A camera doesn’t preserve truth—it translates it. It bends light, flattens depth, and freezes a moment that vanishes the instant the shutter closes. “The camera adds ten pounds” isn’t a joke; it’s a confession. The machine that built our modern idea of realism has always lied a little. We just got used to the lie.

Photography was the first technology that simulated reality at scale. It startled the 19th century as much as AI startles us now. Painters feared extinction; philosophers worried we’d lose touch with the “aura” of the original.

But the camera became our new oracle of truth. The photograph wasn’t real. It just became the reality we chose to believe.

The simulation deepened from there. Film, television, and digital video re-engineered our sense of what’s real. Actors look taller on screen than in person; anchors are slimmer; voices are equalized; color and light are manipulated. Nobody calls these distortions “misinformation.” We accept them as style.

AI now steps onto the same stage, doing what cameras have always done: turning perception into pixels. Yet suddenly, people panic. Deepfakes. Fake humans. Synthetic reality. We talk as if the world were pure before the algorithm arrived. But every medium reshapes its subject. AI is simply more honest about it.

The discomfort isn’t that AI distorts reality. It’s that it exposes how much of our reality was always distorted. Photography industrialized the image. Film emotionalized it. Television monetized it. Social media personalized it. AI just automates it.

When a model creates a human face that never existed, it’s not inventing fakery—it’s industrializing imagination. The line between representation and reality didn’t break this decade—it broke the moment someone first called a photograph ‘proof.’

So maybe the question isn’t whether AI images are real or fake. It’s why we keep pretending photography ever was either. We’ve spent a century calling our favorite distortions truth. AI isn’t ending that illusion. It’s just holding up a better mirror.

Before the weekend was sacred, it was strategy.

A century ago, most Americans worked six days a week. Then, in 1926, Henry Ford gave his factory workers Saturday off with full pay. He wasn’t being generous; he believed that rested workers would be more productive, and that with an extra day of leisure, they might actually use their cars. The strategy worked. By 1938, the Fair Labor Standards Act made the 40-hour work week the U.S. national standard, and the weekend became a cornerstone of modern consumer life.

What started as an economic calculation became something more like a social contract. By the 1950s, the weekend had evolved into the reward at the end of labor—a space for backyard barbecues, Little League games, and the kind of family time that justified the rest of the week. Sunday morning meant church; Saturday night meant freedom. To work through the weekend became a sign of either burning ambition or quiet desperation. The weekend wasn’t just time off; it was proof you’d made it. Suggesting we rethink it would have seemed as radical as questioning the 40-hour work week itself once did.

Today, a hundred years later, the logic behind that system is starting to collapse. COVID broke the link between productivity and presence. Remote work killed the daily commute. AI is shrinking the need for human hours. Yet despite all our computers, AI, apps, wifi, and coffee shops, we in the U.S. largely still cling to a rhythm designed for assembly lines and punch clocks. But internationally, work week experiments have already been rewriting that rhythm.

In Iceland, between 2015 and 2019, 2,500 workers cut their workweeks from 40 to about 36 hours with no pay loss. Productivity held steady or improved, and burnout dropped sharply. By 2022, nearly 60 percent of Iceland’s workforce had moved to shorter or more flexible hours. The U.K. ran a similar trial in 2022 with 61 companies and 2,900 employees: After six months, 92 percent of firms kept the new schedule, reporting a 71 percent drop in burnout, 65 percent less sick leave, and 57 percent fewer resignations. In Japan, Microsoft tested a four-day work week in 2019 and found productivity per worker jumped 40 percent, while electricity use fell by 23%.

The data are consistent: shorter weeks do not hurt output, but they do heal people. And generally speaking, the formats are flexible. Some firms do four ten-hour days, others go to 32-hour weeks with no pay cuts, and others rotate off-days to keep coverage steady. The point is not working fewer hours, but smarter ones.

Once you question the workweek, other inherited systems start to look suspicious, too. Why do K-12 schools still run on agrarian calendars? Why is health insurance tied to full-time employment? Why does traffic surge at 5 p.m. in a world of remote work?

The five-day work week was one of the 20th century’s great design achievements, a pact between capital, labor, and consumption that built the modern world. But like the Model T that inspired it, it belongs to another era. If the weekend was invented to make people move, maybe the next revolution is to let them stop.

The ancient gods were pranksters. Loki, Hermes, and Krishna mocked mortals to teach them wisdom. But the new digital gods we’re building don’t have the same sense of humor. AI is getting smarter by the day, but it still can’t read the room.

Ask for one specific thing, and it follows you around like an overeager intern, constantly offering more help. “Here’s my analysis of that bump on your forearm. Would you like me to explain what happens at the ER? Or tell you where to buy Advil within a mile of your house?” If I wanted neediness at scale, I wouldn’t have deleted my Twitter account.

Meanwhile, the industry races toward something grander. Meta has merged its AI projects into a new Superintelligence Labs division, pouring billions into the hunt for artificial general intelligence and dangling enormous packages to poach elite talent from competitors. Other leaders like Sam Altman at OpenAI promise systems that will out-reason, out-plan, and out-create the smartest humans alive within a few years. But while my chatbot can list endless variations on a dry martini, it can’t comprehend a dry sense of humor.

Recently, I asked ChatGPT about the Golem, a manmade figure animated by words and willpower in Jewish folklore, and a recurring metaphor for AI, and received a thoughtful answer. But then I referenced the Golem joke in Inglourious Basterds and, sarcastically, asked if Quentin Tarantino himself might be a golem. Instead of laughing or even noting the absurdity, it delivered a four-part analysis of why Tarantino might embody “Jewish mysticism on screen.” A college student would have rolled their eyes at my bad joke. The genius machine leaned into the ask.

These lapses aren’t just quirks. Humor, sarcasm, and reading the emotional temperature of a room are deeply human skills that rely on context, culture, and empathy. AI can pass tests and assemble data, but it consistently flunks social cues.

What happens when we hand that kind of intelligence the keys to everything?

In medicine, a superintelligent diagnostic system might optimize treatments while missing that a patient is too ashamed to describe their real symptoms. In finance, AI could execute panic-driven trades that a human would have talked someone down from. It might approve insurance claims from smooth liars while denying nervous, honest people. It could even escalate military conflicts, WarGames style, because it can’t tell when an adversary is bluffing or begging for a face-saving exit.

We’re investing trillions to build gods that can solve impossible math problems but can’t read a room. Until machines can grasp tone, humor, and intent, the smartest systems will remain blind to the something that truly defines intelligence: knowing when to lean in and when to back off.

Before AI learns to rule the world, it should probably learn to take a joke.

We think of ChatGPT and its siblings as “cheap” — free in light use, or a modest subscription. But behind the scenes, billions are burned on data centers, cooling, chips, energy, and staff. Even a single response carries non-zero compute costs: one estimate puts ChatGPT’s hardware bill at about $694,000 a day, roughly 0.36¢ per query. The cost of serving queries accounts for 80–90% of total costs. Subscription fees alone can’t realistically absorb all of that. There are practical ceilings on what consumers or enterprises will pay monthly.

So what’s the alternative? Subscriptions can’t cover it, and prices can only rise so far. Which leaves the same playbook Google used for search: advertising. In the near future, we may even see “sponsored intelligence” or “branded intelligence,” where parts, or all, of an AI answer are explicitly or implicitly funded by sponsors. Think “This answer is brought to you by Ford,” or “Sponsored result: General Mills recommends the following for a nutritious breakfast.”

But once answers are sponsored, bias becomes baked into the system. Will sponsored content rank higher or be integrated more prominently, regardless of factual merit? Without clear transparency, users may struggle to distinguish commercial influence from neutral reasoning.

