markets that use cryptocurrencies to manage payments, as those are even less likely than their fiat currency peers to keeps solid tabs on who’s actually behind these bets, and thus who might be trading on knowledge that they’re not supposed to be trading on.That said, it could be argued that such insider trading makes these markets even more accurate, eventually at least. And that points us toward another problem: the possibility that someone on the inside might look at a market and realize they can make a killing if they use their position, their power to sway these markets after placing a bet, giving them the ability to assure a payout by abusing their position—major events being influenced by the possibility of a community-funded payday for those in control.What I’d like to talk about today is the same general principle as it’s playing out in the sports world, and why the huge sums of money that are now sloshing around in the sports betting industry in the US are beginning to worry basically everyone, except the sports betting companies themselves.—In October of 2025, the head coach of the NBA basketball team, the Portland Trail Blazers, Chauncey Billups, Miami Heat player Terry Rozier, and former NBA player Damon Jones, and about 30 other people were arrested by the FBI due to their alleged illegal sports gambling activities. Rozier was already under investigation following unusual betting activity that was linked to his performance in a 2023 game—he was later cleared of wrongdoing, but the implication then and in this more recent instance is that he and those other folks who were rounded up by the FBI may have been involved in rigging things so they could get a big payoff on gambling markets.Similar things have been happening across the sports world, including a lifetime ban for Jontay Porter, a former ...
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    16 m
  • Data Center Politics

    Data Center Politics
    Dec 23 2025
    This week we talk about energy consumption, pollution, and bipartisan issues.We also discuss local politics, data center costs, and the Magnificent 7 tech companies.Recommended Book: Against the Machine by Paul KingsnorthTranscriptIn 2024, the International Energy Agency estimated that data centers consumed about 1.5% of all electricity generated, globally, that year. It went on to project that energy consumption by data centers could double by 2030, though other estimates are higher, due to the ballooning of investment in AI-focused data centers by some of the world’s largest tech companies.There are all sorts of data centers that serve all kinds of purposes, and they’ve been around since the mid-20th century, since the development of general purposes digital computers, like the 1945 Electronic Numerical Integrator and Computer, or ENIAC, which was programmable and reprogrammable, and used to study, among other things, the feasibility of thermonuclear weapons.ENIAC was built on the campus of the University of Pennsylvania and cost just shy of $500,000, which in today’s money would be around $7 million. It was able to do calculators about a thousand times faster than other, electro-mechanical calculators that were available at the time, and was thus considered to be a pretty big deal, making some types of calculation that were previously not feasible, not only feasible, but casually accomplishable.This general model of building big-old computers at a center location was the way of things, on a practical level, until the dawn of personal computers in the 1980s. The mainframe-terminal setup that dominated until then necessitated that the huge, cumbersome computing hardware was all located in a big room somewhere, and then the terminal devices were points of access that allowed people to tap into those centralized resources.Microcomputers of the sort of a person might have in their home changed that dynamic, but the dawn of the internet reintroduced something similar, allowing folks to have a computer at home or at their desk, which has its own resources, but to then tap into other microcomputers, and to still other larger, more powerful computers across internet connections. Going on the web and visiting a website is basically just that: connecting to another computer somewhere, that distant device storing the website data on its hard drive and sending the results to your probably less-powerful device, at home or work.In the late-90s and early 2000s, this dynamic evolved still further, those far-off machines doing more and more heavy-lifting to create more and more sophisticated online experiences. This manifested as websites that were malleable and editable by the end-user—part of the so-called Web 2.0 experience, which allowed for comments and chat rooms and the uploading of images to those sites, based at those far off machines—and then as streaming video and music, and proto-versions of social networks became a thing, these channels connecting personal devices to more powerful, far-off devices needed more bandwidth, because more and more work was being done by those powerful, centrally located computers, so that the results could be distributed via the internet to all those personal computers and, increasingly, other devices like phones and tablets.Modern data centers do a lot of the same work as those earlier iterations, though increasingly they do a whole lot more heavy-lifting labor, as well. They’ve got hardware capable of, for instance, playing the most high-end video games at the highest settings, and then sending, frame by frame, the output of said video games to a weaker device, someone’s phone or comparably low-end computer, at home, allowing the user of those weaker devices to play those games, their keyboard or controller inputs sent to the data center fast enough that they can control what’s happening and see the result on their own screen in less than the blink of an eye.This is also what allows folks to store backups on cloud servers, big hard drives located in such facilities, and it’s what allows the current AI boom to function—all the expensive computers and their high-end chips located at enormous data centers with sophisticated cooling systems and high-throughput cables that allow folks around the world to tap into their AI models, interact with them, have them do heavy-lifting for them, and then those computers at these data centers send all that information back out into the world, to their devices, even if those devices are underpowered and could never do that same kind of work on their own.What I’d like to talk about today are data centers, the enormous boom in their construction, and how these things are becoming a surprise hot button political issue pretty much everywhere.—As of early 2024, the US was host to nearly 5,400 data centers sprawled across the country. That’s more than any other nation, and that number is growing quickly as those ...
