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Comment on Project Glasswing: An initial update in ~tech
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Comment on Why airlines are always going bankrupt in ~transport
skybrian LinkFrom the article: [...] [...] [...] [...] [...]From the article:
The collapse of Spirit was unique in that in its death throes it managed to solicit a bailout offer from the U.S. government; but it was not unique among its fellow airlines in going broke. Airlines are a bad business: a really, really bad business. The International Air Transport Association, the trade body of the global airline industry, has documented for years that airlines as a sector destroy investor value in the aggregate. The IATA’s 2026 outlook, looking forward to a quite strong year—this was before the Iran war broke out and oil prices surged—projected an average return on invested capital of 6.8 percent, against a weighted average cost of capital of 8.2 percent. As the IATA’s report said, “the airline industry collectively does not generate earnings that cover its cost of capital.” This has been the case for a long time. From its deregulation in 1978 to the end of 2025, the airline industry has cumulatively lost money: its net profit over those 47 years sits at negative $37 billion.1
Given these grim economics, you won’t be surprised to hear that airlines have a bad habit of going insolvent. This includes many of the most famous names in the history of aviation. Pan Am, long the unofficial flag carrier of the United States, ceased operations in 1991; Eastern Air Lines liquidated the same year; TWA, the carrier of Howard Hughes, was absorbed into American Airlines after a third bankruptcy filing in 2001; Braniff died in 1982. And those are only the most famous names; countless aviation startups have come and gone. (Have you ever heard of Trump Shuttle?) Even airlines with the backing of a national government go bankrupt all the time: Alitalia, Italy’s flag carrier, reported only a single year of profit since its founding in 1946 and was saved countless times by the Italian government before ultimately ceasing operations in 2021. Even those airlines that survive for long periods of time are perpetually in financial distress. Between 1978 and 2005, more than 160 airlines filed for bankruptcy; virtually every major U.S. carrier other than Southwest has been to bankruptcy court at least once. In September 2005, every one of the four largest American airlines—United, Delta, Northwest, and US Airways—was operating simultaneously under Chapter 11 protection.
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One of the central ideas in the study of cooperative games is the idea of the core. The “core” of a game is simply the set of outcomes that no coalition of players can improve upon by breaking away and dealing among themselves. If an outcome is “in the core,” it’s stable, such that nobody can propose a side deal that makes every member of some subgroup better off; if the core is “empty,” then every arrangement is vulnerable to being undercut by some side-coalition, and the market has no resting point, no stable equilibrium. It cycles, destabilizes, and, without outside intervention of some kind, eventually breaks down.
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Suppose you try to run the industry with just two firms. Demand exceeds supply, such that prices are high and there’s plenty of profit to enjoy. But that profit is exactly what invites a third firm to enter, undercut both incumbents, and still cover its costs. Now there are three firms, and supply exceeds demand. Someone has to operate below scale and bleed money on fixed expenses; eventually one of the firms will have to leave the market. Now you’re back to where you started: prices recover, profits climb higher, and the cycle begins again.
So whichever side of the integer you land on—one firm too many, one firm too few—there is some coalition of firms and customers that can profitably reorganize the market against the existing arrangement. In the language of cooperative game theory, the allocation is always vulnerable to defection by some coalition. The core is empty.
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You find the empty-core syndrome, for example, in the railroad industry of the nineteenth century. Building a railroad required vast capital expenditure on track, rolling stock, depots, and bridges; but once the infrastructure was in place, the marginal cost of carrying an additional ton of freight or another passenger across it was almost zero. Two railroads running competing lines between, say, Chicago and New York could not both operate at full cost recovery; so they spent the 1870s and 1880s alternately forming pools and rate-fixing agreements, then watching them collapse into ruinous price wars, going bankrupt, reorganizing, and starting the cycle over again.
And you’ll find the same dynamic in the contemporary airline industry.
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The economics of a genuinely competitive airline industry, then, are really bad—for the same reason the economics of any empty-core industry are bad. And this suggests that, in search of stability, the market participants will eventually try to suppress competition, if only so they can survive.
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In the course of this long path of suffering, every airline has decided, in one way or another, that the competitive airline industry is structurally unprofitable, and not really worth participating in.
