The Organization Mad -- Parody as historical snapshot.

I'd argue that looking at what people chose to mock often gives us a better take on contemporary attitudes than you get from standard sources. There's a tendency to look upon previous generations as being a little bit simple-minded. All too often, we assume that they unquestioningly believed what they were told and were oblivious to what seems obvious in retrospect. For this reason, subtle satire is often missed by current readers. Fortunately, excessive subtlety is not, and has never been, a problem with Mad magazine.

The second half of the '50s was a transition for Mad magazine. Its early years under Harvey Kurtzman had been distinctive and often brilliant but were largely a reflection of the editor's personality. When Al Feldstein took over, the magazine struggled for a while to find its identity before settling on a basic formula in the early '60s that would remain more or less constant for 40 or so years.

This transition period was arguably the least impressive; it certainly has inspired the least love from fans. But if you're looking for insights into the mood of the times, this might be the richest vein.

The title of this 1956 book (available on Internet Archive) was a play on the just-published The Organization Man, a highly influential critique of a perceived postwar loss of rugged individualism. This was a fertile field for satirists of the period such as Mort Sahl, Bob Newhart, and Stan Freberg.

The book is surprisingly text-heavy, including some pieces with almost no pictures, such as this one.

 

 

The writers and publications parodied were Westbrook Pegler, Louella Parsons, the Daily News, the Daily Worker, Hearst's Journal American, Time, and the New York Times. Of these, the most interesting from a historical point of view is Pegler.

Pegler may be forgotten now, but for those trying to understand the rise of the far right, he's worth taking a look at.

Francis James Westbrook Pegler (August 2, 1894 – June 24, 1969) was a Pulitzer Prize-winning American journalist described as "one of the godfathers of right-wing populism". He was a newspaper columnist popular in the 1930s and 1940s for his opposition to the New Deal, labor unions, and anti-lynching legislation. [Worth noting that Pegler was from  Minneapolis, in case you thought these positions were strictly a Southern thing. -- MP]

As an ardent proponent of states' rights, Pegler criticized a variety of targets whom he saw as extending the reach of the federal government, including Herbert Hoover, FDR ("moosejaw"), Harry Truman ("a thin-lipped hater"), and John F. Kennedy. He also criticized the Supreme Court, the tax system, labor unions, and any federal intervention on the issue of civil rights. In 1962, he lost his contract with King Features Syndicate, owned by the Hearst Corporation, after he started criticizing Hearst executives. His late writing appeared sporadically in publications that included the John Birch Society's American Opinion.

 ...

In the 1950s and 1960s, as Pegler's conservative views became more extreme and his writing increasingly shrill, he earned the tag of "the stuck whistle of journalism."  Despite having earlier called for the desegregation of baseball, Pegler denounced the civil rights movement and in the early 1960s wrote for the John Birch Society. He aligned himself with the white supremacist White Citizens Council.  He was ultimately expelled from the John Birch Society because of his extreme views. However, the Society did put his picture on the cover of its magazine, American Opinion, when he died. 

Mad's previous editor, Harvey Kurtzman, had gotten his start as a cartoonist for the Daily Worker, so this might be Feldstein's dig at a departed rival.  

I've long been fascinated by Hearst, so I'm the target audience for this one. 

Meta, Microsoft, and other tech giants are losing billions on AI. Can Apple catch up?

Steven Levy of Wired has been at this for a long time. In many ways, he might be the ultimate insider tech journalist. That means that even when his takes are bad, and this one certainly is, they're still interesting because they give us some insight into the way people in the tech industry are thinking.

Apr 24, 2026 11:00 AM 

Apple's Next CEO Needs to Launch a Killer AI Product

 

Sometime in the next year or two, Apple’s new CEO, John Ternus, will step onto a stage and tell the world that his company has a revolutionary product. This product, he’ll say, will put the full and awesome power of AI into everyone’s hands. It probably won’t represent a breakthrough in AI research, and it might not let people automate work or perform tasks any better than a lot of technically minded people are doing today. It may or may not involve a new device, though if it doesn’t, one should be in development. But if it all works out, that keynote will mark the moment when Apple did to AI what it has done for desktop computers, the internet, mobile technology, wearables, and music distribution. That is, it’ll offer a solution to a troublesome technology that’s so delightful and right that it seems obvious in retrospect.

This isn’t optional for Ternus. While AI is clearly the future and millions of people use it, even more are suspicious of it. Powerful new AI agent technologies such as Claude Code and OpenClaw are still too risky or technical for most people to adopt. If Apple doesn’t decode this for the masses, someone else will. Current CEO Tim Cook, who announced this week that he’ll vacate his role in September and become the company board’s executive chairman, has done a superlative job guiding the company after Steve Jobs, but he left this important box unchecked. Apple Intelligence, rolled out with much fanfare in 2024, was underwhelming and uncompleted.