This shift would probably fragment trust in AI systems themselves. Some users might flee to “ad-free” tiers or independent models just to avoid sponsored content, creating a two-tier ecosystem of intelligence: “clean” channels for those who can pay, and “sponsored” channels for everyone else. Inequality wouldn’t be about who has access to AI, but who gets answers without strings attached.

The implications for platform power are equally significant. The gatekeepers of AI models wouldn’t just be tech monopolies — they’d become media and advertising platforms with unprecedented influence over the very fabric of mediated knowledge.

Of course, where there’s pressure, there’s innovation. We might see entirely new business models and alliances emerge: brands paying to insert “expertise modules” or sponsor domain-specific knowledge in areas like financial advice, nutrition, or automotive recommendations. AI companies could bundle or white-label sponsored modules for enterprise clients, creating a marketplace of commercially aligned intelligence.

The pressure to monetize AI will likely push us toward new hybrid models combining subscriptions, advertising, sponsorship, or some blend thereof. Sponsored intelligence might be the next frontier in how we pay for, and perceive, machine reasoning. The question isn’t whether it will happen, but how transparently, and how soon.

Generative AI was fueled by a gold rush for high-quality data. But those streams are nearly dry, and while lawyers fight over scraps, the industry is already moving on to a far bigger prize: your lived experience. This is because the largest language models have strip-mined the public web, books, news archives, and open-source code. Experts estimate that relatively soon, most open, high-quality material will likely be depleted, forcing a sea change in discovery, licensing, or synthesis just to keep training the models.

This data grab hasn’t gone unchallenged. In 2025 alone, over fifty AI-related copyright lawsuits were active. Judges in different circuits are split: some have found aspects of training to qualify as fair use, especially when the use is transformative; others side with creators when substitution is clear or economic harm is demonstrated. The biggest cases against OpenAI, Meta, and Anthropic continue to grind forward, with industry and plaintiffs alike waiting for a decisive precedent.

But while many eyeballs are focused on these legal outcomes, the AI industry’s data and model-training focus is already shifting to new frontiers. Why? Because the well is nearly dry and pipelines to scrape the remaining data and ingest real-time streams are already in place. These copyright wars may set legal precedent, but function mostly as rearguard actions. The true frontier has shifted from static corpora to lived experience.

Meta’s new Ray-Ban glasses push AI into everyday perception and memory. Apple’s Vision Pro bets on spatial computing as a workflow foundation. Perplexity, Grok, and others pitch conversational agents as always present, always adaptive “companions”. As the Financial Times recently put it, “experience is now the dominant medium of technological improvement”.

Critics call some of this hype, and they’re not completely wrong, but the market direction is unmistakable. The most valuable data won’t be yesterday’s text or images. It will be the messy, evolving context of ordinary life: what individuals see, hear, say, and do, captured, synthesized, and fed back in real time. Copyright wars will end in the courts, but the experience wars will be fought on our faces, in our ears, and in the human loop of daily action. The decisive battles for AI’s future won’t be over library books—they’ll be over reality itself.

This shift raises questions we're barely beginning to ask. Unlike the copyright battles fought between corporations and creators over past works, the experience wars will be waged with ordinary people as both the battlefield and the prize. When AI systems learn from how we navigate our kitchens, react to conversations, or pause before making decisions, traditional notions of consent become murky.

We're rapidly moving from a world where AI companies ask forgiveness for scraping public data to one where they'll need permission to scrape our lives. If that notion is correct, today’s book and newspaper copyright cases will look quaint compared to the governance challenges of tomorrow’s ambient intelligence, when the prize isn’t past content but our lived reality.

Jimmy Kimmel is furious. ABC yanked him off the air after a monologue where he suggested Charlie Kirk was assassinated by someone from the MAGA movement (and after the chair of the FCC weighed in on the situation during an interview). Kimmel’s future at ABC is now in limbo. On the other side of the country, President Trump has sought to fire Federal Reserve Governor Lisa Cook on contested allegations of mortgage fraud, triggering a historic legal battle over the Fed’s independence. Meanwhile, reports surfaced that Trump pressured a U.S. attorney in Virginia to probe New York Attorney General Letitia James—the same James who grew her fame by suing Trump. Democrats and commentators call the current state of affairs hypocritical. Weren’t Republicans the ones howling about weaponized justice and cancel culture? Yes. This is not irrational. It's game theory in action.

American politics isn’t a morality tale. It’s a repeated game with endless rounds and each side responding to the other's previous move. In the 1980s, political scientist Robert Axelrod ran tournaments to test strategies to win the Prisoner's Dilemma, which describes two players who can either cooperate or defect: cooperation brings the best outcome, but the lure of cheating for a personal win often leaves both worse off. The surprise winner was “Tit for Tat,” a simple algorithm that starts by cooperating, then copies the opponent’s previous move. Its genius was simplicity: reward cooperation, punish betrayal, and forgive quickly.

While political hardball isn't new, recent years have seen a Tit for Tat-like escalation in specific domains. Democrats leaned into legal challenges, impeachments, and social pressure; Republicans responded with their own investigations and norm-breaking to gain a competitive advantage. Each side points to the other's previous moves to justify their current tactics as the arms race accelerates. The result isn’t just partisan hypocrisy, but system-wide Tit for Tat logic in action, copying the other side’s move in each round of gameplay. These rounds build on each other, with past behavior defining expectations and responses, just as in Axelrod’s tournaments.

Cries of hypocrisy miss the deeper system-wide dynamic. Democrats and Republicans have learned that unilateral cooperation can mean getting played for suckers, while retaliation pays off in the short-term gratification world we now live in. Kimmel, Cook, and James aren’t anomalies, but casualties of war in an endless Axelrod tournament that threatens to grind away what little trust remains in America’s institutions.

Independent journalist Pablo Torre has built what might at first seem like an airtight indictment of LA Clippers owner Steve Ballmer for breaking NBA league rules: a $28 million no-show endorsement for star Kawhi Leonard, underwritten by a bankrupt startup Ballmer personally funded. On its face, it resembles textbook creative salary cap circumvention. But Torre's case illuminates something much deeper than alleged sports corruption: like a high-profile prosecution, it shows how investigative journalism weaves a one-sided sense of certainty from circumstantial facts in the court of public opinion.

The financial web has smoke all over it. A company related to Kawhi Leonard, KL2 Aspire LLC, signed a four-year, $28 million deal with Aspiration, a now-bankrupt sustainability-as-a-service startup with celebrity investors such as Leonardo DiCaprio, Cindy Crawford, Robert Downey Jr., and Steve Ballmer, owner of the Clippers, who put in $50 million. (Other investors included the venerable Allen & Co. and Omidyar Network, and fans included Ron Klain, later President Biden’s Chief of Staff.) In total, Aspiration raised around $250 million with hopes of going public via a SPAC. After a federal investigation that eventually caused the company to collapse, Aspiration co-founder Joseph Sanberg pleaded guilty to two counts of wire fraud. It’s all a bit icky.

Torre is making a headstrong case that Ballmer himself, personally, was behind a plan to funnel money to Kawhi Leonard via an investment in Aspiration, which then would create a partnership with an LLC with ties to Leonard, as a way to circumvent the salary cap. The NBA and outside counsel from white-shoe firm Wachtell, Lipton, Rosen & Katz are investigating whether this arrangement actually broke salary cap rules. Ballmer, formerly the longtime CEO of Microsoft, firmly denies orchestrating anything. (It’s probably important to note that Ballmer has a personal office of 50-100 employees whose only job is to manage his philanthropic initiatives and assets.)