One response is to cartelize the industry through means other than direct rate-fixing: to recreate, by private contract, the kind of competition-suppressing arrangements that the CAB previously wrote into statute. The international alliances of which airlines are so fond—Star, SkyTeam, and Oneworld, with their codesharing and antitrust-immunized joint venture agreements—are one form of this: they allow nominally competitive airlines to coordinate scheduling, share revenues, and refrain from undercutting each other on high-value trans-oceanic routes.
The hub-and-spoke model that dominates domestic aviation is another form of this tacit cartelization. By concentrating its operations at a few major airports, an airline can turn those airports into something close to local monopolies. American Airlines, for example, carries about 90 percent of passengers at Charlotte Douglas and 82 percent of passengers at Dallas-Fort Worth, but only about 7 percent of passengers at San Francisco, where the market is dominated by United, and 2 percent at Atlanta International, which is the central hub for Delta. In effect, major domestic airlines have carved up the country into a sort of feudal map of fortress hubs, with each one operating a quasi-monopoly through which it produces the margins that cannot be earned in genuine competition.
But the other response, and perhaps the more interesting one, is to leave the airline business entirely: to treat the planes as a kind of loss-leading distribution channel for what has become the actual product. The main innovation of the airline industry of the last few decades, from this vantage point, has been the frequent flyer program. Invented in the immediate aftermath of deregulation as airlines scrambled for ways to lock in customer loyalty, frequent flyer programs have become something quite different: enormous, free-floating financial businesses, miles-as-currency operations whose value bears essentially no relationship to the cost of the seats backing them.
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Why airlines are always going bankrupt
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Comment on Language models are weird for the same reason human cultures are weird in ~comp
skybrian LinkFrom the article: [...] [...] [...] [...] [...] [...]From the article:
Suppose, as an illustration, that you live in a very different time and place: let’s say you live in a small farming community in prehistoric Mesoamerica, about 5,000 years ago. Though you don’t really see it this way—you have other things to deal with—your world is composed of feedback loops, some of which are more immediate and more obvious than others. For example: if you run in front of a snake, the feedback from your environment will be immediate and obvious. It will be so immediate and so obvious, in fact, that your aversion to doing so will be hardwired into your genes. No one needs to tell you not to run in front of a snake, because your ancestors survived and reproduced in part because they didn’t run in front of snakes.
But there are other feedback mechanisms that are more opaque. For example: the big new thing in your farming community right now is the cultivation of an interesting grain, a wild grass with small, hard kernels. Eventually this will come to be called “maize.” It’s a wonderful food source, since it’s calorie-dense and easy to cultivate; but if you eat it as a staple for a long time without the right preparation, you’ll develop a horrible wasting disease, and your body will start to display such symptoms as cracked skin, diarrhea, and dementia. And after a long time, this disease—people will later call it “pellagra,” from the Italian for “rough skin”—will kill you.
So maize provides a much more opaque feedback mechanism. Eventually you’ll suffer and die from eating it; but it takes so long to set in, and cause and effect are so unclear, that you don’t have any instinctive sense of what to do. (And cultivating maize is a new thing anyway: your primate ancestors weren’t doing it millions of years ago.) And there are all sorts of problems like this. How do you prepare fish and not get sick? How do you pick mates such that your offspring are healthy? If feedback from the environment is coarse and sparse, how do you learn what to do?
Henrich says that you learn through imitation. The true “secret of our success” was our propensity for imitating others, above all imitating those who are successful and visibly competent. At some point in your farming community, or in a farming community in the broader Mesoamerican region, someone will prepare maize in a certain way that involves soaking it in an alkaline solution of water and ash. (We now call that process nixtamalization.) Unbeknownst to them, that process will release otherwise inaccessible nutrients, such that they’ll be able to avoid pellagra. People will notice their success and imitate the practice, while other attempts to ward off pellagra will have failed; and the practice will catch on, and become Mesoamerican tradition.
This is cultural evolution. Scaled across many generations, the result is a kind of slow learning process: adaptive practices are carried forward, since their hosts thrive and are imitated; and maladaptive ones are pruned, since their hosts do not.
But here we encounter another problem. If feedback cycles are long and the feedback itself is coarse, then it’s hard to know why someone succeeded. The nixtamalization process, for example, was bundled with practices that didn’t do anything in particular, like blowing on the maize before putting it to cook or swaddling certain cobs like newborns and letting them sit outside the house all year. But if all you know is that the process as a whole seems to prevent pellagra, then the optimal thing is to imitate the entire bundle of practices. It’s much easier to see that something is working than to intuit exactly what is working.
