Trump Is Selling a Phone + The Start-Up Trying to Automate Every Job + Allison Williams Talks ‘M3GAN 2.0’
Summary
- Trump Mobile packages wholesale network capacity into a $47.45 monthly service tied to Trump branding, while the T1 Phone 8002 gold version seeks a $100 preorder toward a purported $499 price. Casey Newton’s MVNO shorthand is “cheaper for worse service”: customers may be deprioritized at busy times, but the operator avoids building towers. The phone’s Android 15 and “made in the USA” claims look harder to reconcile with economics—the only domestic benchmark cited, the Liberty Phone, starts at $1,999.
- The sharper risk is that a presidential side business creates fresh channels through which regulated companies could purchase influence. Trump appoints the FCC chair, while Amazon or Meta could hypothetically pay to preinstall apps on the Trump phone—what Casey calls “a new avenue essentially for bribery.” A recent disclosure reportedly showed Trump made $57 million from his family’s crypto firm last year and put his crypto holdings near $1.7 billion at the conservative low end.
- Kevin Roose sees Trump’s ventures as a replicable playbook for converting attention, reputation, and political loyalty directly into cash. Mint Mobile sold for more than $1 billion, celebrity MVNOs are proliferating, and meme coins created another monetization route. Casey’s darker formulation is that Trump has learned to “monetize tribalism”; Kevin described loyal supporters paying more for products that may be less valuable, while Casey warned that repeated shocks can become ordinary.
- Mechanize is building scored virtual workplaces—“very boring video games”—in which AI agents repeatedly practice software engineering and eventually other professions. Backed by investors including Patrick Collison and Jeff Dean, the startup supplies reinforcement-learning environments containing tools such as GitHub, Slack, email, and spreadsheets; AI companies then use them to train their own models. Its declared destination is the automation of all labor.
- Mechanize does not need full automation on venture timelines: Ege Erdil says automating 20% of current jobs within five years would already be “insanely valuable.” The founders expect near-term AI to automate tasks rather than whole professions, potentially raising software-engineer productivity and wages while humans retain coordination, planning, testing, and cross-team work. Full replacement might take decades.
- The founders’ ethical case is that economy-wide labor substitution might raise growth by 10 times or more and create enough production to overwhelm the costs of displacement. Ege’s formulation is that “the secret to mass consumption is mass production,” with sovereign-wealth-style distributions or expanded public benefits supplying income after wages disappear. Kevin’s pushback is the load-bearing one: technology may help in the long run, but “people don’t live in the long run.”
- Mechanize offers no concrete transition policy and argues that detailed planning before the disruption becomes legible is “overrated.” Kevin counters that societies stockpile for foreseeable pandemics even without knowing their timing; Matthew concedes that the broad answer probably resembles the past century’s Social Security, Medicare, Medicaid, unemployment insurance, and greater redistribution. He also admits that his utilitarian calculus can sound cold to workers already frightened by automation.
- M3GAN 2.0 turns the same debate into questions of parenting, creative ownership, and whether humans should relate to AI rather than merely extract from it. Allison Williams stopped letting her three-and-a-half-year-old question voice-mode ChatGPT after he named “the person who talks from your phone” Chapatiti; her rule became “less is more.” As an actor, she wants a synthetic likeness compensated like her physical performance and believes the “tiny moments where I’m bad at my job” may be precisely what keeps art—and employment—human.
Deep dive
1. Trump Mobile turns political identity into telecom margin
Trump Mobile is an MVNO: rather than spend billions building towers, it buys unused capacity from established networks at wholesale prices and resells access. Casey’s understanding is that it will bundle capacity from multiple carriers rather than rely on one network.
Casey’s blunt pitch for the category is “cheaper for worse service.” A direct AT&T or Verizon customer might pay more than $80 monthly for priority during congestion; an MVNO customer might pay $30 or $40 and accept slower busy-time service. Trump Mobile lands at $47.45, referencing Trump’s status as the 45th and 47th president.