Both Ballmer and the Clippers have consistently stated they are cooperating fully with the NBA’s ongoing investigation, as well as with any relevant law enforcement inquiries. In statements to the media, Ballmer has welcomed the league’s scrutiny, explicitly saying “I’d want the league to investigate, take it seriously,” and reiterating his denial of any wrongdoing. NBA Commissioner Adam Silver and league policies are also clear: disciplinary action against a team, owner, or player requires clear, direct evidence of violations and not simply a suspicious pattern or appearance of impropriety.

Despite Torre’s prosecutorial enthusiasm about Ballmer’s guilt, there’s plenty of reasonable doubt in the pile of circumstantial evidence, and there are many reasonable alternative hypotheses as things stand today. As I understand it, there’s no corroboration inside the Clippers from people like minority owner Dennis Wong, president Gillian Zucker, or basketball ops president Lawrence Frank, any one of whom theoretically could have made such arrangements behind Ballmer’s back (or made none at all). I don’t believe there’s any information directly from Ballmer’s family office, either. And the “seven sources” Torre keeps bragging about from inside Aspiration all got their information from the same place: the leaders of the company, who are not beyond lying. The CIA calls that “information laundering,” and PR people call it “astroturfing.” It’s suspect evidence at best.

Mark Cuban, who knows a thing or two about running businesses, investing, and owning an NBA team, says the evidence shows the hallmarks of a grift on the part of Aspiration. Imagine a desperate and fraudulent startup seeking to juice its public profile with celebrity endorsements by overpaying to manufacture an aura of momentum. (Tom Brady and FTX, anyone?) Now, owners investing in sponsors of their own team who, in turn, work with a player with unusual contractual provisions is certainly poor optics and perhaps even a bit bizarre. Nevertheless, intent and authorship are essential when assessing what all of the evidence really means.

Here are some key questions that I don’t think have been answered yet. First, what exactly did Ballmer know, and when? Similarly, what did Clippers minority owners and leadership know? If this were a Law & Order episode, you’d interview these people and get them to turn on each other to save their skins—or discover that they really didn’t know anything at all.

Second, what did Kawhi, his uncle Dennis Robertson, his agent Mitch Frankel, or his legal team know? I don’t think any of them have sat for an interview, but that’s not exactly the Clippers’ problem; the questions remain unanswered. Moreover, Kawhi’s contract required no promotional or other work to get paid $7 million a year, which is interpreted by Torre as evidence of a crime. But it also could be interpreted as a “loophole” to ensure he himself did not know about the payments, so that someone else could benefit. Who were the members of the LLC, what bank account(s) did it use, and where did the funds go from there, if anywhere? If Kawhi didn’t even know he was getting “paid off,” that would certainly be evidence against circumvention of the salary cap via that LLC.

Finally, who exactly at Aspiration created this arrangement, and did they have direct contact with Ballmer or other Clippers leadership about this specific issue? Cofounders Andrei Cherny and Joe Sanberg could go on the record about this, and in fact would seem to be incentivized to do so at this point. You’d have to take their word with a grain of salt, but it would certainly be something if they themselves named names.

Listening to Torre speak about this topic, he reminds me of a prosecutor who doesn’t quite have the goods and should probably bring a lesser charge, but who is pressing for the death penalty anyway because it’s a high-profile case. But unless investigators uncover direct orders (emails, messages), cooperating witnesses (e.g., Clippers executives or Leonard’s reps) stating the true intent, or clear quid-pro-quo paper trails, this case should not clear the NBA's burden of proof. Recall: the Joe Smith precedent needed both extensive documentation and executive confessions. The NBA is not a court of law, and these are not criminal charges, but right now, there’s so much reasonable doubt that it doesn’t matter how enthusiastic Torre is, how many podcast interviews he does to promote the story, or how many times he repeats “3000 documents and seven sources” as if volume and workload itself is evidence of something.

To be sure, Torre's reporting is interesting and invaluable for surfacing the realities of power, regulation, and narrative in pro sports. But from what I understand of the facts that are out in public view, one could objectively draw several different conclusions. But it also shows how quickly circumstantial patterns harden into public "proof" and why journalistic rigor actually matters when concentrated wealth and regulatory ambiguity are in play. The most basic of journalistic questions haven’t been answered yet in this case: Who knew what when, and why did they do what they did?

​​In the end, this entire saga is a living example of what we might term Pablo’s Paradox, in honor of Mr. Torre: The rush to bring hard-hitting accusations into the spotlight is always in tension with the patience needed to settle every last question. Torre’s work reminds us that journalism’s greatest challenge isn’t just unearthing secrets, but also resisting the urge to mistake the volume of evidence for the weight of proof. As Ballmer’s case shows, in sports and in journalism alike, the line between exposure and certainty can be razor-thin, and like in sports, getting there too soon can sometimes be as risky as getting there too late.

America doesn’t have a crime problem. It has a crime shortage. For all the headlines about rising violence, the real threat is scarcity: too few offenders to keep the criminal-industrial machine running at full capacity. Imagine a city with no criminals—no shoplifters, no drug dealers, no repeat offenders cycling endlessly through arraignment. It sounds idyllic. Until you realize the collateral damage: nonprofits without clients, public defenders without caseloads, police without overtime, grant programs without funding. Crime-free streets would mean mass layoffs across the criminal-industrial economy.

Pundits condemn policies like no cash bail and lenient sentencing as failed experiments. But view these strategies through economics, and they look less like fumbles and more like routine maintenance of a vast criminal-industrial machine. The numbers tell the story.

Most urban crime comes from a small population of repeat offenders. The Bureau of Justice Statistics shows 66% of released prisoners are rearrested within three years, over 82% within a decade. When they disappear behind bars, both street crime and demand shrink for the network that depends on them: public defenders, probation officers, nonprofit programs, private security, and police overtime.

New York City’s 2019 bail reform offers a live example. With cash bail abolished for misdemeanors and nonviolent felonies, thousands of defendants cycled back to the streets. About 20% of those released were rearrested within a year. California, meanwhile, raised its felony theft threshold to $950 in 2014, and San Francisco has reported double-digit increases in shoplifting incidents, with retail consortiums citing the rash of repeat offenders as a driver for storefront closures (CA Retail Facts).

These numbers are budgets, overtime checks, and jobs. The NYPD spent $740 million on overtime in 2023—nearly doubling projections—with much of that tied directly to street-level crime response and repeat offenders. The federal government distributes billions in justice-related grants; when caseloads drop, so does funding and employment.

If a city actually cracked down, locking up every shoplifter and handing out long sentences, crime would drop—but so would overtime, grant money, and staffing across justice and social sectors. Historical cases like New York City in the 1990s confirm that successful crime reduction means staff cuts, less federal money, and downsized agencies.

This isn’t a conspiracy—it’s economic incentives: thousands of careers and paychecks depend on manageable, repeat street crime. Lenient criminal justice measures, in this light, function less as failed reform and more as a stimulus—ensuring the system never runs out of clients. What looks like dysfunction may actually be the system working exactly as designed. If crime vanished tomorrow, thousands of jobs would vanish with it. That’s the hidden success of leniency: keeping the machine fed.

Alien: Earth (FX) opens in 2120: world governments have collapsed, and five mega-corporations rule Earth like feudal lords. The formidable Weyland-Yutani Corporation controls the Americas, young upstart Prodigy and its barefoot childlike CEO rules “New Siam” in Asia, and so on. The show clearly wants us to be horrified by this, in addition to the show’s titular monsters. Democracy collapses! Corporate dystopia! Citizens as assets! But here’s the uncomfortable question Alien: Earth accidentally raises: would this really be worse than the governments we already live under?