The notion that companies must rush to embrace every technological next big thing or be consigned to the dustbin of history is an article of faith among techno-optimists. It hasn't been true in the past—there are countless exceptions—but the belief remains as strong as ever.

To get a full sense of the disconnect from reality here, consider what Levy is able to come up with in the way of actual AI products that Apple might offer its customers.

 That’s fine, but I look back to the mid-2000s when everybody was waiting for Apple to come out with a phone. When Jobs finally delivered in January 2007, the product defined the mobile era. It’s a big ask for Ternus to do something similar for the AI age—but it’s an opportunity that must be seized. AI threatens to disrupt the entire iPhone ecosystem. By the end of this decade, it’s unlikely that people will swipe on their phones to tap on Uber or Lyft. They will just tell their always-on AI agent to get them home. Or that agent will have already figured out where they need to go, and the car will be waiting without the friction of a request. “There’s an app for that,” may be replaced by “Let the agent do that.”

I am hard-pressed to think of something less in need of agentic automation than booking a rideshare. It's a process of three to five clicks, taking less than a minute in most cases. What's worse, the only way that an agent might streamline the process is by removing steps that most people would very much want to maintain control over, like reviewing the driver or selecting the type of car.

(Admittedly, there is a long Apple history of removing functionality in the name of innovation.)

Of course, there are real and important uses for large language model-based technology, but in the case of Apple, this seems to be someone trying and failing to come up with a problem for this particular solution. That's the story of far too much tech journalism in the 21st century.

Sixteen years ago at the blog: I'm not wasting time reading old paperbacks; I'm conducting serious research into Cold War political attitudes

  

Friday, May 7, 2010

Things I've learned -- 60s pulp edition

One of the pleasures of popular art is the glimpse it provides into the attitudes of the times. Sometimes this pleasure comes from the naivete of earlier times. More often, though, the experience is just the opposite -- you discover that the attitudes of previous generations were complex, surprisingly modern and often at odds with the conventional view of the era.

I recently read a couple of pulp novels from the Sixties that fell into the second category. One was The Thief Who Couldn't Sleep by Lawrence Block (discussed here). The other was The Ambushers, the sixth book in the Matt Helm series by Donald Hamilton.

Matt Helm was a counter espionage agent specializing in wet work, closer to a John LeCarre hero than to James Bond. The books were well reviewed (Anthony Boucher, writing, I believe, in the New York Times, said "Donald Hamilton has brought to the spy novel the authentic hard realism of Dashiell Hammett; and his stories are as compelling, and probably as close to the sordid truth of espionage, as any now being told."), they were extremely popular and they were sturdy enough to survive a god-awful series of in-name-only adaptations starring Dean Martin.

If you picked up a copy of the Ambushers in 1963 and turned to the first page, you'd find the narrator slipping into a Latin American country carrying a rifle with a high powered scope. The rebel leader he has been sent to kill may have had it coming, but it's not obvious that target was any worse than the leader the U.S. supported (later in the story, Helm is glad to hear that the latter has also been assassinated though his superiors most certainly are not).

The Russians in the Ambushers are sometimes enemies and sometimes allies, depending on the circumstances. The American agents generally hold the high moral ground but it's a distinction that can be rather thin and the protagonist has learned not to dwell on it too much. Everyone's hands are dirty.

As in many stories of the period, the threat nuclear war here comes not from either of the superpowers but from a third party. In this case, a Nazi war criminal who, not surprisingly, would like to see Russia and America destroy each other.

You will often hear the attitudes implicit in this story associated with the late Sixties and early Seventies, usually attributed to the escalation of the war and and the rise of a politicized youth culture, but this was 1963. It was early in the war and even the oldest of the boomers were still in high school.

If you surveyed the pop culture of the time you would find other evidence that a radical shift in the way we looked at the cold war took place in the late Fifties and early Sixties. Nuclear war was no longer likely to be depicted as a Pearl Harbor-style attack but rather as either a horrible mistake (Failsafe -- novel 1962, film 1964) or the work of a madman (Red Alert -- 1958/Strangelove* -- 1963) or the terrorist scheme of a third party (too numerous to mention). Anti-communist agents could be as morally compromised as the enemy (The Spy Who Came in from the Cold -- 1963). A sympathetic Russian was so acceptable that by 1964, you could have a loyal and openly communist Russian agent as a co-star in a popular spy show (albeit a Russian played by the Scottish David McCallum).

I've wondered if this shift was a reaction the Cuban Missile Crisis. Talking to those who were around at the time, I get the impression that for all the paranoia and anxiety and civil defense drills, the concept of nuclear war was never truly real to most people until 1962.

Good researchers in sociology or political could probably provide a rigorous answer to the question of what exactly drove the shift. I'd be interested in seeing what they came up with and if they need another topic after that, I have a whole shelf Gold Medal paperbacks for them to check out.

