The Leverage Podcast

Read the original essay here: https://www.gettheleverage.com/p/rich-enough
0:00 The Agony of Missing Out
1:06 Quantifying the AI Wealth Boom
3:53 The Hidden Cost of Mega Wealth
6:21 The Logarithmic Law of Happiness
7:26 Redefining What It Means to Be Rich

Subscribe to my substack: https://www.gettheleverage.com/This week, the CEO of Cloudflare announced that bots had passed human traffic for the first time in the internet’s history. By their count it’s 57.4% bots to 42.6% humans.Many of the economic assumptions of our world are constructed on the basis of humans being the ones who look at, and operate computers. Facebook sells ads because they have eyeballs looking on websites they control, and the brains behind those eyes are convinced to buy stuff with the ads they show. Content providers like me put stuff online assuming we can monetize a reader relationship through subscriptions or ads. This has done enormous good (independent media companies like mine can exist) and enormous harm (engagement, rather than truth, becomes what systems like Instagram’s algorithm optimize for).In our current internet, like as of the moment you are reading this, that old economic equilibrium is kaput. Irrelevant. Toasted and buttered bread. Aka, cooked. AI agents search differently, they use the internet differently, and most importantly they monetize very differently.CHAPTERS:00:00 Humans Become Minority00:33 Attention Economy Breaks01:32 AI as Content Middleman03:49 Turned Into a Skill04:59 Skills That Still Matter06:07 Deflationary AI Economics08:21 Taxing the Agents10:56 Bots Buy Everything11:58 Power Shifts and Scarcity13:16 AI Proof Your Career

Subscribe to my Substack: http://www.gettheleverage.comSpaceX is one of the most ambitious and historically important companies ever built… and a potentially wildly overvalued stock investment. In this episode, we explore the history of SpaceX and what you need to understand about their IPO.TIMESTAMPS:00:00 SpaceX Vision vs Stock04:36 Grit Becomes Culture05:47 S1 Fantasy Markets08:09 The Business Breakdown10:17 Why It Might Work?13:06 The Muskian Multiple14:54 AI Hype And Vibes17:09 Key Man Risk

Subscribe to my Substack: http://www.gettheleverage.comDesigners used to be the monarchs of software. Now they're being squeezed out of existence. It's not just them.In this episode, I break down how AI has automated both sides of the design workflow, what that's done to hiring, and what this means for every knowledge worker right now.YouTube Chapters:00:00 — Designers Used to Run the Show01:15 — How AI Automated Both Sides of Design02:30 — The Data: Design Hiring Has Flatlined03:57 — The Three AI Workflow Paradigms06:06 — Node-Based Systems and the New Design Stack07:08 — "Designers Are Basically Just Cops Now"08:23 — What AI-Native Companies Actually Want From Designers09:05 — The Real Question: Could This Happen to You?09:40 — The Two Roles That Survive: Junior Execution and Taste Executive12:20 — Evan's Personal Fear and Why He Made This Video12:56 — The Only Choice You Have Left

Subscribe to my Substack: http://www.gettheleverage.comThere's a great contradiction of Tim Cook as maybe the best CEO of the last decade, and also maybe the one who has set Apple up to fail.He just announced his retirement. He's gonna step down in September. You're going to see two types of commentary about Tim Cook. One is that he is a genius who created so much value. The other is that Apple's product was bad and never innovated.

What I'm here to tell you is that both of these people are actually wrong. The answer is weirder than you might think.

Timestamps:

00:00 Tim Cook Is Retiring

01:36 Apple Silicon Masterstroke

02:43 Where Apple Thrived

05:33 Tim Cook Is An Operator, Not A Visionary

06:05 Apple Car and the Vision Pros

10:20 Where Apple Intelligence Went Wrong

12:13 Why Apple Lost the Narrative

14:24 The Next CEO's Opportunity

 Every law, every investment thesis, every AI-related career is built around the assumption that we can forecast where these models are going to go, that we'll be able to control them, understand them, and therefore build and invest appropriately.But Claude Mythos shows that the more capable these models get, the worse we are at understanding what is going on. So we need to talk about what the model is and what it can do before we can talk about the implications of it.