This isn’t just Hollywood sci-fi speculation. There’s a real-world intellectual movement called the Dark Enlightenment whose chief theorist, Curtis Yarvin (AKA Mencius Moldbug), has long argued that governments should function more like corporations, with clear hierarchies, efficiency, and accountability instead of the messy chaos of elections. What was once an obscure, edgy blog niche is now circling mainstream power. Peter Thiel has said he no longer believes freedom and democracy are compatible. Marc Andreessen, venture capitalist and author of the Techno-Optimist Manifesto, considers him an influence. Vice President J.D. Vance has publicly described Yarvin as a friend. When the people building tomorrow’s technologies and laws are openly skeptical of democracy, we should take that seriously.

In a nutshell, the Dark Enlightenment argues that states should be run as joint-stock companies led by a CEO—clear hierarchy, efficient execution, and minimal electoral churn. While critics of corporate rule usually compare it to an idealized democracy, many of the world’s citizens today live under authoritarian strongmen, military juntas, failing kleptocracies, war zones, refugee camps, and ungoverned regions. According to Freedom House, around 40% of the world’s population currently lives in such “not free” political systems. Now imagine if a hypothetical tech and communications company called BrilliantFreeTech ran North Korea or Myanmar, or stepped in to help fragile states, like Somalia or Afghanistan. Would human rights improve? Probably. Would people have internet? Definitely. Economic opportunity? Almost certainly. The grim truth is that in many cases, corporate rule could in some contexts plausibly outperform failed or predatory states. (Even U.S. trust in our own federal government is only ~22%.)

How might this actually happen, though? Corporate governance wouldn’t begin by overthrowing functioning states. It would spread into places where the government is already weak. Somalia’s federal institutions remain fragile; Afghanistan is authoritarian. Corporate logistics and communications might work better than government services, though obviously with human rights tradeoffs. At the poles, governance exists—the Antarctic Treaty System, UNCLOS, national EEZs—but climate and commerce are stressing those regimes, creating space for de facto corporate rule over operations.

And then there’s space. Companies like AstroForge aren’t waiting on UN treaty-making; they’re moving under national laws that recognize rights to extracted resources (the 2015 U.S. statute; Luxembourg’s 2017 law) and the nonbinding Artemis Accords—which endorse resource utilization—while the 1967 Outer Space Treaty still forbids any claim of sovereignty. In that gap, governance will look like contracts, “safety zones,” and operational control by whoever runs the landing sites, logistics, and networks. Bezos and Musk wouldn’t be sovereigns in law, but they could become de facto rule-makers wherever their systems pay the bills and keep people alive.

History isn’t silent on this point: chartered firms like the East India Company and Hudson’s Bay exercised taxation, courts, and force—proof that corporate sovereignty is administratively feasible, and a warning about extraction.

Antitrust advocates like to remind everyone, sometimes quite militantly, that competition is good for markets, prices, and consumers. What’s striking is that corporate rule could introduce competition into governance itself. If your government sucks today, you’re stuck until the next election—if there is one. But in a corporate-sovereign world, dissatisfied ‘citizens’ could—where exit is actually feasible—move to a different brand of governance; the catch is that exit isn’t free: passports, capital controls, property, community ties, and language create high switching costs, and ‘voice’ (representation, due process) may be thin.

Picture ‘Apple World’ (seamless minimalism), ‘Amazon World’ (logistics with worker surveillance), ‘Tesla World’ (green futurism mixed with occasional chaos), ‘Google World’ (total algorithmic governance at the expense of art), ‘Microsoft World’ (boring, reliable utilities that need complete rebooting from time to time). Each of these fictional worlds represents a different trade-off between efficiency, control, and rights. But today, we already make similar trade-offs—ceding privacy and autonomy to corporations in exchange for services and convenience. The real choice is between the messy, often brutal reality of current states (genocidal regimes, failed states, paralyzed democracies), and the potential efficiency of corporate governance (hierarchies optimized for performance, accountable not to citizens but to shareholders).

None of the above is utopia. But Alien: Earth reminds us that corporate sovereignty is no longer science fiction—it emerges wherever governments fail and corporations step in. The Dark Enlightenment frames this not as dystopia, but as a plausible evolutionary pathway where state capacity erodes and corporate capacity fills the gap. That’s what makes it so provocative—and so dangerous.

The economics of attention have changed. The old consensus comedy—polished, expensive, and time-slotted—no longer holds. In its place is a tribal scramble for narrative dominance across platforms and ideologies.

Stephen Colbert was a casualty of this shift not because he leaned too far left, but because he cost too much and moved too slowly. The math no longer worked: rising costs couldn’t be justified in a collapsing ad market and a culture that now favors edge over polish. It wasn’t a political assassination, as some prominent Democrats have speculated. It was financial euthanasia.

Across the board, legacy network late-night show hosts—Fallon, Kimmel, Meyers—are relics of a time when cultural authority came bundled with a broadcast license. They are probably the last of an era that dates back to Steve Allen and Jack Paar. Today, politics-as-entertainment thrives elsewhere: on YouTube, on Fox News, in strangely-named podcasts where senators and comedians swap war stories. The new landscape is fragmented, faster, and cheaper. Audiences don’t want pre-chewed satire or bipartisan talking points. They want blood, jokes, and insider gossip served raw.

Consider the outliers. Fox News’ late-night competitor Gutfeld! routinely beats the networks in viewership, despite airing on cable and employing far, far fewer people than Colbert’s reported 150-200. Audiences all over the spectrum increasingly want the stripped down, raw likes of Joe Rogan, Club Shay Shay, The Breakfast Club, Call Her Daddy, Flagrant, The Shawn Ryan Show, and Tucker Carlson. Donald Trump may have tilted the last election cycle not by appearing on CBS’ 60 Minutes, but by skipping it—choosing to do 60-minute-long podcasts instead.

Politics is hotter—and more entertaining—than ever.

Ruthless—a five-year-old podcast hosted by four Republican political insiders (Josh Holmes, Michael Duncan, John Ashbrook, and Shashank Tripathi) mixing campaign gossip, golf-ready quarter-zips, and mockery of progressive overreach—is now part of the Fox News portfolio. It’s fast, cheap, fun-natured, and influential. David Ellison’s Skydance (soon to own CBS) circling former Wall Street Journal and New York Times writer and editor Bari Weiss’ The Free Press shows that even traditional news producers want a stake in politically-adjacent media—ideologically messy, but narratively potent.

How long before a network makes a play for All-In, a similarly aged podcast born of COVID-19 and hosted by four Silicon Valley insiders (Chamath Palihapitiya, Jason Calacanis, David Sacks, and David Friedberg) (note that Sacks currently serves as the AI and crypto “czar” for the Trump administration)? Their interesting and influential conversations veer from markets to politics to poker to tequila. Maybe an All-In spinoff replaces some of CNBC’s tired after-work programming, in the spirit of Fox Business’ show Happy Hour from the 2000s—buoyantly filmed at the Bull and Bear bar inside the Waldorf Astoria. Maybe a spinoff like Ruthless After Hours slides in after Gutfeld! on FNC. Maybe Bari Weiss takes over Face the Nation—rebranded, naturally, as The Free Press.

We don’t know the answers to these questions, but what is clear is that the winners in this new alignment are what we might call hybrid operators—insiders with outsider energy. People who know how the system works but talk like they’re not part of it. They’re not cleanly partisan, but they’re not neutral either. They’re fluent in conflict, comedy, and real-time relevance. They blend access with attitude. And they know that in today’s media-political economy, you don’t need to be right—you need to be compelling. (Tucker Carlson might be the honorary chairman of this club.)

The losers in this shift? Anything too safe, too scripted, or too slow. Legacy hosts and legacy formats. Comedians who sound like press secretaries. Pundits who stopped surprising ten years ago.