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And so Henrich says that human culture is shaped not just by imitation but indeed by overimitation: and that overimitation is the source of all sorts of weirdness within every culture. The same tendency for social learning that allowed us to inhabit the most inhospitable parts of the world also resulted in all sorts of eccentricities, things that can’t quite be explained as functional. Some inert or inexplicable thing was bundled with something adaptive, and was imitated along with it; that practice was passed on and inherited by generation after generation; and eventually it ossified into tradition. As a result every culture has its fair share of weird quirks and eccentricities.
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And the result is that human evolution is both a remarkably powerful learning mechanism and a remarkably crude one: prone to creating cultures that are adaptive to their local environments, while also riddled with eccentricities that range from the harmlessly inert to the actively destructive.
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Now let’s consider another adaptive system, of a very different kind: the large language model.
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The pretraining process is extraordinarily dense, with tens of trillions of microcorrections localized to specific tokens; but post-training is quite different. The SFT and RL stages that characterize post-training involve orders of magnitude fewer training events; and because they score entire outputs rather than specific tokens, the feedback the models receive is much more coarse than what they receive from pretraining.
In this way pretraining is akin to biological evolution, and post-training to cultural evolution. And the result is what we’d expect from the logic of adaptive systems: post-training frequently produces eccentricities of all kinds in language models.
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The language models overlearn—overfit would be the more precise term here—for the same fundamental reason that humans do. Overfitting is the Bayesian-optimal strategy in environments of coarse and sparse feedback. If you receive a single reward signal for a complex output and have no way of knowing which features of that output earned the reward, the rational move is to reproduce all of them, including the ones that were incidental.
That, ultimately, is where the goblins came from.
At some point in 2025, OpenAI trained a reward model for the “Nerdy” personality feature on ChatGPT. During that training process, OpenAI’s blog post says, human raters “unknowingly gave particularly high rewards for metaphors with creatures.” The “Nerdy” prompt advised the model that it is “an unapologetically nerdy, playful and wise AI mentor to a human” and must “undercut pretension through playful use of language”; and human raters, asked to score the adherence of outputs to that prompt, consistently gave the model a better score if it mentioned goblins, presumably because such mentions were “playful” and “nerdy.” Simply including “goblin” in a response had positive uplift for the “Nerdy” fidelity rating in 76 percent of cases.
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The goblins are a particularly funny tic: but there are countless others. Opus’s tendency to think that questions phrased in certain ways are word games, or GPT-5.1’s tendency to think that conditional statements (like “if it’s sunny, go for a walk”) demand that the model output code, or Haiku 4.5’s tendency to rebut the claim that “5 + 8 = 13,” are artifacts of the same dynamic.
Language models are truly fantastic learners; but because post-training processes are “chunky,” with feedback sparse and coarse relative to the signal they receive from pretraining, the inevitable result is that they bundle genuine ability with strange tics and behaviors. What was true for the adaptive system of human culture is true for the adaptive system of language models: weird behavioral artifacts are inevitable when a capable adaptive system must learn from sparse and coarse feedback.
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Language models are weird for the same reason human cultures are weird
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Comment on Is there a "Razor" for the idea that "If a Billionaire is against it, I'm for it?" in ~finance
skybrian LinkI think it’s reasonable to be suspicious when someone on the opposite side advocates for something. But why be a blind partisan? Why give it catchy name? Repeating simple slogans is the sort of...I think it’s reasonable to be suspicious when someone on the opposite side advocates for something. But why be a blind partisan? Why give it catchy name? Repeating simple slogans is the sort of thing that makes social media worse.
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Comment on Project Glasswing: An initial update in ~tech
skybrian LinkFirst public macOS kernel memory corruption exploit on Apple M5 ... ...First public macOS kernel memory corruption exploit on Apple M5
The latest flagship example is MIE (Memory Integrity Enforcement), Apple’s hardware-assisted memory safety system built around ARM’s MTE (Memory Tagging Extension). It was introduced as the marquee security feature for the Apple M5 and A19, specifically designed to stop memory corruption exploits, the vulnerability class behind many of the most sophisticated compromises on iOS and macOS.