Subscribe to my Substack: http://www.gettheleverage.com

Timestamps00:00 Hype Versus Reality

00:49 Meet Mythos Model

01:48 Cybersecurity Shockwave

02:44 Benchmarks Are Breaking

04:49 Model Taste And Vibes

05:49 Why Mythos Improved

07:09 Recursive Acceleration Fears

08:55 Career Impact Syndrome

10:59 Company Productivity Bottlenecks

12:32 Prepare For The Shift

https://www.gettheleverage.com/- Update your information diet and perspective on technology markets with my weekly newsletter.Within two years, your laptop will run an AI as smart as the one that you're paying $200 a month for.

That changes the power dynamics of who makes money in the AI industry, what power individual consumers have, and even how computing itself works.

Timestamps:

00:00 The Future of AI

01:22 Gemma 4's Breakthrough

02:45 TurboQuant Memory Compression

04:07 From Data Centers To Devices

06:02 Who Wins Economically?

07:38 Cloud Providers Still Matter

08:56 Privacy And Sensitive Use

10:23 The Risk

12:15 Final Thoughts

Watch on YouTube • Listen on Spotify • Listen on Apple

Here is something that actually happened: A company put advertisements inside a coding tool—the kind of tool used by senior software engineers at serious enterprises who, as a demographic, are famously resistant to being marketed to—and instead of the internet erupting in the predictable conflagration of outrage, the company generated somewhere between $5 million and $10 million in annualized revenue within a month.

Quinn Slack, co-founder and CEO of Sourcegraph, announced something called “AMP Free” in October, watched his X post accumulate 900,000 impressions, and—this is the part that made me sit up—had advertisers signing six-figure contracts between 6 PM and 8 AM the night before launch. Which is to say, companies were so eager to advertise inside a coding agent that they were doing paperwork at 2 AM.

The implications here extend considerably beyond one startup’s monetization experiment, though if you’re the kind of person who tracks startup monetization experiments (and if you’re reading this, you probably are), it’s a pretty interesting one.

Quinn is explicitly building towards an ad network that could serve other coding agents and developer tools, which is another way of saying he wants to become the Google of developer attention. His thesis—and it’s the kind of thesis that sounds either visionary or delusional depending on which assumptions you accept—is that ads are the only business model capable of driving the scale necessary to justify the truly staggering datacenter buildouts currently underway, and that ad-supported agents become what he calls a “sponge” for unused GPU capacity.

Advertisers, meanwhile, are apparently willing to pay $500 to $1,000 per “qualified action,” which in this context means a developer who not only clicks on an ad but actually implements an API key. If Slack is right about all this, the entire AI infrastructure stack just found its demand floor. If he’s wrong, well, at least someone tried something genuinely weird.

For operators and investors this interview functions as a kind of masterclass in contrarian positioning. While Slack’s competitors are racing to win enterprise deals by discounting their products 85-100% (which, just to be clear, means they are sometimes giving away their product entirely in exchange for the privilege of having a customer), Slack is deliberately taking only 10% of deals in order to preserve what he calls “product velocity.” The bet is that staying on the frontier matters more than short-term market share, and that ads let you answer to users voting with their feet rather than disconnected enterprise buyers who want commitments to things like “self-hosting” and “model choice.” It’s either brilliant discipline or extremely sophisticated cope—and the next twelve months will tell us which. But $5-10M ARR in the first month is, as they say, pretty baller.

Ideas & Analysis

1. Ads Are the AI Demand Floor

Thesis: Ad-supported AI agents create guaranteed demand for GPU capacity, de-risking datacenter investments and enabling more ambitious infrastructure buildouts.