Say what you want about cranky ol’ Bill Maher—Real Time still draws serious viewers for HBO (so does John Oliver, in his own very effective political-comedy way), and Maher’s unique pod Club Random had hosted everyone from Sean Penn to David Mamet to Charlie Kirk to Billy Joel to RFK Jr. And people have kept coming back for more since his show Politically Incorrect launched over 30 years ago. And, perhaps improbably, SNL is still influencing politics after 50 years on the air.

This is a battle over who shapes meaning, who gets heard, and who still thinks they’re programming for America’s living room in 1965. Just as the world order is realigning before our eyes, so too is the news media and entertainment that maps it. Late-night network TV used to broadcast the voice of establishment power. Now the conversation’s happening somewhere else. The action has moved off-airwaves, off-schedule, and off-script.

Reports of late-night politics’ death have been exaggerated. It’s just evolving: mutated and multiplatform.

Before Kevin Feige turned crossover storytelling into a corporate science with the Marvel Cinematic Universe, another shared universe was quietly forming—one the studios never branded, but which now spans more than a dozen major action films. It doesn't revolve around a single hero or IP catalog. It revolves around Los Angeles—and you've probably watched parts of it without realizing they were connected.

The sun of this accidental universe is the Fast & Furious franchise—11 movies, over $7 billion in box office revenue, a rotating and ever-expanding cast, and global adventures involving everything from car heists and safe-cracking to cyberwarfare and their version of a space force. But it began—and remains—rooted in LA's actual street racing scene. Films like The Fast and the Furious (2001), Fast & Furious (2009), and Furious 7 (2015) return to Los Angeles not just for plot, but for identity. The city isn't a setting; it's the home turf and emotional anchor of these films.

F&F's tentacles into this larger, hidden, unbranded cinematic universe—call it the "LA Actionverse"—stretch back to 2002, six years before Iron Man launched the MCU. The first spark came with Better Luck Tomorrow (2002), Justin Lin's Sundance breakout about high-achieving Asian-American teens who spiral into crime in Orange County. One of them is "Han," played by Sung Kang—cool, quiet, always snacking. Four years later, when Lin took over The Fast and the Furious: Tokyo Drift, he brought Han back, and retroactively, a low-budget indie drama became canon in a multi-billion-dollar franchise.

The second spark of 2002 came from actor Jason Statham. In Fast & Furious 6 (2013), he debuts as "Deckard Shaw," a British ex-special forces operative with a vendetta. But before that, he'd already played: "Frank Martin," the British ex-special forces operative driver-for-hire in The Transporter series (2002–2008); "Handsome Rob," the premier wheelman in The Italian Job (2003), which culminates in an elaborate LA heist; and "Airport Man," in Collateral (2004), who passes a briefcase to Tom Cruise's hitman Vincent—later confirmed to be Frank Martin, aka the Transporter.

Got all that? Is "Shaw," who comes from a British family of special forces operatives-slash-criminals, just another alias for "Martin"? Maybe. The skills match. The arc fits. The connections were never formally announced, but enough was confirmed to make the theory credible—and fun. The timeline:

• 2002 – Better Luck Tomorrow – "Han" (Sung Kang) is introduced in Orange County (just south of LA)

• 2002 – The Transporter – "Frank Martin" (Jason Statham) driving for southern France's underworld

• 2003 – The Italian Job – "Handsome Rob" (Jason Statham) helps pull an LA heist

• 2004 – Collateral – "Airport Man" (Jason Statham) hands off a briefcase at LAX

• 2005–08 – Transporter 2 & 3 – "Frank Martin" (Jason Statham) resurfaces in Miami and Europe

• 2006 – Tokyo Drift – "Han" (Sung Kang) resurfaces in Tokyo

• 2013–2023 – F&F 6–10, Hobbs & Shaw – "Shaw" (Jason Statham) and "Han" (Sung Kang) storylines expand; multiple films return to LA

• 2019 – Fast & Furious Presents: Hobbs & Shaw – "Shaw" (Jason Statham) explicitly references The Italian Job

In an era when cinematic universes are planned years in advance, the LA Actionverse emerged organically—through overlapping creators, actors, and tone. It reveals something studios can sometimes forget: audiences don't just care about continuity. They care about coherence. Han and Shaw feel like they belong in the same world because they do—cinematically, thematically, emotionally. So do Collateral's Felix (Javier Bardem) and Vincent (Tom Cruise), both stuck in LA's moral quicksand.

And the LA Actionverse extends further still. Heat’s Neil McCauley (Robert De Niro) and Vincent Hanna (Al Pacino). Den of Thieves’ detective “Big Nick” (Gerard Butler) and robber Ray Merrimen (Pablo Schreiber). Los Angeles is the main character, binding these people and their actions together: a noir city of night driving, loyalty codes, and silent professionals with murky pasts.

(Don't even get me started on whether Charlize Theron's "Stella Bridger" from The Italian Job reinvents herself as "Cipher" from F&F.)

Disney’s Marvel built an empire. But Fast & Furious—along with Better Luck Tomorrow, The Transporter, The Italian Job, and Collateral—quietly built something more elusive and resonant: a mythos.

It just doesn’t have a logo.

If you think ChatGPT in schools is just for cheating on essays, think again. OpenAI is pushing to make AI assistants as fundamental to college life as email—embedding them in every facet of the experience, from personalized tutors to AI-generated quizzes and job interview simulators. As Leah Belsky, OpenAI’s vice president of education, recently told The New York Times, the vision is for every student to have a personal AI account, just like a school email. Universities like California State and Duke are already on board. Duke has even launched its own branded platform: DukeGPT.

But what happens when the same technology that writes the paper also grades it? Or when professors rely on AI to generate assignments, feedback, and even lesson plans? The classroom becomes a self-reinforcing ecosystem—one where AI is both the tool and the judge.

What makes this dynamic uniquely volatile is the adversarial relationship at its core. In most industries, technology adoption is collaborative or competitive among peers. However, in education, the roles are asymmetrical: teachers set the rules and assign the work; students are expected to produce original output. When both sides adopt AI—students to generate content, teachers to assess or detect it—education becomes an arms race. Each side is incentivized to outmaneuver the other, inside a system that depends on trust and integrity to function.

This isn’t just a story about productivity—it’s a technological arms race in a system built on trust. Yet, as AI blurs the line between help and deception, academic integrity, once a cornerstone, is now under siege. Teachers fear losing authority or relevance; students fear falling behind without machine assistance. Both sides feel pressured to adopt tools they may not fully trust. The more AI is used, the more it becomes expected, creating a feedback loop where its presence is mandatory, not optional. In evolutionary biology, that’s called “runaway selection” or “Fisherian runaway” after the mathematician and biologist Sir Ronald Fisher.

Runaway selection explains how traits—like the peacock’s tail—become extreme simply because they’re popular, not because they’re useful. Flashy, burdensome, but impossible to ignore. The same logic applies here. AI tools are both the trait and the preference: their popularity makes them necessary, and their necessity makes them popular. As AI becomes more common in classrooms—used to write, grade, assess, and simulate—it becomes the standard not because it improves learning, but because the system adapts around its presence. The result is a self-reinforcing cycle where AI becomes indispensable, not because it’s always better, but because it’s what everyone expects.

That shift has real-world consequences. As AI reshapes the classroom, it also reshapes the students within it, altering how they learn and what they become. Over-reliance on machine assistance risks dulling critical thinking, interpersonal skills, and the kind of cognitive struggle that builds real understanding. At the same time, unequal access to AI tools threatens to widen educational divides, privileging the well-resourced and leaving others behind. This isn’t just a pedagogical issue—it’s a blueprint for the workforce, the economy, and the social contract that follows.

That’s not reform. That’s mutation—and it’s editing education at the chromosomal level. Unlike peacocks trapped by their evolutionary programming, however, educational institutions retain the power to step back and redesign the rules of engagement—but only if they can coordinate action before the arms race becomes too entrenched to reverse.