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    17 m
  • Chip Exports

    Chip Exports
    Dec 16 2025
    This week we talk about NVIDIA, AI companies, and the US economy.We also discuss the US-China chip-gap, mixed-use technologies, and export bans.Recommended Book: Enshittification by Cory DoctorowTranscriptI’ve spoken about this a few times in recent months, but it’s worth rehashing real quick because this collection of stories and entities are so central to what’s happening across a lot of the global economy, and is also fundamental, in a very load-bearing way, to the US economy right now.As of November of 2025, around the same time that Nvidia, the maker of the world’s best AI-optimized chips at the moment became the world’s first company to achieve a $5 trillion market cap, the top seven highest-valued tech companies, including Nvidia, accounted for about 32% of the total value of the US stock market.That’s an absolutely astonishing figure, as while Nvidia, Apple, Microsoft, Alphabet, Amazon, Broadcom, and Meta all have a fairly diverse footprint even beyond their AI efforts, a lot of that value for all of them is predicated on expected future income; which is to say, their market caps, their value according to that measure, is determined not by their current assets and revenue, but by what investors think or hope they’ll pull in and be worth in the future.That’s important to note because historically the sorts of companies that have market caps that are many multiples of their current, more concrete values are startups; companies in their hatchling phase that have a good idea and some kind of big potential, a big moat around what they’re offering or a blue ocean sub-industry with little competition in which they can flourish, and investment is thus expected to help them grow fast.These top seven tech companies, in contrast, are all very mature, have been around for a while and have a lot of infrastructure, employees, expenses, and all the other things we typically associated with mature businesses, not flashy startups with their best days hopefully ahead of them.Some analysts have posited that part of why these companies are pushing the AI thing so hard, and in particular pushing the idea that they’re headed toward some kind of generally useful AI, or AGI, or superhuman AI that can do everyone’s jobs better and cheaper than humans can do them, is that in doing so, they’re imagining a world in which they, and they alone, because of the costs associated with building the data centers required to train and run the best-quality AI right now, are capable of producing basically an economy’s-worth of AI systems and bots and machines operated by those AI systems.In other words, they’re creating, from whole cloth, an imagined scenario in which they’re not just worthy of startup-like valuations, worthy of market caps that are tens or hundreds of times their actual concrete value, because of those possible futures they’re imagining in public, but they’re the only companies worthy of those valuation multiples; the only companies that matter anymore.It’s likely that even if this is the case, that the folks in charge of these companies, and the investors who have money in them who are likely to profit when the companies grow and grow, actually do believe what they’re telling everyone about the possibilities inherent in building these sorts of systems.But there also seems to be a purely economic motive for exaggerating a lot and clearing out as much of the competition as possible as they grow bigger and bigger. Because maybe they’ll actually make what they’re saying they can make as a result of all that investment, that exuberance, but maybe, failing that, they’ll just be the last companies standing after the bubble bursts and an economic wildfire clears out all the smaller companies that couldn’t get the political relationships and sustaining cash they needed to survive the clear-out, if and when reality strikes and everyone realizes that sci-fi outcome isn’t gonna happen, or isn’t gonna happen any time soon.What I’d like to talk about today is a recent decision by the US government to allow Nvidia to sell some of its high-powered chips to China, and why that decision is being near-universally derided by those in the know.—In early December 2025, after a lot of back-and-forthing on the matter, President Trump announced that the US government will allow Nvidia, which is a US-based company, to export its H200 processors to China. He also said that the US government will collect a 25% fee on these sales.The H200 is Nvidia’s second-best chip for AI purposes, and it’s about six-times as powerful as the H20, which is currently the most advanced Nvidia chip that’s been cleared for sale to China. The Blackwell chip that is currently Nvidia’s most powerful AI offering is about 1.5-times faster than the H200 for training purposes, and five-times faster for AI inferencing, which is what they’re used for after a model is trained, and then it’s used for ...
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    14 m
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