Quotes:

“An ad support coding agent is essentially like a sponge for any unused tokens from good models out there. And so it lets the whole world be bolder in all these data center build outs.” — Quinn Slack

“Ads are the only way to truly drive the kind of scale that we need to get every single person using a coding agent. And that ultimately drives economies of scale that is going to support all of these data center build outs and that can make it so that everyone’s CapEx plan can be 25% more ambitious because they know that the moment all those GPUs come online, they’re going to have a use.” — Quinn Slack

Analysis:

The quiet insight here—and it took me a moment to catch it—isn’t really about advertising at all. It’s about infrastructure finance, which is to say it’s about the thing that makes all the other things possible. Consider that hyperscalers and model providers are currently making trillion-dollar commitments on datacenters that will not, cannot, be fully utilized on day one. The traditional playbook assumes demand will materialize through paid subscriptions and API usage, but that’s fundamentally a bet on conversion rates and price elasticity, which is another way of saying it’s a bet on human behavior, which is notoriously difficult to predict. What Slack is arguing is that ad-supported agents flip the entire equation. Instead of hoping users will pay, you guarantee utilization by making the marginal cost to users zero. The GPU hours get monetized through attention rather than direct payment. It’s the same economic logic that made broadcast television work, back when broadcast television was a thing people cared about: you don’t charge viewers; you charge Procter & Gamble for access to their eyeballs.

The second-order effect is competitive. If Quinn is right, companies without an ad-supported tier are leaving demand on the table and ceding scale advantages to those who have one. The counterargument is obvious and has probably already occurred to you: developers hate ads, or at least they say they do, and the market will punish anyone who degrades the experience. But Slack’s 95% advertiser close rate and the 900K-impression post suggest the market is at least curious, which is not the same as enthusiastic but is considerably better than hostile.

2. Coding Agents Have Google-Like Ad Potential

Thesis: Coding agents uniquely combine high-intent search behavior with always-on engagement, creating an ad surface superior to either Google or Instagram alone.

Quotes:

“The cool thing about a coding agent is you actually have the opportunity to do both because it’s up on their screen, it’s in their workflow at all times, kind of like people are on Instagram all the time... but then they’re also going to it with high intent. And you don’t really go to Instagram with high intent for specific purchases or actions, you go to Google. But yeah, the coding agent has both.” — Quinn Slack

“There’s no other kind of ad that delivers customers that are so well-qualified and far along on implementing... We’ve heard from our ad customers that they would be willing to pay $500, $1,000 for that kind of really highly qualified lead.” — Quinn Slack

Analysis:

Slack is making a structural claim about attention quality, and it’s the kind of claim that’s either profound or tautological depending on how generous you’re feeling. Here’s the argument: Google wins on intent (you’re searching for something specific, you want an answer, you are a person with a problem), Instagram wins on time-on-platform (you’re scrolling endlessly, you have perhaps forgotten why you opened the app, time has become somewhat irrelevant), but neither platform has both. A coding agent, by contrast, is open all day and captures discrete moments of high commercial intent—specifically, the moments when a developer decides they need authentication, payments, or database infrastructure. The targeting data is also considerably richer than what you’d get elsewhere: not just “what did they search for” but “what’s actually in their codebase” and “how many people are working on this repo.”

This is an idea I strongly believe in and endorse. Ads integrated into B2B workflow products is a market no one has really cracked, and if AI finally makes it possible, hundreds of billions of dollars are suddenly available.

The $500-$1,000 CPL quote is, I think, the buried lede here. That’s not display ad pricing—that’s lead-gen pricing for enterprise SaaS, which operates according to entirely different economics. If AMP can actually deliver a qualified lead who has already implemented an API key, the comparison isn’t Google Ads; it’s a sales development rep who never sleeps and never misses a buying signal and doesn’t require health insurance.

The risk, obviously, is execution: can they actually build the attribution and action layers necessary to prove that value, or will advertisers pay for a few months, see murky ROI, and churn? (Advertisers are famously patient about murky ROI, which is to say they are not patient about it at all.)

3. Trust Is the Moat for AI Ads

Thesis: Separating ads from AI recommendations—like Google separates organic from sponsored results—is essential to maintaining user trust in agentic products.