(You can watch Dr. Strangelove online here)

 

What we're talking about when we talk about the AI bubble

 

One. The AI stock bubble. Analogous to, but far larger than, the dot-com bubble. The main thesis here is that, regardless of the future economic impact of AI in general or large language models in particular, the current state of affairs around companies like Anthropic, OpenAI, xAI, and Meta is unsustainable, that there is virtually no chance of the industry becoming profitable enough quickly enough to justify the level of spending, especially associated with data centers, before bills come due and the current generation of chips becomes obsolete. The essential follow for this story has long been Ed Zitron.

Two. The AI hype bubble. Closely related to the AI stock bubble, with a strong symbiotic relationship. You could argue that one cannot exist without the other, but they are still very much distinct. Though it is sometimes couched in pseudo-technical terms, the hype bubble is largely magical, with claims ranging from the silly to the literally apocalyptic.

Three. Benefits and costs. If we limit ourselves strictly to those things that we know large language models are currently capable of, which ones are likely to be economically or socially useful, and which are likely to cause (or already have caused) substantial harm? Examples of the former include coding tools; the ability to extract information from text-based data at scale; and better interfaces for interacting with various devices. The latter includes (and this is a very partial list) creating severe mental health crises; polluting research papers, legal documents, and historical records with false information; degrading the quality of computer code and security systems; drowning original, high-quality writing and art in a sea of AI slop; crowding out potentially more valuable research; taking a huge toll on the environment; and allowing disinformation to be generated at an unprecedented scale.

It is worth noting that there is almost no overlap between the doomsters in the hype bubble and the critics raising the concerns mentioned here.

And sort of...

Four. Where do we go from here? This isn't really part of the bubble conversation, but it is adjacent. The current AI discourse is a profoundly dysfunctional. It is an ugly mass of hyperbole, misinformation, and badly framed conversations. Journalists covering the topic, more often than not, have no understanding of what they're talking about. Statements from the companies developing these models are, at best, based on questionable metrics and assumptions, and, at worst, are borderline lies. Much of the research (arguably most of what makes its way into The New York Times or The Atlantic) is sensationalistic, anecdotal, and terribly thought out.

But we can't and shouldn't simply dismiss large language models and their potential for positive impact. They represent the biggest breakthrough we've ever seen in natural language processing. The changes they have wrought to the way we code are already substantial and will only grow bigger. Our ability to analyze and classify text at scale will have huge implications for countless fields of study.

All of this means that it is imperative that we improve our understanding of this technology and have a serious, productive conversation about how best to use it while limiting the damage it can do. Unfortunately, that's not the conversation we are having.

Generational fairness

This is Joseph.

One of the recent questions that we have had to deal as a society is the way that housing price appreciation has concentrated wealth in older cohorts. This is not a small point as programs like social security are getting more expensive as the population ages and part of the rationale has always been anti-poverty. 

But the worst type of proposal is often been seen in Canada. Where you reform the mechanisms to hurt the generation that has not benefited from housing appreciation. 

Patrick Boyle addresses this generational problem in this video. 

I think that the same issue is more generally an issue across a lot of countries trying to balance generational issues. I admit, as Gen X, I am just assuming now that the systems I was hoping to be used will be trashed by the time I get there. Which is unfortunate. 

And it isn't a Canada-only problem:

At some point there probably needs to be a hard conversation about some of these decisions as the pressure mounts. 

I apologize for calling AI a bubble. It is actually a bouncy house, and right now everybody's listening to the blowers.

Last Monday, Allison Morrow wrote the following

Buckle on up: Alphabet, Amazon, Microsoft and Meta all report earnings on Wednesday — that’s about 19% of the S&P 500 by market cap — are all reporting after the closing bell, just a couple of hours after what is expected to be Powell’s swan song of a press conference announcing the Fed is holding rates steady yet again. Just 24 hours later, Apple also reports earnings. 

 

Of course, we don’t have a crystal ball to know what’s in those reports or how investors will react to them. But here is some important context: Wall Street’s AI fever appears to be back (if it ever went away, and we’ll get to that in a minute), and that makes these particular tech earnings much less about money coming in than money going out. 

 

Investors will be laser-focused on capital expenditures, aka how many dump trucks full of money the companies are committing to their AI buildouts. The “Magnificient 7” stocks that have been propping up the broader market — Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia and Tesla — are expected to raise their combined capex by 30% from last year to at least $680 billion in 2026. 

 

“If they say, 'we're going to continue spending at the pace we’ve been spending, or a faster pace,' that sort of vindicates the the crazy move we've just had in the SOX index,” Steve Sosnick, chief strategist at Interactive Brokers, told me. 

 

ICYMI: There’s a gauge called the Philadelphia Semiconductor Index, or SOX, that tracks the 30 largest US-traded semiconductor companies (like Nvidia, Advanced Micro Devices, Qualcomm, etc). That index has been on a tear, shooting up 45% in just four weeks. Why? Eh, why not. 