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The exploit is a data-only kernel local privilege escalation chain targeting macOS 26.4.1 (25E253). It starts from an unprivileged local user, uses only normal system calls, and ends with a root shell. The implementation path involves two vulnerabilities and several techniques, targeting bare-metal M5 hardware with kernel MIE enabled.
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We didn’t build the chain alone. Mythos Preview helped identify the bugs and assisted throughout exploit development.
Mythos Preview is powerful: once it has learned how to attack a class of problems, it generalizes to nearly any problem in that class. Mythos discovered the bugs quickly because they belong to known bug classes. But MIE is a new best-in-class mitigation, so autonomously bypassing it can be tricky. This is where human expertise comes in.
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Comment on Nasdaq rewrites its index inclusion rules ahead of SpaceX IPO in ~finance
skybrian Link ParentFor the Nasdaq, the company that makes the list is the Nasdaq stock exchange and they benefit if SpaceX lists its stock on their exchange, so they’re doing it to get the business. For the S&P 500,...For the Nasdaq, the company that makes the list is the Nasdaq stock exchange and they benefit if SpaceX lists its stock on their exchange, so they’re doing it to get the business.
For the S&P 500, it’s not just about SpaceX. OpenAI and Anthropic are going to be very large and unprofitable when they go public too. The size of these IPO’s is unprecedented. The stated justification for the S&P 500 is that they want to include all the largest public companies:
Index investing isn’t about what companies are “legitimate.” It’s about having investments in all of them in case they go up.
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Comment on Nasdaq rewrites its index inclusion rules ahead of SpaceX IPO in ~finance
skybrian Link ParentTesla was added to the S&P 500 in 2020. This is about SpaceX.Tesla was added to the S&P 500 in 2020. This is about SpaceX.
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Comment on Waymo pauses Atlanta service as its robotaxis keep driving into floods in ~transport
skybrian LinkWaymo expands pause to four cities as robotaxis keep driving into floodsWaymo expands pause to four cities as robotaxis keep driving into floods
Waymo also halted service in Dallas and Houston because of severe weather across Texas this week, the company confirmed to TechCrunch late Thursday. The expansion was first reported by Bloomberg News.
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Comment on Nasdaq rewrites its index inclusion rules ahead of SpaceX IPO in ~finance
skybrian Link ParentOther indexes have different rules. Looks like the S&P 500 might have a rule change, though not as extreme: Elon Musk's SpaceX Could Be Fast-Tracked Into S&P 500 After IPO Under Proposed Rule ChangesOther indexes have different rules. Looks like the S&P 500 might have a rule change, though not as extreme:
Elon Musk's SpaceX Could Be Fast-Tracked Into S&P 500 After IPO Under Proposed Rule Changes
The rule changes include letting IPOs enter the index six months after their debut on an eligible index instead of a 12-month period, according to current rules.
The index also proposed eliminating a minimum Investable Weight Factor (IWF) of 0.10 for megacap companies. The IWF is a methodology used to calculate the number of shares of a company available to trade on the market.
Notably, the proposed rule changes also eliminate profitability requirements for megacap companies. Current rules require a company to be profitable on a GAAP basis for 12 months to be considered for the index, but that rule could be eliminated.
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Comment on Project Glasswing: An initial update in ~tech
skybrian LinkFrom the article: [...] [...] [...]From the article:
So far, Mythos Preview has found what it estimates are 6,202 high- or critical-severity vulnerabilities in [open source] projects (out of 23,019 in total, including those it estimates as medium- or low-severity).
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As we noted above, the bottleneck in fixing bugs like these is the human capacity to triage, report, and design and deploy patches for them. Finding them in the first place has become vastly more straightforward with Mythos Preview. We’ve created a dashboard of the open-source vulnerabilities we’ve scanned, below, which shows the different steps in our disclosure process and will track our progress over time. This shows vulnerabilities of all severity levels, rather than only the subset initially assessed as high- or critical-severity by Mythos Preview. Note the steep drop-off at each phase, reflecting the amount of human effort required to verify and fix each of the vulnerabilities.