The model has real precedent. Mint Mobile, founded in 2016 and partly owned by Ryan Reynolds, sold to T-Mobile eight years later for more than $1 billion; Kevin recalled estimates that Reynolds made roughly $300 million. Even the Smartless podcast has launched Smartless Mobile.
2. The $499 T1 requires a supply-chain miracle
The T1 Phone 8002 gold version is advertised as a gold-colored Android phone running Android 15, “made in the USA,” and purportedly selling for $499. Prospective buyers are being asked for a $100 preorder despite having seen only one rendered image, which some people have called a concept rather than a finished device.
Kevin’s supply-chain objection: US production requires not merely domestic assembly but specialized component fabrication and precision equipment. Casey added that celebrity-license operators are rarely the manufacturers that suddenly achieve “supply chain miracles” while delivering a premium product at an unusually low price.
Their benchmark was the Liberty Phone, described as fabricated and assembled in California, with a $1,999 starting price. If Trump’s family can deliver at one-quarter of that, Kevin would be impressed; otherwise, the unresolved possibilities are hidden shortcuts, a later price increase, or a claim “out of thin air.”
3. The phone creates new ways to purchase presidential favor
Telecommunications is heavily regulated, and Trump appoints the FCC chair. Casey argued that Brendan Carr would now have to consider how policy affects Trump Mobile and the Trump phone; Kevin agreed the conflict would become especially acute if the initially small MVNO ever competed materially with large carriers.
App preinstallation supplies another mechanism. Manufacturers routinely charge technology companies to ship apps on new phones, so Amazon or Meta—both with substantial government business—could offer Trump’s company generous terms for prominent placement. Casey’s framing: that opens “a new avenue essentially for bribery.”
Crypto shows how quickly the influence model can scale. Citing a recent financial disclosure, Casey said Trump personally made $57 million from his family’s crypto firm last year and held roughly $1.7 billion in crypto at the conservative low end, while appointing the SEC chair overseeing the sector.
Kevin called Trump’s meme coins, NFTs, phone, and network a “roadmap” for monetizing fame and influence. His inversion: the most loyal supporters reward the politician by buying products that may deliver less value. Casey worries repeated shocks become ordinary—“that’s Trump”—until conflicts once considered extraordinary stop registering.
4. Mechanize sells the training grounds for AI labor
Matthew Barnett, Ege Erdil, and Tamer Basaroglu left Epoch AI, a nonprofit research organization, to found Mechanize with an explicit goal other AI companies often soften: automate all labor. Investors include Patrick Collison and Jeff Dean.
Their Epoch research suggested that AI capable of substituting for workers across the economy might increase growth by 10 times or more. Because an AI workforce can scale faster than a human one, the founders expect far more goods, services, medicine, and technological progress—including advances that money cannot currently buy.
Mechanize’s product is a collection of virtual workplaces with tasks and scoring. Agents use spreadsheets, email, Slack, GitHub, and other professional tools; repeated success or failure becomes the reward signal through which an AI company trains its model.
Kevin’s best analogy was “very boring video games”: the model repeatedly plays at being an engineer, lawyer, or accountant until it improves. Software engineering comes first, with data science a possible adjacent target; podcasting, Ege joked, would require “a different kind of reward signal.”
5. The venture case only needs to automate 20% of jobs
The founders resisted claiming that the next five or 10 years are qualitatively different from earlier automation. One said two co-founders believe full automation may take many decades; the nearer-term expectation is continued task automation inside professions, not immediate replacement of most workers.
Coding alone is not software engineering. Engineers coordinate across teams, plan projects, test whether software meets specifications, and integrate several kinds of work. If AI handles code but not those surrounding responsibilities, the founders expect greater productivity and potentially higher wages rather than extinction of the profession.
The financing logic does not require Mechanize to reach its ultimate mission quickly. Ege called automating 20% of current jobs within five years “very ambitious,” but said that milestone alone would be “insanely valuable” by conventional venture standards.