 

Strong earnings and positive forward guidance from a few key players helped, but “no new fundamental or technological development justified re-rating the group nearly 50% higher in the span of a month,” Mike O’Rourke, chief market strategist at JonesTrading, said in a note Sunday.

 

“We talk about bubble valuations and the market's pricing mechanism being broken — behavior like this clearly reinforces that thinking.”

Marketplace's analysis of the day's trading made the same "less about money coming in than money going out" point.

The AI landscape is only able to maintain its shape through a constant infusion of cash through endless funding rounds, mountains of debt, and, most importantly, spigots of money from some of the biggest and most profitable companies in the world, such as Microsoft and Alphabet/Google.

If investors truly believed that things were about to turn around and this bottomless money pit was about to become a gusher (sometimes when you get on a metaphor kick, you just can't stop yourself), they shouldn't be that excited one way or the other about whether or not Microsoft et al. keep the air pumps running. Instead, the mentality seems to be: how long do we have until the music stops?

As with so many things, it sounds better when Jeremy Irons says it.

Anthropic's Mythos: One moderate step forward for white-hat hacking; one giant leap forward for journalists' credulity

[I kid, of course. That particular shark was jumped ages ago.] 

You should probably approach any story of large language models displaying initiative, or trying to mislead or blackmail users, or generally doing anything of the sort with the same mindset you approach accounts of paranormal activity. In both cases, virtually all the reporting will be sensationalistic, anecdotal, and likely to collapse under scrutiny.

Example du jour, Anthropic is getting an enormous amount of sky-is-falling coverage over what appears to be the development of a good but hardly revolutionary white-hat hacking tool. 

Here's Gary Marcus's assessment:

To a certain degree, I feel that we were played. The demo was definitely proof of concept that we need to get our regulatory and technical house in order, but not the immediate threat the media and public was lead to believe. 

Not only has the reporting been credulous and incurious, it has largely ignored the ever-present elephants in the room when discussing OpenAI, Anthropic, etc.

Cal Newport follows up:

Since Marcus published his essay, I’ve come across several more similar findings:

• The AI security expert Stanislav Fort ran ​an experiment​ to see if existing, cheap open-weight models could find the same vulnerability in FreeBSD (an open-source operating system) that Anthropic touted as evidence of Mythos’s scary abilities to uncover bugs that had been hiding for decades. The result: all eight existing models they tested discovered the same issue.
• Meanwhile, the renowned security researcher Bruce Schneier ​weighed in​, similarly concluding: “You don’t need Mythos to find the vulnerabilities they found.”

And of course, it doesn’t help that a week before Anthropic released this supposedly super-powered vulnerability detector, they accidentally leaked the Claude Code source, and security researchers immediately found ​serious vulnerabilities​. (I guess Anthropic forgot to use Mythos to clean up their own software…)

Journalists covering this story need to constantly remind themselves that hundreds of billions of dollars, possibly even trillions, are at play here. What's more, the constant flow of funding that keeps this game going appears to be drying up, making this the highest-stakes game of musical chairs ever played. One of the key motivators that has kept the music going this long has been the carefully promoted belief that the end of the world is possibly days away and the only thing that can save us is if the good wizard discovers the incantation before the bad wizard does (at the risk of putting too fine a point on it, the bad wizard here is China).

Software developer Carl Brown of the Internet of Bugs has a good take. In particular, pay close attention to the part about Responsible Disclosure. 

Brown got on my radar through this excellent discussion with Ed Zitron, Over an hour but well worth the time.

More context from Devereaux

Not to put too fine a point on it (and avoiding the question of where regular authoritarianism ends and fascism begins), this highly recommended essay from Bret Devereaux addresses the question of how the Trump administration has managed to screw up the war in Iran on such a spectacular, arguably unprecedented level. While the United States has had military disasters before, I can't think of one so unnecessary or foolish.

The cartoonishness of Hegseth certainly highlights the argument that the inadequacies and emotional needs of fascists make them highly unsuited for the roles they see themselves in. Anytime the military leaders of a country are primarily concerned with making themselves look powerful, aggressive, and hyper-masculine, the resulting strategies will be a disaster. And since this personality type is almost by definition incapable of admitting errors, let alone serious introspection, these disasters will only grow worse with time. 

 

From (On the Military Failures of Fascism) by Bret Devereaux  

I imagine I am missing other near-fascist regimes, but as far as I can tell, the closest a fascist regime gets to being effective at achieving desired strategic outcomes in non-civil wars is the time Italy defeated Ethiopia but at such great cost that in the short-term they could no longer stop Hitler’s Anschluss of Austria and in the long-term effectively became a vassal state of Hitler’s Germany. Instead, the more standard pattern is that fascist or near-fascist regimes regularly start wars of choice which they then lose catastrophically. That is about as bad at war as one can be.