Self-driving vehicles and humanoid robots are both inching into everyday life—but at different speeds, and with different public reactions. Tesla, for example, is piloting its first robotaxi service in Austin with a small, closely monitored fleet. Meanwhile, Amazon is reportedly training humanoid robots for package delivery, testing them on obstacle courses and sending them on “field trips” to simulate real-world conditions. But the real question isn’t “when will this be possible?”— it’s “when will people actually want it?”

After years of hype, fully autonomous vehicles are progressing much more slowly than predicted. The tech is improving—AV systems are reducing crashes, optimizing traffic, and saving time—but large-scale personal adoption is still years away. According to McKinsey, most privately-owned AVs won’t reach broad deployment until the late 2020s or early 2030s. Fleet-based robotaxis and delivery vans will come first, but mainstream car buyers still aren’t ready to give up the wheel.

Trust is a major hurdle. A 2024 survey from the American Automobile Association (AAA) found only 13% of U.S. drivers feel comfortable riding in a self-driving car. Even younger generations remain divided. A recent YouGov poll found that urban residents are more open to AVs than those in rural areas—but overall, hesitancy remains high. Many people associate driving with freedom, identity, or even joy. Luxury and performance cars—from Mercedes to Mustangs—still symbolize status, aspiration, and power. It’s hard to imagine people trading in that experience for a featureless pod, no matter how good for climate change it is.

Meanwhile, humanoid robots are becoming more capable, but not yet road-ready. Companies like Tesla and Figure AI are building bipedal robots that can walk, lift, and navigate warehouse environments, with Figure AI aiming to ship 100,000 units by 2029. Amazon has started trials of humanoid delivery bots trained to climb stairs and operate in urban settings. Driving, however, remains a higher hurdle—navigating unpredictable roads in real-time is still well beyond the reflexes and reliability of current robots. So, for now, the “Megan Fox nanny” model from Netflix’s Subservience remains science fiction.

The cultural reaction to humanoid robots is mixed. Research shows people are generally more comfortable interacting with robots that have friendly, human-like features—but only up to a point. The “uncanny valley” effect kicks in when robots become too lifelike, triggering discomfort instead of trust. Still, studies suggest humanoid robots may find broader acceptance than people expect, especially in support roles like elder care or household assistance.

The likely outcome isn’t a clear winner—it’s a gradual, uneven evolution. In cities, autonomous fleets may gain ground. In homes, humanoid robots could appear in high-income households or specialized settings as soon as 2026 before going mainstream. And across the board, many people will keep driving their own Escalades and 911s for years to come—not because they have to—because they want to.

The real turning point won’t be a technical breakthrough. It’ll be when these technologies earn emotional acceptance. And that’s about more than functionality—it’s about trust, aesthetics, and whether people see the machine as a tool or a threat. The future may be autonomous, but the adoption curve will be deeply human.

Amid a fresh round of tariffs, retaliatory threats, and phone calls between Washington, D.C., and Beijing, U.S.-based importers are scrambling to exploit a set of 19th- and 20th-century loopholes: bonded warehouses, foreign trade zones, and the magical $800 de minimis exemption. Trump’s 2025 tariffs hit 145% on Chinese imports, while U.S.–China trade volumes are projected to collapse by 80% this year. But far from Capitol Hill, Oxford physicists just made a quantum leap (so to speak) that hints at a stranger future. “We’re teleporting bits, not bananas,” one physicist said. We’re decades from moving fruit, let alone people, but this raises an interesting question: what happens when we can teleport the bananas?

Current U.S. trade law hinges on geography. Goods are taxed when they physically enter the country—usually at a port, airport, or border checkpoint. But if you could beam a $50 million Picasso from your flat in Paris to your Manhattan townhouse without crossing a border, does that count as an import? If not, you’d dodge $3.25M in duties overnight. There’s no ship, no scan, no customs declaration. WTO rules, designed for shipping containers and cargo manifests, have nothing to say about matter transmission.

And yet, this isn’t as hypothetical as it sounds. Demand for bonded warehouse space in the U.S. has exploded in 2025 as importers try to delay tariffs triggered by Trump’s new trade war. Geneva Freeport—often called ‘the world’s largest invisible art collection’—holds an estimated million artworks, including Picassos and Da Vincis, valued conservatively in the tens of billions of dollars— technically, they’re “in transit” and haven’t entered any country. If teleportation existed, it would supercharge this legal gray zone. No entry means no jurisdiction. And without jurisdiction, there’s no tariff—no matter how many diamonds beam into your penthouse.

Of course, once the government catches up—and it always does, eventually—it would scramble to redefine ‘entry’ as anything that appears on national soil. Enforcement would rely on encrypted quantum manifests and teleportation receipts, the customs version of a UPS tracking number. Smuggling would get weirder: fentanyl, diamonds, or banned semiconductors beaming into someone’s garage without ever passing a checkpoint. (The film will star Ana de Armas, no doubt.)

Like the 15-year lag between container shipping (1960s) and modern customs protocols, regulators are already struggling to track TikTok data flows, let alone quantum-smuggled goods. The stakes climb if you include people. Teleportation would upend the business models of toll roads, airlines, passport systems, drug cartels, and even prisons. A biometric ID might become the only thing standing between order and chaos. Homeland Security becomes irrelevant if a terrorist can materialize directly into Times Square from thousands of miles away.

So no, teleportation won’t kill tariffs tomorrow. But the thought experiment exposes a fragile truth: trade enforcement is built on physical assumptions that technology is already beginning to undermine. As governments struggle to regulate TikTok videos and encrypted cross-border payments, the arrival of matter transmission—even just the idea of it—should be a warning.

Policy moves at the speed of paperwork. Physics doesn’t.

The debate over kids using generative AI (GenAI) for schoolwork has reached a fever pitch, with teachers and parents scrambling to keep up with change, as tools like ChatGPT, Gemini, and Claude become nearly ubiquitous in classrooms. Fox News recently spotlighted a high school English teacher who went viral after quitting, blaming ChatGPT for killing effort, literacy, and presumably, the Oxford comma. But as the dust settles, it’s clear AI isn’t going anywhere—and educators are left to figure out how to grade fairly in this new landscape.

AI’s adoption among today’s students is nothing short of explosive. In the past year, the percentage of K-12 students using ChatGPT weekly has jumped to 48%, with nearly half of teachers also reporting weekly use. Only about 20% of students say they’ve never used generative AI. This isn’t a tech fad—it’s a pedagogical jailbreak. Students are rethinking assignments; teachers are rethinking their entire reality.

Yet, public opinion surveys reveal a more complex picture. A recent Pew survey found that only 6% of teachers believe AI does more good than harm in K-12 education, while a quarter say it does more harm than good. The rest are split or unsure, reflecting deep ambivalence. High school teachers are the most skeptical, with 35% saying AI’s negatives outweigh its positives at that level. Concerns about plagiarism, reduced student effort, and the potential for AI to undermine genuine learning are widespread.

Despite fears, there’s an argument to be made that using AI doesn’t eliminate the need for skill—it just changes the game. Even with a powerful AI model, writing a compelling paper or solving a tough math problem requires critical thinking, creativity, and the ability to effectively prompt and refine AI output. Like steroids in sports, injecting AI doesn't make you a star—it just makes your shortcuts faster. You still need skills, discipline, and the digital-age equivalent of game sense.

New academic research backs this up: a prominent meta-analysis of 51 studies published between November 2022 and February 2025 found that ChatGPT can significantly improve student learning performance, especially in skill-based courses, but its impact depends on how students use it and what they’re trying to learn. Interestingly, AI’s advantage is less pronounced in project-based or creative tasks, preserving space for human ingenuity.