Pull Quotes:

“It’s really important to know we do not inject ads to steer what the AI is doing or saying or recommending to you. It’s entirely separate, just like in Google, how the organic results are separate from the sponsored results.” — Quinn Slack

“Trust is so important. And this is why Google, you could say, they have all the temptation in the world to just start showing ads as organic results. But you can stop going to Google if they do that. And coding agents, it’s a really highly competitive space.” — Quinn Slack

“I think there’s a lot of other coding agents that if they came out with ads, people would say, that’s just going to be junk. But because that trust we had with AMP, we were able to try this and ultimately build something I think is valuable.” — Quinn Slack

Analysis:

The Google analogy is doing a tremendous amount of work here, and it’s clever framing—perhaps too clever, in the way that things designed to be persuasive sometimes are. Google’s entire business depends on users believing organic results are unbiased, even as the company makes $200+ billion per year from ads displayed alongside them. (This is one of those arrangements that sounds impossible when you describe it but has somehow persisted for decades.) Quinn is arguing that the same church-and-state separation can work for AI agents: the model gives you unbiased recommendations, and the ads are clearly labeled and shown separately, and everyone understands which is which, and nobody feels manipulated. The mechanism that enforces this arrangement is competition: if users feel manipulated, they switch to Cursor or Claude Code or Codex. The switching cost for coding agents is low enough that trust violations get punished quickly, which is either reassuring or terrifying depending on whether you’re building one.

But there’s a tension here that Slack acknowledges without fully resolving, and I found myself underlining the relevant passage. When asked why AMP doesn’t inject tool recommendations directly into the AI’s suggestions—which would, one imagines, be considerably more valuable to advertisers—he says they could do that, if it’s “clearly marked” and comes with “additional benefits to the user.” And look, maybe that’s fine. But that’s also a slippery slope, or at least it’s the beginning of a slippery slope, or at minimum it’s standing near a slope and commenting on how slippery it looks.

The line between “here’s a sponsored recommendation that’s relevant” and “the AI is now a salesperson” is exactly the line Google has been blurring for years with Shopping results and AI Overviews and all the other things that are technically ads but don’t look quite like ads.

5. Enterprise May Accept Ad-Supported AI

Thesis: Large enterprises are considering ad-supported coding agents, using Google Search as the precedent for employees seeing ads in work tools.

Pull Quotes:

“We have gotten some of our big enterprise customers that are interested in using AMP free, ad supported for their enterprise users... The analogy they have is, yeah, this sounds weird. Why would we let, you know, Big Bank let our devs use an ad-supported product, but they use Google and they see ads there.” — Quinn Slack

“If, say, JP Morgan employees go to google.com, there’s no enterprise web search contract that those two companies have signed. JP Morgan is not paying $200 a month for their employees to use Google. It’s a free product, frictionless free product, and they see ads there.” — Quinn Slack

“When you think about how much money these enterprises are spending on coding agents that has been unbudgeted and really hard to prepare for, it’s compelling in the short term.” — Quinn Slack

Analysis:

This is, I think, the most counterintuitive claim in the entire interview, and Slack seems genuinely surprised by it. The traditional assumption is that enterprises will pay almost anything to avoid ads: ads mean data leakage concerns, compliance headaches, and unprofessional optics, which are three categories of problem that enterprise buyers are exquisitely sensitive to. But the Google precedent is actually kind of persuasive, once you sit with it. Fortune 500 employees absolutely use ad-supported Google Search for work queries, every single day, and nobody—not the CTO, not the CISO, not the procurement team—has negotiated an enterprise contract to make those ads go away. If developers already see ads when they Google “how to parse JSON in Python,” is it really that different to see ads in their coding agent? I’m genuinely not sure, which is why I’m asking.

The budget pressure angle is probably underrated in all of this. Coding agents are genuinely expensive—Slack says power users spend $200-300/month, which adds up remarkably fast across an engineering organization—and that spend materialized faster than IT budgets could adapt. If a CFO is staring at an unexpected seven-figure line item for coding tools and wondering how exactly this happened, the pitch “what if some of your devs use the free tier for low-stakes tasks” starts sounding not just reasonable but prudent.

What happens next?