We miss this fact precisely because fascism prioritizes so heavily all of the signifiers of military strength, the pagentry rather than the reality and that pagentry beguiles people. Because being good at war is so central to fascist ideology, fascist governments lie about, set up grand parades of their armies, create propaganda videos about how amazing their armies are. Meanwhile other kinds of governments – liberal democracies, but also traditional monarchies and oligarchies – are often less concerned with the appearance of military strength than the reality of it, and so are more willing to engage in potentially embarrassing self-study and soul-searching. Meanwhile, unencumbered by fascisms nationalist or racist ideological blinders, they are also often better at making grounded strategic assessments of their power and ability to achieve objectives, while the fascists are so focused on projecting a sense of strength (to make up for their crippling insecurities).

The resulting poor military performance should not be a surprise. Fascist governments, as Eco notes, “are condemned to lose wars because they are constitutionally incapable of objectively evaluating the force of the enemy.” Fascism’s cult of machismo also tends to be a poor fit for modern, industrialized and mechanized war, while fascism’s disdain for the intellectual is a poor fit for sound strategic thinking. Put bluntly, fascism is a loser’s ideology, a smothering emotional safety blanket for deeply insecure and broken people (mostly men), which only makes their problems worse until it destroys them and everyone around them.

This is, however, not an invitation to complacency for liberal democracies which – contrary to fascism – have tended to be quite good at war (though that hardly means they always win). One thing the Second World War clearly demonstrated was that as militarily incompetent as they tend to be, fascist governments can defeat liberal democracies if the liberal democracies are unprepared and politically divided. The War in Ukraine may yet demonstrate the same thing, for Ukraine was unprepared in 2022 and Ukraine’s friends are sadly politically divided now. Instead, it should be a reminder that fascist and near-fascist regimes have a habit of launching stupid wars and so any free country with such a neighbor must be on doubly on guard.

But it should also be a reminder that, although fascists and near-fascists promise to restore manly, masculine military might, they have never, ever actually succeeded in doing that, instead racking up an embarrassing record of military disappointments (and terrible, horrible crimes, lest we forget). Fascism – and indeed, authoritarianisms of all kinds – are ideologies which fail to deliver the things a wise, sane people love – liberty, prosperity, stability and peace – but they also fail to deliver the things they promise.

These are loser ideologies. For losers. Like a drunk fumbling with a loaded pistol, they would be humiliatingly comical if they weren’t also dangerous. And they’re bad at war.

 

Not one of Boyle's funny ones, but we do get the unlikely pairing of Zippy Duvall and (not the) Pablo Escobar.

While I don't want to downplay the importance of the rest of the video—you should listen to the whole thing—I did want to single out this one quote from the Financial Times.

Just to be clear, what is being labeled Trump derangement syndrome is analysts reacting normally to bad news. The argument here is that the people who say we should price in that news are the ones who are deranged.

I have long had the suspicion that the Trump cult of personality was influencing investors, particularly retail investors. There has long been an overlap between the Silicon Valley alt-right and the diamond hands/"we are the Spartans" traders on Robinhood. 

 

It is reassuring, if not comforting, to have these impressions backed up by reporting from a source as credible as the FT.

This is also another reminder that with a Keynesian beauty contest, irrational behavior can become rational, at least for a time, if enough people do it. In this case, buy-the-dip crowds have created a self-fulfilling prophecy that the market will always bounce back almost immediately from the worst news. Investors who try to base their positions on reality are punished; those who don't are rewarded. Paired with the faith that there will always be a TACO, this has created a profoundly dysfunctional market, which is particularly bad given that stock prices are one of the few things that have previously kept Trump in line.

Admittedly, I'm no expert, but I don't believe that the laws of financial gravity have been repealed. At some point, reality will win. I also believe in that bit of folk wisdom that says the higher you climb, the further you fall.

I'm sure someone at the studio at some point described this project as Colossus meets Rosemary's Baby

I haven't seen Demon Seed in who knows how many years. I remembered it being well done, and the ending certainly stuck with me, but until I saw this video, I hadn't given much thought to the film or to how well the major elements had aged. If you remade the film under a different title, I doubt many viewers would suspect that the source novel was over 50 years old.

The Tragic Betrayal in the Editing Room of Demon Seed (1977)




It's important to note not just how closely Demon Seed tracks with current narratives about AI, but how well established all of these elements were at least a decade before the movie was made. A supercomputer had taken over the world in Colossus. The original Star Trek (which was always a reliable reflection of the popular sci-fi of the postwar era) had featured multiple rogue AIs and at least one episode where the plot hinged on deepfakes. We won't even try to cover I Have No Mouth, and I Must Scream and all of the other notable examples from '50s and '60s short stories and novels.

At the risk of oversimplifying the matter and ignoring more than a few exceptions, it is generally safe to say that the idea of what we now call the future emerged in the late 19th and early 20th centuries and reached its final form in the quarter century following the end of World War II. What's perhaps most remarkable about this is how static, some would even say stagnant, the perception of the future has been over the last 50 years. Except for some cyberpunk trappings, the old visions of tech visionaries almost invariably consist of flashbacks to the Eisenhower administration. 