As with any industry disruption, there will be winners and losers. Some students who struggle with traditional tests may thrive in an AI-augmented environment, where the ability to leverage technology becomes a new form of literacy. In fact, 56% of students in a recent survey said AI reduced their math anxiety (apparently, the fear of long division fades when a computer’s doing the work), and 21% reported improved scores thanks to AI tools. But there’s a risk that access to AI could deepen existing divides—students from wealthier backgrounds may have more reliable access to advanced tools, while others are left behind. Nonprofits and public programs are pushing for ‘AI for every child,’ echoing Google.org’s recent $25 million pledge to support AI education for underserved students.

Teachers, for their part, are resigned to the future—96% believe AI will be baked into education within a decade. The challenge is to create clear guidelines, invest in AI literacy, and find ways to grade students on the curve of effort, creativity, and mastery, not just the knack for cranking out a passable essay with a chatbot. As with any disruptive technology, the goal isn’t to ban or blindly embrace AI, but to harness it to elevate learning for all students, while keeping a close eye on fairness, integrity, and opportunity.

In May 2025, Argentina’s capital was rocked by a new kind of election interference: an AI-generated video, circulated widely on social media, appeared to show a prominent political figure endorsing a rival candidate. Released during the pre-election silence period, the video was quickly denounced as fake, but not before reaching millions and potentially influencing the outcome. This is a vivid example of how generative AI and deepfakes can rapidly reshuffle a political landscape.

Argentina’s recent experience is part of a global wave. In their 2023 presidential race, both major campaigns in Argentina weaponized AI: Javier Milei’s team distributed fabricated images of his opponent Sergio Massa dressed as a communist general, while Massa’s supporters used AI to create images of Milei as a zombie or pirate. The technology is now so accessible that anyone with a smartphone and a few minutes can create convincing fake photos, videos, or audio, making it nearly impossible to distinguish real from fake, even for experts. The goal? To go viral, inflame passions, and blur the line between truth and fiction.

This phenomenon is certainly not limited to Argentina. Around the world, deepfakes and AI-driven disinformation have already muddied elections in places like Slovakia, where a faked audio tape of a candidate discussing vote rigging spread just days before the polls, and Moldova, where a deepfake video of the president endorsing a pro-Russian party aimed to erode trust in the electoral process. In Bangladesh and India, deepfakes targeting politicians have gone viral, particularly in communities with lower digital literacy. This amplifies risks in countries with lower digital capacity — and little recourse for victims or fact-checkers.

Even in the United States, where regulatory efforts are underway and public awareness is higher, the deepfake threat is growing. The 2024 election cycle saw the first widespread use of AI-generated robocalls, fake news sites, and manipulated videos targeting candidates and voters alike. In one case, a robocall using Joe Biden’s voice urged New Hampshire Democrats to stay home—a clear-cut attempt at voter suppression. The U.S. Federal Election Commission and Congress are now considering new rules to regulate AI-generated political ads, but the pace of campaign innovation continues to outstrip the speed of political regulation.

The real danger is not just that a single fake video might fool voters, but that the sheer volume and sophistication of AI-generated content could eventually make people doubt everything they see and hear (a “hall of mirrors” in intelligence speak). This so-called “Liar’s Dividend” allows candidates and their supporters to dismiss real evidence as fake, undermining accountability and weakening democratic institutions everywhere. Watermarks, fact-checking networks, and disclosure rules are being tested, but none have scaled to meet the moment. As deepfakes become more convincing and common, the ultimate challenge may not be technological, but philosophical: defending the very idea of truth in the public square.

This Memorial Day weekend, Mission: Impossible – The Final Reckoning marks the end of an era: Tom Cruise’s final outing as Ethan Hunt. For nearly three decades — since the franchise’s cinematic debut on May 22, 1996 — Cruise has sprinted across rooftops, clung to planes, and redefined action cinema while performing his stunts. As the torch is poised to be passed, the question looms: Who could follow Cruise?

Enter Zac Efron. Once the fresh-faced teen heartthrob of High School Musical, Efron is now a rugged, remarkably fit, and surprisingly seasoned 37-year-old actor. He’s grown up, and so has his audience. While no credible reports or industry rumors have linked Efron to the Mission: Impossible mantle to my knowledge (not even from the indomitable Matthew Belloni), perhaps I can play Hollywood power broker here for a moment.

Efron occupies a unique space in Hollywood. He’s recognizable but not overexposed, charismatic, athletic, and — crucially — unclaimed by the Marvel or DC universes. Many popular contenders like John Krasinski, Chris Pine, Chris Pratt, or even Tom Holland are already typecast as iconic heroes or superheroes, making it hard for audiences to see them as Ethan Hunt. Efron is a familiar face but still capable of surprising audiences, and his lack of action movie roles could work in his favor.

There’s also a certain meta aspect to Efron stepping into Cruise’s shoes. The two share more than a jawline: in July 2021, Efron posted a comedic Mission: Impossible-style video on Instagram, staging a daring COVID-era “rescue” of his grandfather from a nursing home — complete with the iconic theme music and playful stunts. Years earlier, Cruise himself taught a then-23-year-old Efron how to ride a motorcycle, inviting him to his home for a two-hour lesson on one of the Triumph bikes used in Mission: Impossible III.

Efron’s about the right age, too. Cruise was 34 when the first Mission: Impossible hit theaters in 1996. Efron, now 37, is roughly the same age, while other contenders are already well into their 40s, or too young to be believable as IMF superagent Hunt. This is a similar challenge to recasting James Bond, except that this would be the first such change for the M:I franchise. And speaking of Bond, two words: Taron Egerton (35).

What Mission: Impossible needs isn’t just another action star who can punch people and shoot guns — it needs someone fit, charismatic, surprising, and ready to evolve. Efron’s a blank slate with an edge. His 2023 turn as a retired wrestler in The Iron Claw transformed him to the point he was hardly recognizable. And his recent guest appearance on Apple TV’s The Studio reminded viewers that he remains a relevant and dynamic artist.

No, there’s no buzz on this (yet). But I think maybe there should be. Efron isn’t just a familiar face with all the qualities described above — he’s the Cal Ripken Jr. of Hollywood: a workhorse with an IMDb page that reads like a marathon, not a sprint. Since his professional debut nearly a quarter century ago, Efron has racked up over 50 acting credits across film and television, rarely missing a year without a project. He’s tackled everything from musicals and comedies to dark dramas and thrillers, earning a reputation for reliability and versatility– no headline-making injuries, no mysterious absences, just steady, consistent work. Those are important qualities in someone who would have to helm three, five, or even seven action movies over more than a decade.

With Paramount Pictures — the studio behind Mission: Impossible — currently being acquired by Skydance, the franchise could soon enter a new era of leadership and creative direction. If Mission: Impossible is to be rebooted for a new generation, why not cast someone already trained by Tom Cruise himself?

Your mission, should you choose to accept it: take Zac Efron seriously.

Goldman Sachs’ Jared Cohen and David Solomon discussing geopolitics

The latest U.S.-China trade escalation reveals something more calculated than typical commercial sparring. While Trump's 125% tariffs on Chinese imports sparked Beijing's 84% retaliation on American goods, the fascinating story lies in what China chose to target—and why.

In recent months, the U.S. imposed tariffs as high as 145% on Chinese imports, prompting China to retaliate with tariffs up to 125% on American goods. This standoff brought bilateral trade to a near standstill and rattled global markets. However, after tense negotiations in Geneva, both sides agreed to a 90-day truce: U.S. tariffs on Chinese goods are now temporarily reduced to 30%, and China’s tariffs on American imports are reduced to 10%. While this has eased immediate economic pressure and buoyed financial markets, most pre-existing tariffs and non-tariff barriers remain in place, and the threat of renewed escalation still looms.