6. Radical abundance does not settle the transition problem
The founders’ ethical case is explicitly consequentialist: automation has costs, including lost jobs, but those must be weighed against vastly cheaper production and a much higher standard of living. Ege said, “The secret to mass consumption is mass production”—prosperity requires the goods and services people are meant to consume.
Kevin accepted that mechanization generally improved life relative to the backbreaking labor of earlier generations. His pushback was temporal: “People don’t live in the long run.” Every technological revolution leaves some people unable to cross smoothly into the next economy, whatever aggregate benefits eventually arrive.
Ege argued that, if AI substitutes for every worker, wages need not remain the source of income. Countries already distribute returns from natural-resource endowments through governments or sovereign wealth funds; an AI economy could use a comparable mechanism once human labor can no longer compete.
The founders also rejected the premise that work uniquely supplies meaning. Someone in 1800 might not have imagined publicly funded education or university as rewarding ways to spend time outside farming; they expect new institutions and activities to emerge, while conceding that benefits only spread if production is distributed at least somewhat broadly.
7. The founders decline a job-loss policy blueprint
The founders did not predict mass unemployment within the next five or 10 years: one said that world is “definitely more than 10 years away,” while Matthew likewise ruled it out over the next few years and said policy discussion was premature.
Matthew considers distant policy blueprints “overrated.” Ten years ago, few people could have designed an effective 2025 response to LLM displacement; he prefers disclosing Mechanize’s intentions now, then letting governments use richer evidence and better tools when disruption becomes concrete.
Kevin’s counterexample was pandemic preparation: uncertainty about timing does not prevent vaccine manufacturing or stockpiling. Matthew conceded that the right family of responses likely extends existing redistribution—Social Security, Medicare, Medicaid, unemployment insurance, and broader public support—without endorsing UBI or another precise design.
Kevin closed with workers’ fear: listeners already report bosses changing or threatening their jobs with AI. Matthew said he does have empathy, but admitted it “feels cold” beside his conclusion that benefits exceed costs; that is how “utilitarian calculus” sounds, even when he considers the project unusually positive-sum.
8. M3GAN 2.0 treats AI as a relationship already under way
The film, due June 27, deliberately incorporates ideas including instrumental convergence and the paperclip maximizer. Williams credited director Gerard Johnstone for avoiding meaningless technical gobbledygook: the team wanted “praise from the 100 people who know what we’re talking about.”
Williams sees the first M3GAN as a hypothetical that became urgent before release; the sequel says, “Hypothetical over. We are here. Now let’s have an ethical conversation.” Its question is whether people can move from parasitic use—“take and take and take”—to a relational posture toward systems they brought to life.
That tension reached her home through voice-mode ChatGPT. Her three-and-a-half-year-old used it for perfectly calibrated explanations and follow-ups, then called it Chapatiti, “the person who talks from your phone.” Williams pulled back: children need enough information “to wonder more,” not an endlessly responsive machine that fills every gap.
9. Human imperfection is the franchise’s creative moat
Williams’s first concern about generative AI is job security. Her defense is not technical perfection but its opposite: stray hair, smeared lipstick, slurred words, inconsistent handwriting, and continuity errors. “I kind of rely on the tiny moments where I’m bad at my job to save my job.”
If a future M3GAN used her digital likeness, she would demand compensation equal to her physical participation so synthesis was not the vastly cheaper choice. She already hears AI-generated versions of her voice in draft trailers; she then records the lines herself, and says the human pass makes them sound “worse” in the more normal, human sense.
The sequel moved from horror toward action because its expanding stakes pushed the story there—not “the tail wagging the dog.” The creative constraint was retaining the first film’s thriller DNA and transporting its characters into a world shaped by the existence of Terminator 2 without building a one-for-one copy.
Williams said a simple “AI bad” moral would be “a deeply dick move” now that the technology is embedded in society. The sequel instead adds asterisks to Gemma’s caution. Its camp also works only because the performances remain earnest; excessive self-awareness or camera-winking would make the franchise exhausting rather than tonally distinctive.