Ten years ago at the blog when we still considered Watergate the pinnacle of political corruption

There was a comment on the YouTube page that I meant to add to this post but apparently never got around to.

 

Kirsch was a grad student when he co-produced the re-edited speeches that made up the first side of the album. It appears that he did not have a serious appetite for cocaine because he left the worlds of the recording industry and National Lampoon to finish his degree and go into academia. 

He was last seen on the faculty of the Harvard Medical School. 

 

 

Thursday, March 17, 2016

The Missing White House Tapes -- you can find everything on Youtube 

 A friend I grew up with was a big fan of comedy albums. One of his favorites was National Lampoon's Missing White House Tapes.

The Missing White House Tapes was a sketch comedy voice recording which was a satiric commentary on the Watergate scandal. It was a spin-off from National Lampoon magazine. The recording was produced by Irving Kirsch and Vic Dinnerstein. It was released as a single on Blue Thumb Records in 1973. In 1974 it was expanded into an album, which was subsequently nominated for a Grammy Award as Best Comedy Recording of the year.

The single consisted of a doctored speech, in which Richard Nixon confesses culpability in the Watergate break-in. Side One of the album contains additional doctored recordings of Nixon's speeches and press conferences. Side Two contains sketches performed by John Belushi, Chevy Chase, Rhonda Coullet, and Tony Scheuten.

I hadn't thought about, let alone heard the record for years, but recently something (I don't remember what) reminded me of it. A quick Google search later...





I made some notes for a post I might actually write up one of these days but, in the meantime, for those interested in the period it's a fascinating relic (not to mention a reminder that the National Lampoon brand used to be associated with humor).

More thoughts on predictive markets

From Patrick Boyle's recent video overview of prediction markets.

If you are a retail trader who’s tired of losing money to hedge fund algorithms, you might be tempted to look for an edge.

In traditional financial markets, acquiring non-public material information and trading on it will generally result in a conversation with the authorities and eventually a lengthy stay in a federal facility.

In prediction markets, however, insider trading is often described by proponents as a feature, not a bug.

That’s right—a feature.

If we look at recent events, this feature appears to be working remarkably well.

Last summer, a Polymarket user operating under the pseudonym Rico Suave 666 made a series of highly precise and highly lucrative bets regarding the exact timing of military strikes in the Middle East.

It later turned out that Rico Suave 666 was not just a very astute reader of geopolitical tea leaves. The Israeli government arrested two men, including an army reservist, for allegedly placing bets using classified military intelligence.

So to be clear, a soldier with advance knowledge of when bombs were going to be dropped used that information to win money on what is essentially a crypto gambling website, which is not really what people have in mind when they talk about the wisdom of crowds.

 One of the most annoying but absolutely on-brand comments to come out of the prediction market industry is that they are creating "truth machines." That combination of the pretentious and the portentous is exactly what we've come to expect from tech visionaries, along with the inevitable moderately dishonest framing.

Truth is a big word that brings with it volumes of connotations, used here in the narrowest possible sense to mean an estimate is more accurate (given that a large part of the blog's readership consists of statistics professors, I should probably acknowledge that even that grossly oversimplifies what we're talking about here).

"Truth" also puts a massive thumb on the rhetorical scales. Who could be against truth? Hell, it's part of Superman's credo, for crying out loud.

But the dishonesty really comes to a head when the people behind these markets argue in defense of insider trading. The whole point of the magic of the market is that it brings together a large number of guesses that have been weighted according to strength of belief. (There's also a good chance that some of the people reading this are economics professors, so, once again, I apologize for this sub-layman's summary.)

If one of the investors/bettors knows the outcome in advance, it's no longer magic; it's the cheap trickery of a fake psychic checking the mark's wallet while he's in the bathroom.

Insider trading is not itself a social good for at least a few reasons. For starters, the very concept of being an insider is heavily correlated with being someone with power, connections, and money. Allowing people to profit off of that status is, with respect to investors, effectively a tax collected from the have-nots and paid to the haves.

But it gets worse. Having mechanisms in place where inside information is valuable creates an incentive to create more inside information, or, put another way, to keep information on the inside that would normally get out. Let's take the example of weather forecasting, one of those cases where there is a clear social good associated with knowing the answer in advance. If a researcher with NOAA can get rich by plugging non-publicly available data into their models, they have every reason to keep as much data as possible out of the public domain. The net effect of this would be less, not more, "truth." Making data publicly available allows for more calculations, more research, more models. Our ability to make predictions based on government data which, in theory, belongs to all citizens is diminished, not increased.

This gets at the absurdity of the pro-insider argument: it doesn't even make sense on its own terms. Compared with other ways of getting information out there, insider trading is perhaps the least effective way of making prediction markets more accurate. In all but the most thinly traded of markets, insider information will only slightly move the needle, while by comparison taking that same information and releasing it publicly through the press or through the government would have a far greater impact on the value of those trades. In other words, insider trading only improves predictive power if the information doesn't come out through other means. Otherwise, it actually reduces the level of "truth."