Our process for triaging vulnerabilities is intensive. First, we or one of the external security firms we work with reproduce the issue that Mythos has found and re-assess its severity. Once we’ve confirmed that a vulnerability is real, we check for whether there are already fixes in place, and write a detailed report to the software’s maintainers. We take considerable care here: on top of the regular challenges of maintaining open-source software, maintainers have been facing a deluge of low-quality, AI-generated bug reports. Indeed, several maintainers have told us they’re currently severely capacity constrained, and some have even asked us to slow down our rate of our disclosures because they need more time to design patches. (On average, a high- or critical-severity bug found by Mythos Preview takes two weeks to patch.)
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75 of the 530 high- or critical-severity bugs we’ve reported have now been patched, and 65 of those have been given public advisories. The number of patches is still relatively low for three reasons. First, we’re still early in the 90-day window that’s set out in our Coordinated Vulnerability Disclosure policy: we expect many more patches to land soon. Second, we are likely to be undercounting patches because some vulnerabilities are patched without a public advisory: in those cases, we’re reliant on scanning for the patches ourselves using Claude. Third, the low volume of patches reflects a genuine problem: even at our relatively slow pace of disclosures, Mythos Preview is adding to an already-overloaded security ecosystem.
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Many generally-available models can already find large numbers of software vulnerabilities, even if they can’t find the most sophisticated vulnerabilities or exploit them as effectively as Claude Mythos Preview. Project Glasswing has already spurred many other organizations to take action on their own codebases with these generally-available models; we’re working to make this much easier to do.
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Project Glasswing: An initial update
22 votes -
Comment on Why Japanese companies do so many different things in ~finance
skybrian LinkFrom the article: [...] [...] [...] [...] [...] [...] [...]From the article:
But Toto’s remarkable year doesn’t have much to do with toilets or bidets. Toto might have been founded in the 1910s to “provide a healthy and civilized way of life” through affordable toilets, and in the decades since might have become the global leader in the bathroom game. But Toto also does a lot of other things. Toto manufactures not just bidets and toilets but also bathroom tiles, prefabricated bathroom modules, faucets, modular kitchens, photocatalytic coatings for buildings, and assistive equipment for the elderly. And, most importantly, Toto has a very lucrative sideline in the fabrication of memory chips.
Since 1988, in a once-obscure corner of the company called the “advanced ceramics division,” Toto has been producing a very particular component called the electrostatic chuck, or the “e-chuck.” The e-chuck is a sort of high-precision ceramic plate, about the size of a steering wheel, that uses electrostatic force to hold a silicon wafer perfectly flat and thermally stable while memory chips are etched into it with bombardments of plasma. Making these components is extraordinarily difficult, since the ceramic body needs to have near-zero particle generation and be polished to submicron flatness: and this means that there are only a few companies in the world that are capable of manufacturing e-chucks reliably. Almost all of them—Shinko Electric, NGK, Toto, Kyocera, Sumitomo Osaka Cement, Niterra—are based in Japan.
For most of its history, the advanced ceramics division was a rounding error on Toto’s balance sheet: the money maker, as it had been since the 1910s, was the toilet and bidet business. But we’re in a new era. Demand for AI is exploding, meaning that demand for the high-bandwidth memory that AI data centers require is exploding, meaning that demand for memory chips is exploding, meaning that demand for e-chucks is exploding. And so Toto’s advanced ceramics division is suddenly the company’s largest business, generating the majority of its operating profit. Toto’s leadership, suddenly awash in AI-driven revenue, announced that they would double down by investing hundreds of millions in expanded electrostatic chuck production: the toilet company had become, quite unexpectedly, a supplier to the semiconductor supply chain.
The Toto story is a fun and interesting illustration of corporate diversification and how strange bets can pay off. But that type of diversification—a toilet company that also produces photocatalytic coating and high-precision components for semiconductors—isn’t really unique to Toto. Practically every company in Japan seems to do a thousand very different things.
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Here is the answer I want to suggest: Japanese companies excel in lots of very different domains because it’s inherent in how they’re structured. The form of the corporation that we know and love in the United States—specialized, market-oriented, governed by shareholders—is just one form that the corporation can take; but it’s not the only way to coordinate capital and labor in a successful and profitable way. The protean corporations of Japan are best understood as a different species of thing altogether: better at some things, worse at others, but still highly adapted to their particular environment. And the things that they’re very good at turn out to be extraordinarily helpful for all sorts of things in which American companies tend to struggle.