We're past soybeans and steel. Beijing has aimed its economic artillery at America's cultural crown jewels: Harley-Davidson, Jack Daniel's, Coca-Cola, and Crayola—emblems of American life that have spent decades embedding themselves in global consciousness. When China taxes these exports, it's not just imposing economic pain—it's attempting to sever the emotional connective tissue between America and world consumers.

These cultural icons are serious revenue generators. Coca-Cola, for example, reported $47.1 billion in global revenue in 2024, with China cited as a key driver of growth; its China business saw improved trends and increased marketing investments. Despite recent struggles, Harley-Davidson has spent years cultivating a Chinese customer base. Crayola is expanding its footprint in China, with its first international Crayola Experience set to open in Beijing in 2026, tapping into China’s booming family entertainment market.

To be sure, China's retaliation extends to strategic sectors like aerospace, where Boeing faced the potential collapse of aircraft orders. China recently lifted its Boeing delivery ban after the tariff truce, highlighting how quickly access can vanish. With China accounting for 10% of Boeing's backlog, political shifts mean hundreds of millions at stake.

But targeting lifestyle brands reveals Beijing's sophisticated understanding of soft power economics.

This precision targeting forces American C-suites to confront uncomfortable questions: How exposed is our business to Chinese political risk? What percentage of our valuation depends on access that could vanish overnight? For investors, the calculus shifts from growth projections to geopolitical resilience. Analysts must now factor “China-proofing” into their recommendations.

Investment banks are responding to this new reality. Goldman Sachs and Lazard, for example, have launched dedicated geopolitical advisory practices, reflecting how geopolitical risk is now a central concern for corporate boards and investors. Last year, in a discussion with the bank’s President of Global Affairs, Jared Cohen, Goldman CEO David Solomon noted that, “geopolitical competition is sending more frequent shockwaves through the global economy, and investors are paying increased attention to great-power politics.”

While some companies might successfully reshore or diversify, others, particularly those whose brands embody American identity, may find China permanently closed.

This transcends traditional trade warfare. We're witnessing superpowers weaponize economics to erode cultural influence. Just as Hollywood and Starbucks once surfed waves of American soft power across the Pacific, today's tariffs expose how politically fragile brand affinity has become. If China can reshape global consumer preferences through strategic retaliation, we're not merely in a trade dispute—we're in a battle over which nation sets the world's cultural defaults.

The tariffs themselves may prove temporary. Their psychological impact on markets, minds, and the machinery of global influence could resonate for decades.

Is American exceptionalism in decline—or are we looking in the wrong direction?

Yes, we ceded America’s machine tooling advantage somewhere around the Kennedy administration. Anyone in the workforce under 25 runs the risk of slipping into a TikTok fugue state at their desk. Billionaire investor Ray Dalio has taken 1,000 pages to diagnose the U.S. with late-stage decadence. And China? It’s building megacities, acquiring raw materials, and funding science at scale. The decline narrative writes itself.

But about a half hour into a recent All-In Podcast, Coatue's Philippe Laffont delivered a sharp reframe: we're not witnessing an end to American exceptionalism—rather, a new beginning is just coming into focus. His thesis cuts through the nostalgia: America dominates artificial intelligence. The full stack. OpenAI to Anthropic, NVIDIA to AMD, cloud infrastructure to foundational models—from Silicon Valley to Boston, America has built a flywheel of tech talent, risk capital, and institutional agility that no other power center—not China, not the EU, not India—can match.

America didn't ‘lose’ machine tooling. We transcended it.

The world’s industrial base is evolving. Yes, China has physical scale and a command economy that can build bridges, cities, and chip fabs on demand. But much of it is about replicating, not inventing. Crazy Rich Asians-style capitalism—garden parties, jazz bands, Roaring 20s glamour—feels more like an homage to America’s Gilded Age than a leap into the unknown.

Meanwhile, America writes the future's operating system. AI isn't another app—it's the new infrastructure. And we're not just competitive; we're dominant. The top models, chips, cloud platforms, and VC-backed experiments are American. Microsoft alone processed 50 trillion AI tokens in March 2025, a surge that signals both usage and the scale of American AI ambition.

Reality intrudes, naturally. AI's energy appetite strains our aging power grid. Data centers already consume 4% of national electricity, heading toward 12% by 2030. A third of our electricians are approaching retirement. China plays the long game, investing in open-source AI, cultivating its digital sphere, and exporting affordable solutions to emerging markets while we debate guardrails.

At home, policy wonks are still wrestling with AI ethics, privacy, and questions of existential risk. But for all our hand-wringing, America remains the world's premier laboratory for what's possible. Our private sector moves quickly, our universities magnetize global talent, and our messy, maddening system still rewards invention over imitation, risk over repetition. Of course, sustaining this edge isn’t automatic: smart public policy is essential. For example, a Trump White House executive order aims to clear away regulatory barriers and solidify U.S. leadership in AI by promoting innovation and national security.

Through the lens of innovation velocity, America isn't declining—it's accelerating. Exceptionalism was never our birthright. It's our daily choice. And so far, we're still choosing wisely.

American exceptionalism isn't dead. It just looks different now. And if history teaches anything, that's precisely how it should be.

Before Tony Soprano strangled rivals in New Jersey, before Walter White cooked meth in his tighty-whities—there was a man in black breathing mechanically through a mask, crushing windpipes with his mind. We rarely frame Darth Vader as America’s prototype antihero, but the evidence stares back at us through that obsidian mask.

The modern antihero thrives in the razor-thin space between villainy and virtue. Tony Soprano battled panic attacks. Walter White battled cancer. Donald Trump? His supporters argue he battles “the system” itself—a real estate mogul turned dark knight against DC's Death Star. But this framework—the damaged man committing atrocities for allegedly noble reasons—was already perfected in 1977.

Vader terrified audiences without becoming cartoonish. Even in A New Hope, complexity bled through: his deference to Tarkin, his position as enforcer within another's empire. By Empire Strikes Back, he’s hunting not just rebels but his bloodline, embodying those opposing forces Trump later branded as “victim and strongman.” When Vader's mask finally comes off in Return of the Jedi, we confront the truth: beneath the monster lies a dying father, desperately grasping for redemption.

What makes Vader the ur-antihero isn't just his villainy—it's his narrative chemistry. He proved audiences would embrace the fallen if their wounds cut deep enough. This same chemistry now fuels political theater. When Trump's 2016 rise coincided with what scholars call a "zeitgeist primed for antihero worship," supporters cast him as their Breaking Bad protagonist—the outsider dismantling corruption from within.

Admittedly, not everyone buys Vader’s antihero credentials. Critics point out that his atrocities far outstrip those of Tony or Walter, and that his poignant redemption arrives only at the saga’s end, hardly enough to erase a lifetime of villainy. But it’s precisely this tension between horror and empathy that seeded the antihero archetype in pop culture.

The parallels turn surreal. Just a few days ago, Trump's team circulated AI images of him wielding a red lightsaber while branding Democrats "the Empire." The imagery reveals how thoroughly antihero DNA has infected our politics. Vader's legacy transcends fiction—it lives in leaders who weaponize moral ambiguity.

Vader may have walked so Tony Soprano could run–but did he walk so Trump could sprint? The answer lurks in storytelling's dark heart. Antiheroes seduce us by reflecting our pain back as power. Vader channeled primal fears of failed fatherhood; Trump channels rage at institutional decay. Both prove the most dangerous character isn't the villain—it's the villain who makes us see ourselves.

Vader needed just three films to complete his fall and rise. Trump's saga remains unfinished, leaving America trapped between Empire and Rebellion, unsure which side we're on.