Add to that the potential for gaming the system, either trying to influence events to make a bet pay off or trying to influence the prediction markets to game events.

The people at Kalshi and Polymarket argue for insider trading because they have to, because they have no real way of stopping it, and if people have to acknowledge how terrible the practice is, the only logical next step is strict regulation.

Finally, and perhaps most importantly, we have to ask ourselves what the social good of accurate predictions is in specific contexts. There are cases where knowing the future is a wonderful thing. We previously mentioned weather. This is an area where an accurate forecast is beneficial in any number of ways, from safety to resource allocation to infrastructure. Here in the West, knowing when to top off the reservoirs and when to plan for wildfire conditions would be of tremendous value. On the East and Gulf Coasts, having advanced warnings of hurricanes might be even more important.

But there are any number of events where "truth," in the sense of knowing the outcome in advance, is of no social good whatsoever. Sporting events obviously fall into this category. I would argue that general elections do as well, though I might get some pushback on this one. Just to be clear, this objection includes both polls and prediction markets, though the social costs associated with the latter are considerably greater. While there is a social good associated with polling in a primary—it can help party members converge on a candidate who is both acceptable and electable—all that prediction models do in general elections is tell the public who the public is about to choose for an office. (We'll get back to that one.)

Prediction markets, humanoids, LLMs.  It's the same scam. The boosters pitch their shiny new innovation has a magical machine that can do anything or tell us anything and we're all supposed to be so entranced that we don't ask inconvenient questions about how good a job it does or how it works or what it costs.  

 

Patrick Boyle explains why it's illegal to bet on the price of onions but it's OK to bet on the Dodgers' double header

One of the best overviews I've seen of the strange world of prediction markets, told with Boyle characteristic dry wit. I've included some excerpts to give you a sense of the essay, but the thing is quotable. 





Coincidence

You might expect the federal commodities regulator to step in at this point and clarify that a bet on the New York Knicks is not, in fact, a vital financial derivative. But they have not.

According to the Financial Times, the CFTC has mostly just been avoiding the question. Well, it’s actually a bit worse than that. Under the new administration, the CFTC and the Department of Justice have gone to federal court to block the state of Arizona from enforcing its gambling laws against Kalshi.

So, the federal government now appears to be deploying its legal resources to defend a tech platform’s right to operate what Arizona considers an unlicensed sportsbook, overriding state law in the process.

Whatever your views may be on prediction markets, you have to agree that this is a rather unusual use of the Department of Justice’s time.

Now, if you’re wondering why the new administration might be so accommodating to these prediction platforms, there’s one small detail that’s probably worth mentioning. A fellow named Donald Trump Jr., who seems to be some sort of relative of the sitting president, is currently serving as a strategic adviser to both Kalshi and Polymarket.

I looked up this fellow’s background, and he appears to have no real work experience in either strategy or advice. He seems to be a reality TV star who also worked for his dad’s real estate company.

I can’t think of why they hired him, but I suppose it’s still worth noting that the president’s son advises the companies that the federal government is currently shielding from state prosecutors.

I’m sure that it’s all a coincidence.

The new crypto

So, if prediction markets are not entirely reliable as truth machines, what are they actually for?

To understand the current boom, it helps to look at the broader shift in retail investing over the last few years. Dimitri Kofinas of the Hidden Forces podcast uses the term financial nihilism to describe what’s been going on.

The idea is that traditional paths to building wealth feel increasingly out of reach for a lot of young people. So instead of saving and investing carefully, they try to get rich quickly by putting money into crypto tokens featuring pictures of dogs that were pitched to them by edgy billionaires, or by buying shares in bankrupt companies.

Prediction markets slot in perfectly here.

If you go back five years, crypto was the exciting product that everyone was talking about. But crypto is kind of dull today. Bitcoin is up about 25% over five years, which sounds okay until you realize that a money market fund paying 4% with no risk at all would have gotten you most of the way there.

Your dad has achieved triple the return of Bitcoin over the last five years with his index fund. And he didn’t have to check his phone at 3:00 in the morning or pretend to understand what a layer-two rollup is.

 

 Sharks and Fish

The problem with all of this is that whenever a large pool of enthusiastic retail money shows up somewhere, the professionals are usually not far behind.

According to the Financial Times, large quantitative trading firms like Susquehanna and DRW—firms that normally act as market makers on stock exchanges—are now setting up dedicated prediction market desks. They’re reportedly paying traders base salaries of $200,000 a year to build algorithms that systematically identify mispriced contracts on these platforms.

So, on one side of the trade, you have a person betting on the Super Bowl because it seemed like fun, and on the other side, you have a machine that does this 24 hours a day and never gets excited about anything.