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Here’s an illustration. Let’s say you run a factory. You decide that you want your lines to produce fewer defective goods: maybe you want to improve your yield from 95 percent to 98 percent. So you decide to invest in better training for your workers: maybe training now lasts six weeks instead of two weeks. This works, and now your yield is higher; but that change makes other things more attractive too. For example: now that your yield is higher, it makes sense for you to reduce your inventory, since fewer defects mean you no longer need a large buffer of spare parts to replace the bad ones. So now you’ve cut your inventory: but now it makes sense for you to shorten your production runs and switch more frequently between products, since without a mountain of inventory to work through you can afford to change what the line is making. And if you’re switching frequently between products, then it makes sense for you to invest in flexible, reprogrammable machinery instead of dedicated, single-purpose equipment. So one relatively small tweak shifts the entire calculus of what you do.
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So if we want to know why Japanese companies have one apparently unusual practice—why they’re so diversified into countless unrelated industries—we can’t really answer the question in isolation. We need to ask which bundle of practices they employ.
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And this means that Japanese companies strive to avoid financial pressure from outsiders. Relationships with suppliers are longstanding and entrenched: many Japanese companies have been working with the same suppliers for 50 years or longer. Outside investors seeking to interfere in this happy picture will find few avenues for influence. A standard Japanese firm’s board of directors is composed almost exclusively of the firm’s own senior managers; a large fraction of the firm’s equity is held not by outside investors but cross-held by other Japanese firms; and most of the firm’s financing comes from a single “main bank” that provides loans and monitors performance.
And as a result, Japanese companies don’t really try too hard to return profits to shareholders. Earnings are mostly reinvested, and investor dividends are kept low. For a long time, Japanese firms would spend as much entertaining the managers of other firms as they would on dividends to shareholders.
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And the complete Japanese bundle, I should say, ends up producing something with entirely different objectives and interests than the American bundle. The H-firm exists to make money, or rather to return money to shareholders; but the J-firm, run by its employees and largely indifferent to the interests of shareholders, exists simply to continue existing. That’s why Japanese companies are so protean and willing to change what they do. Nintendo was founded in 1889 as a maker of handmade playing cards; in the 1960s, it was pushed out of the playing cards game by a wave of competition; and it spent several years experimenting with new markets—taxi services and instant rice, though contrary to the rumors not love hotels—before finding its way to video games. Fujifilm, which faced a near-total collapse of photographic film in the 2000s, simply used its expertise in chemical coatings and fine optics to pivot into cosmetics, pharmaceuticals, LCD films, and semiconductor process materials.
And that basic impulse toward survival is why Japanese companies are so insistent on diversification. If you’ve made a commitment to keep people employed for life, then you need to create jobs for them if their current jobs stop making sense: indeed, you might need to keep them employed even if you can’t find anything for them to do. If you’re not very worried about profitability, and have lots of well-trained generalist employees, then it makes perfect sense to reinvest your company’s earnings by expanding into new industries: doing so not only allows your company to survive longer—your company’s portfolio of bets is now more diversified and thus lower-risk—but also ensures that you’re able to keep your surplus workers busy in one way or another.
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And this system, as it turned out, was really good at particular things. Aoki’s key insight was that the J-mode had a comparative advantage in environments of moderate volatility: situations where conditions changed frequently enough that rigid central plans would be outdated before they were executed, but not so radically that only top-down strategic intervention could cope. In an environment of stable, predictable demand, the H-firm did fine; in an environment of extreme disruption, where the whole product line had to be rethought, centralized authority was indispensable, and the H-firm also did fine. But in between—where the challenge was to make constant small adjustments in a changing but recognizable paradigm—the J-firm excelled.
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But catch-up growth, by definition, has to end: at some point you’ve caught up, and the challenge at the frontier is not only to refine what’s already known but to invent what is not known. And paradigm invention is precisely the sharp discontinuity for which the J-mode has no particular gift. Consensus-driven, horizontally coordinated organizations are very good at refining what already exists: but they are very bad at deciding what should exist.
That basic weakness is why Japanese firms are so dominant in some domains and entirely absent in others. Japan excels in automotive manufacturing, machine tools, industrial robotics, optics, and precision materials: domains characterized by incremental refinement. But they have very little to add in software, internet platforms, artificial intelligence, or electric vehicles. The architecture of the Japanese firm is built to perfect a domain through progressive advancement; it’s quite poorly suited to sharp discontinuity.