Here’s the thing about ads that makes people uncomfortable: they work. Not always, and not for everyone, and certainly not in the way that advertising executives would have you believe when they’re trying to sell you advertising—but they work often enough and well enough that they’ve funded most of the internet you actually use. And what Quinn is betting on, I think, is that the same economic logic that made Google and Facebook and yes, newsletters, into viable businesses can also make AI into a viable business, which is a

One of the biggest long-term problems that will result from AI is the inability to tell reality from siliconized fiction. Typically when people talk about this sort of stuff, they mean deepfakes or misinformation—an image, video, or essay that spews out lies.

That problem is real, but is one that is not all that new. Misinformation has been around since we invented the printing press. Instead, what I’m personally more concerned about is the inability to tell human and robotic identity apart. Large language models are remarkably good at manipulating people’s emotions and convincing people to change their opinions on topics. In the very, very near future, the internet will be filled with intelligent AI agents that are taking actions on some human’s request. Those actions could be innocuous stuff like booking a flight or it could be about deploying a bot swarm on social media to try to sway people’s votes.

The worse problem is that we have no good answer. We are hurtling towards a future where you can’t tell who’s real online and there isn’t a ton we can do about it. Our current solutions—whether we certify someone is human through little puzzles, texts to their phone, or photo verification of their driver’s license—are relatively easy to fake.

One startup, cofounded by Sam Altman, thinks the answer is that we all need to get a picture taken by an orb.

That camera’s picture will be used as “proof of human” and from there, issue you a cryptocurrency that you can use to move through the internet. Whether that’s genius or dystopia depends on your tolerance for irony—the world’s most prominent AI builder trying to save us from the problems AI created feels a bit like the drug dealer running the rehab.

Still, this might be the best shot we have. So when Tools for Humanity—the team behind Worldcoin—asked to come on the pod, I figured it was worth hearing them out.

In this episode, I talk to Adrian Ludwig, their Chief Architect. I press him hard on the technical and ethical holes in their plan—and to his credit, he doesn’t flinch. If you want to understand whether scanning eyeballs into orbs is our salvation or our surrender, this conversation will give you what you need to decide.

There’s a new class of public intellectual emerging—the tech pessimist who actually knows how the sausage gets made. They’re not your standard Luddites railing against smartphones; they’re former engineers, economists, and journalists who can offer technical sounding explanations for why your Instagram feed got worse. Frankly, most of the time I find these individuals more bombastic than insightful.

Cory Doctorow is different. He is the leader of this new class of commentator while simultaneously being a rigorous thinker about the internet. If you’ve heard of him, you’ve probably heard of the word he invented, “enshittification.” This has become a handwavy term to describe when digital services go bad. Instagram forcing ads down your throat? Enshittification. Amazon being full of junky products? Enshittification. It is a vibe that I, you, and everyone else probably feels.

However, enshittification is not just a vibe. It is a specific theory, buttressed by strong claims about anti-trust and consumer rights. This theory has become the go-to framework for understanding platform decay among left-wing regulators, including the former head of the FTC, Lina Khan.

In our conversation, Doctorow laid out the three-act tragedy: platforms subsidize users until they’re locked in, then squeeze users to subsidize businesses, then squeeze everyone to pay shareholders. To be transparent, I do not fully subscribe to his framework. Specifically, I’m skeptical that programatic advertising is as ineffective as he claims, and I think he underestimates genuine consumer preferences for convenience over privacy. You’ll hear it in our conversation when I push back on topics like the efficacy of Meta’s ads or why consumers continually choose to consume shitty content.

But while I disagree with the totality of his theory, I agree with the underlying emotional feeling. Namely, that the internet can and should be better for people. Consumers should have significantly more rights. Attention economies have consolidated too much power into individual companies and I want regulators to take action so that startups have a fighting chance. In that regard, I found Doctorow to be a kindred spirit.

His chosen solution is not the usual “break up Big Tech” handwaving, but specific, implementable fixes. He wants to force platforms to support data portability through open protocols (great idea). Make reverse engineering legal again (great idea). Stop pretending that surveillance advertising works (meh). These aren’t revolutionary, but coming from someone who can speak so eloquently and passionately, they resonate. The timing of his new book matters too. Regulators are circling and even the courts are starting to question whether “free” services can really be monopolies.