This brings us to what the gambling industry calls the sharks and fish problem.

In the early 2000s, there was a huge boom in online poker. Millions of amateurs—the fish—logged on to play. But it didn’t take long for the professionals, or the sharks, to show up. The professionals didn’t play for fun. They played the odds methodically, and eventually they deployed bots to do it for them around the clock.

The survival time of a new recreational player on these sites was eventually reduced to not very long. The amateurs worked out that they were no longer really playing a game. They were donating their money to a server farm in New Jersey. They stopped logging in. The liquidity dried up, and the whole ecosystem collapsed. The sharks had eaten all of the fish and then starved.

Today, prediction markets are full of retail money and the platforms are growing quickly. But unlike trading a meme stock, where the price is just whatever the next person is willing to pay, an event contract eventually resolves to either true or false. There is an actual answer.

And if you’re a retail trader betting on a geopolitical event based on a feeling, and the person on the other side of your trade is a gamma-neutral algorithm being run by a multi-billion-dollar hedge fund, the odds are not in your favor.

This is not a skill gap that can be closed by doing more research. It’s a structural disadvantage.

  

 

Wonderful except...

So when you look at the mechanics of the whole thing, prediction markets start to look less like a truth machine and more like a wealth transfer mechanism.

The platform takes a transaction fee. The quantitative algorithms extract capital from retail bettors. The insiders extract capital from everyone, and society picks up the tab for the bankruptcies and the unpaid bills.

It’s a wonderful business model for everyone except the people using it. 

We need to talk about this clip.

Yes, these videos are fun, and yes, the engineering behind these machines is impressive. That said, this and all those other gee-whiz, look-at-the-cool-robot stories are leaving out key context and often doing real damage. 

A humanoid robot won a half-marathon in Beijing in 50 minutes and 26 seconds, finishing faster than Ugandan runner Jacob Kiplimo's world record. Read more: https://cnn.it/4sGl276

[image or embed]

— CNN (@cnn.com) April 19, 2026 at 2:51 AM

The practical value of these demonstrations is exactly the same as that of a high school competition to build Rube Goldberg machines. Hopefully, someone involved will apply something they've learned to something of actual use.

But where's the harm in having some fun with robots? To answer that we have to dig into hype and market bubbles and everybody's favorite real life Tiny Stark.

At this point, I'm not sure I even need to argue the idea that Elon Musk is a horrible person. He has accumulated the world's largest fortune almost entirely through stock pumps and shady accounting. The damage he has done through his involvement with Trump will take decades to calculate, as will his support of neo-Nazi movements here and in Europe. Not to mention that he has a messiah complex, he's a terrible engineer, and he's just annoying.

I could fill (and have filled) pages listing the crimes and embarrassments of Elon, but most readers, and well-informed people in general, would probably already know most of the highlights. What fewer people realize is how Musk's fortune now rests largely on the narrative this video helps support.

Though the absurd one- to two-trillion-dollar valuation of the money-losing SpaceX complicates things, in terms of even semi-liquid assets, virtually all of Elon Musk's wealth comes from Tesla stock. This is a company with a P/E ratio of over 372 at the moment, despite the fact that sales of the company's automobiles are cratering (even worse than we thought now that we've learned that the biggest purchaser of the Cybertruck appears to be other Elon Musk companies). Add to that Waymo beating Tesla's robotaxi service like a red-headed stepchild, and, even with decent sales of power packs, you have a stock that would drop by close to two orders of magnitude if priced on fundamentals.

The primary justification for considering Tesla by far the most valuable car company in the world is the absolutely insane belief that the company is about to usher in a golden age of abundance thanks to its Optimus humanoid robot. This isn't just unsupportable bullshit; it is layers of unsupportable bullshit.

The future of labor may very well belong to robots (it certainly does in space, but that's a topic for another time), but it almost certainly does not belong to bipedal humanoid robots. Musk, whose ideas about the future are derived almost entirely from juvenile postwar science fiction, deserves, for lack of a better word, "credit" for the current humanoid bubble. Billions of dollars are being poured into designs that virtually every roboticist not on the payroll will tell you are a technological dead end. The use cases for these C-3PO knockoffs are vanishingly small. In the vast majority of cases, wheels or treads beat legs, and where legs are called for, "four legs good; two legs bad."

There's no way that the total addressable market for humanoid robots justifies a trillion-dollar-plus company. What's worse, even within that niche field, Tesla probably isn't even in the top five. It certainly can't match the technology of Boston Dynamics, which is primarily owned by the profitable car company Hyundai, and yet the market cap of the former is more than 10 times that of the latter.

The humanoid bubble is bad engineering crowding out good tech and propping up the fortunes of bad, dangerous people. The sooner it pops, the better. 

ET reads the NYT

Yes, I am going to keep bringing that up.

XKCD 2953: Alien Theories

 (Now that Gelman has poked this bear again, we'll have a few more X-Files posts coming up in the near future.)