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Why Japanese companies do so many different things
34 votes -
Comment on Samsung chip workers to get $340,000 average bonus in AI boom in ~tech
skybrian Link ParentYou still benefit when the company has a good quarter and the stock goes up. It’s been almost a decade since I left Google, and I still benefit. Obviously, this has nothing to do with the work...You still benefit when the company has a good quarter and the stock goes up. It’s been almost a decade since I left Google, and I still benefit.
Obviously, this has nothing to do with the work that I actually did, but I don’t see how that matters.
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Comment on Samsung chip workers to get $340,000 average bonus in AI boom in ~tech
skybrian (edited )Link ParentIt's true that at a large company, the work that most people do day-to-day is unlikely to affect the stock price. But it does mean that when the company does well, you do well, so you can cheer...It's true that at a large company, the work that most people do day-to-day is unlikely to affect the stock price. But it does mean that when the company does well, you do well, so you can cheer when the company has a profitable quarter instead of feeling alienated because the company is making money and you're not.
Of course there are a lot of other ways that management can alienate workers, and layoffs will definitely do that. But profitable, growing companies can do other nice things for their employees too and send a consistent message.
It was before my time, but HP was once legendary for treating their employees well, and Google was that way too in the early years.
Another thing about Silicon Valley is that you know that even successful companies don't necessarily last. A physical sign of that was that Google's main campus was built on SGI's former headquarters. Facebook's old campus was formerly a Sun campus.
So, it was pretty obvious that those were the good times and that I should enjoy them while they lasted.
Nowadays the vibe towards tech companies is so negative that there are commenters on Hacker News trying to tell me I was exploited. Like, just no. There's a lot of injustice in the world and there are much better targets for your sympathy than rich retired tech workers.
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Comment on Samsung chip workers to get $340,000 average bonus in AI boom in ~tech
skybrian Link ParentSomething like this is common practice at Silicon Valley firms. Employees become shareholders via stock options and RSU’s and directly benefit when the stock goes up.Something like this is common practice at Silicon Valley firms. Employees become shareholders via stock options and RSU’s and directly benefit when the stock goes up.
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Comment on Waymo pauses Atlanta service as its robotaxis keep driving into floods in ~transport
skybrian Link ParentMaybe LIDAR has weird reflections off water? Whatever it is, I imagine they will take a few weeks or months to fix this and it will stay fixed. It doesn’t seem like it would be as difficult as...Maybe LIDAR has weird reflections off water? Whatever it is, I imagine they will take a few weeks or months to fix this and it will stay fixed. It doesn’t seem like it would be as difficult as kangaroos.
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Comment on Samsung chip workers to get $340,000 average bonus in AI boom in ~tech
skybrian LinkFrom the article: [...]From the article:
Samsung Electronics will distribute about 40 trillion won ($26.6 billion) in bonuses to chip division employees this year after striking a tentative agreement with its labor union, according to Bloomberg. Using the proposed terms and analyst projections for 2026 operating profit, Bloomberg calculated the average payout at 513 million won, the equivalent of about $340,000. The total average compensation across Samsung was 158 million won in 2025, per a company filing.
The agreement, subject to a union ratification vote running May 22 through May 27, calls for Samsung to direct 10.5% of operating profit into stock bonuses along with a separate 1.5% cash component, according to Bloomberg. The program runs for 10 years, contingent on the company meeting profit thresholds. One-third of the stock award can be liquidated right away, with the rest parceled out in installments across the next two years, Bloomberg reported. The first payout is expected in early 2027.
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The deal ended a standoff that drew intervention from South Korea's president, prime minister, and labor minister. A strike that shut down chip production could have cost the economy as much as 1 trillion won daily, with losses potentially multiplying to 100 trillion won if in-progress semiconductor wafers were rendered unusable. Samsung's shipments account for nearly a quarter of all South Korean exports.
Workers had pushed for bonuses tied directly to operating results and the removal of a cap that had limited payouts to half of annual salary. The union's original demand was for a bonus pool equivalent to 15% of operating profit. The settled rate of 10.5% was enough, in JPMorgan $JPM +0.34%'s estimation, to push Samsung's total performance-linked compensation to about 12% of operating profit for the year, Reuters reported.
Uh, go back and read the article again. It was not just raising valid concerns. It was a confident hit piece that said it was all bullshit using gleefully inflammatory language, going well beyond reasonable skepticism.