The wildest part? Doctorow thinks Trump’s trade war might accidentally fix everything. The countries that got screwed by American tech companies now have permission to screw back.

Below are the key takeaways of his arguments:

Apps Are Just Websites Plus Handcuffs

Thesis: The primary difference between apps and websites is that modifying an app is a federal crime.

Pull Quotes:

“An app is really just a website skinned in the right kind of IP to make it a crime to defend your privacy while you use it.”

“Under the Digital Millennium Copyright Act you have to reverse engineer them and that becomes illegal... it carries a penalty of a five-year prison sentence and a $500,000 fine.”

“If you care about good product design, you care about products that your users like... then there has to be space for your users to push back... When you have a platform in which everything that’s not forbidden is mandatory, you cannot learn from your users.”

Analysis:

This is the kind of insight that makes you feel stupid for not seeing it earlier. Every growth team in Silicon Valley has the same playbook: nag users to download the app with increasingly desperate popups. Why? Because on the web, users can install ad blockers, modify the interface, scrape data—basically treat your product like they own it. Wrap that same code in an app, and boom, legal protections prevent users from doing any modification.

However, the threat is more theoretical than real for personal use. Since 2010, the U.S. Copyright Office has granted DMCA exemptions for jailbreaking smartphones, explicitly allowing users to modify their devices for personal use without fear of prosecution. These exemptions have been renewed every three years and expanded to include tablets, smart TVs, and voice assistants. Critically, Apple has never prosecuted an individual user for jailbreaking, though distributing jailbreaking tools remains a legal gray area.

The business implications are still brutal and clear. If you’re competing on the open web, you need to actually respect users because they can modify your product whether you like it or not. That’s why web products tend to be cleaner—not because PMs are nicer people, but because users have nuclear weapons (ad blockers). Apps remove that threat, which is why every app eventually becomes a cluttered mess of dark patterns.

Of course, there are legitimate technical advantages that apps offer: push notifications, offline functionality, superior camera/GPS integration, and generally better performance. But the question remains: if these technical advantages were the only draw, why do companies push apps so aggressively even when the mobile web would suffice? The control over user modifications is clearly part of the calculus. In an AI-powered future where browser agents can dynamically modify web pages on behalf of users, the app-first strategy may become less sustainable.

Network Effects Are Actually Coordination Problems

Thesis: People stay on awful platforms not because they like them but because coordinating a group escape is harder than individual suffering.

Pull Quotes:

“You love your friends, but they’re a pain in the ass. And if you can’t agree on what board game you’re going to play this weekend, you certainly can’t agree on when it’s time to leave Facebook.”

“They couldn’t leave because they mattered to each other more than this gross, terrible privacy violation scared them. They loved each other more than they hated Mark Zuckerberg.”

“Building a lot of housing for people in East Berlin when you’re in West Berlin does not mean you’ll get any tenants. You have to tear the wall down.”

Analysis:

Doctorow offered the example of a breast cancer support group that wanted to leave Facebook but couldn’t. Not because they secretly loved surveillance, but because the alternative was losing their support network during cancer treatment. The economics term “collective action problem” sounds bloodless, but we’re talking about real people choosing between isolation and some form of attention exploitation.

Research confirms this dynamic. Studies on platform switching show that even when users express strong intentions to leave a platform due to dissatisfaction, inertia—driven by habits, emotional attachment, and perceived switching costs—significantly moderates whether they actually switch. In one study on social commerce platforms, switching costs were found to strongly moderate the relationship between switching intention and actual switching behavior, meaning people who say they want to leave often don’t follow through.

For anyone building a social product, this reframes competition. You’re trying to solve a coordination problem rather then launch new features. Discord didn’t beat Skype by being better; they gave entire communities a reason to move together (gaming servers). Same with Slack and email (whole companies switching at once). The lesson: stop trying to poach individual users from incumbents. Instead, find natural groups with shared incentives to move together. Churches, gaming clans, companies, schools—any pre-existing organization that can coordinate its own exodus. That’s why every successful social platform started with a specific community (Facebook with colleges, LinkedIn with professionals) rather than “everyone.” You need a Schelling point for coordination, not just better features.

Interoperability Beats Antitrust

Thesis: Forcing platforms to let users export their social graphs would create more competition than any breakup because it removes the actual lock-in.

Pull Quotes:

“If you use legacy social media, there’s no easy way to leave social media and go somewhere else... But, you know, there’s nothing intrinsic to technology that says that it has to be that way.”

“We could say to Elon Musk and Mark Zuckerberg, people who leave your platform have to be able to speak to the people who stay on your platform and you are required to support this.”

Analysis:

Traditional antitrust is fighting the last war. You could break Facebook into five pieces and users would just swarm to whichever piece had their friends—congratulations, you’ve created a temporary inconvenience. The real lock-in isn’t corporate structure; it’s protocol incompatibility. Mastodon already solved this: when you leave one server, you export a simple XML file with your social graph and import it elsewhere. Takes literally minutes. Force platforms to support ActivityPub or similar protocols and suddenly every product decision becomes life-or-death because users can actually leave.

However, the EU already tried a version of this with GDPR Article 20, which has been in effect since 2018. The “right to data portability” requires platforms to provide user data in a “structured, commonly used and machine-readable format” and allow direct transmission to another platform “where technically feasible.” Yet despite this regulation, we haven’t seen the competitive effects Doctorow predicts.

Research on GDPR’s data portability provisions reveals three major obstacles:

* Lack of user awareness and motivation - Most consumers don’t know about or care to exercise these rights

* Limited data scope - The regulation only covers data users directly provided, excluding the valuable inferred and predicted data that platforms generate

* Lack of standardization - Without common formats, exported data isn’t truly portable

More fundamentally, even when switching is made technically easy, consumer apathy remains a massive barrier. Most users complain about platforms but don’t switch, as evidenced by everyone still on Twitter/X despite widespread dissatisfaction. Studies consistently show that “status quo inertia” (the general reluctance to break current associations due to habit and comfort) is one of the strongest predictors of staying on platforms, often outweighing even significant dissatisfaction.

Enforcement would also be far more complex than Doctorow suggests. It’s not as simple as checking whether users can export followers. Real compliance requires:

* Defining acceptable data formats and standards

* Managing spam and harassment across federated systems

* Handling different content moderation policies between platforms

* Ensuring real-time syncing at massive scale

* Authenticating users securely across platforms

This doesn’t mean interoperability is a bad idea—it’s likely better than traditional antitrust. But it’s not the silver bullet Doctorow presents. For platform operators, the calculation does change if mandatory interoperability becomes real, but only if enforcement is robust and consumers actually care enough to switch.

Do Tariffs Prime the Jailbreak Economy?

Thesis: Trump’s tariffs accidentally created the perfect conditions for countries to legalize reverse engineering and become global suppliers of digital freedom tools.

Pull Quotes:

“When someone threatens to burn your house down unless you do what they tell you and then they burn your house down anyway, you’re a sucker if you keep doing what they tell you.”

“We can take the inkjet printer ink that is $10,000 a gallon and knock it down to $100 a gallon and pocket a great deal of that consumer surplus and moreover export those jailbreaking tools.”

“Canada doesn’t need to confine itself to exporting reasonably priced pharmaceuticals to Americans. We can export the tools of technological self-determination because anyone with a payment method and an internet connection will be able to get these tools.”

Analysis:

This is galaxy-brain speculation presented as likely outcomes. The game theory is far more complex than Doctorow suggests. Yes, Trump imposed tariffs on allies. But countries maintain IP protections for many reasons beyond avoiding U.S. tariffs:

* Protecting their own tech companies’ IP - Canada, Europe, and other allies have thriving tech sectors that benefit from IP enforcement

* Diplomatic relationships - IP protection is embedded in multilateral treaties and trade agreements (WTO, WIPO) that provide benefits beyond U.S. relations

* Fear of retaliation in other domains - Trade policy isn’t conducted in a vacuum; violating IP norms could trigger responses in areas like finance, defense cooperation, or market access

The printer ink example, while attention-grabbing, is somewhat misleading. Consumer Reports found t