For context, here’s the BBC’s take (emphasis mine):

Tinder will let users prove they are human and not robots by bringing advanced eye-scanning technology to the app amid rising fears over AI.

Users of the dating app, as well as other major platforms such as video calling service Zoom, will be able to scan their irises to earn a “proof of humanity” badge attached to their profile or name.

Through either an online app or an orb-shaped scanning device run by the World network people can submit to a scan of their iris, the coloured portion of the eye, in order to confirm they are human.

World, formerly known as Worldcoin, is part of Tools for Humanity, a start-up co-founded and chaired by Sam Altman, who is also the head of ChatGPT-maker OpenAI.

Once a person is confirmed as human by the technology they receive a unique identification code, which is stored on their smartphone and considered their World ID.

As Sam Illingsworth over at observed, Sam Altman has manufactured an AI-shaped problem he helped create, and he is now trying to peddle his World eyeball-scanning-orb-cum-crypto-token as a solution.

This is precisely the concern I explored in The Helpful Ladder section in Part 2:

In fact, in early drafts of this part of the article, I had included World as a step on the ladder of how OpenAI would connect its various tools to obtain a complete picture of you. The thing is, I was never able to explicitly connect World to OpenAI’s more overt data grabs. Aside from the fact that Alex Blania is a co-founder of BCI company Merge Labs (which received seed funding from OpenAI in January) and co-founder (with Altman) of the Worldcoin Foundation (the data controller) and Tools For Humanity (owners of the actual Orbs), I hadn’t found any actual evidence.

Ever worried about sounding like an absolute crank, I kept it out, and I really regret that I doubted myself.

I like being right, but not like this

As Husbot knows, I love being right about things, but I hate being right about this, because it’s an awful thing to be right about. This is why I call myself Privacy Cassandra — I can see that we’re headed in a dark, rights- and agency-eroding direction, but nobody ever believes me.

We shouldn’t need to have our irises scanned by a company that has been under investigation or shut down by regulators in at least six countries. We shouldn’t need to prove we’re human1 to go on dates, buy concert tickets, purchase goods, sign a document, attend a Zoom call, or vote. We certainly shouldn’t need to trade our biometric data to Sam Altman for a bullshit token that’s worth fractions of a cent and will only, at best, ever meaningfully enrich the billionaires who own 23% of the token stake.2

I’m angry this is a thing at all.

I’m frustrated that members of the press aren’t reporting on this critically enough, beyond alluding to “privacy concerns”. Did anyone ask whether World has addressed any of the “privacy concerns” highlighted by regulators over the last two years, including the Bavarian Data Protection Authority (BayLDA), the Hong Kong Office of the Privacy Commissioner, or most recently the Philippines National Privacy Commission? Did anyone bother to mention that World has been suspended, forced to shut down entirely, or required to delete data collected in Brazil, Germany, Kenya, Hong Kong, the Philippines, and Thailand?

Since it’s my blog, I’ll lay out the questions that have, to my knowledge, still been left unanswered:

• Does World still believe that iris codes or the encrypted biometric shards currently used to store this data, are not personal data under the data protection laws, or special categories biometric data under Article 9 GDPR?3

• Does World still believe it can bypass informed user consent requirements by relying on a legitimate interests lawful3 basis in the EU?4

• Can data subjects meaningfully exercise their rights to rectification, erasure, withdrawal of consent, or the right to opt out of processing?5

• Similarly, can data subjects even offer informed, specific, unambiguous and freely given consent in the first place?6

• Is World still storing encrypted biometric shards, historic iris codes, or other personal data? If so, can they ensure that their security controls are robust, and that they meet requirements under the data protection laws?7

I have more questions that will probably remain unanswered. I’m angry about this. I’m also angry that Sam Altman is at least partially responsible for us needing to provide “proof of humanness” in the first place.

Either way, I look forward to the next round of regulatory review which will almost certainly follow.

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Bluntly, Trump needs Anthropic (and by extension, the hyperscalers, VC firms, and sovereign wealth funds [who support Anthropic]) far more than they need him. Even if the courts do side with the administration, Trump still has to contend with all the essential companies who have funded his largesse, and the fact that so many of these donors have a vested interest in Anthropic succeeding. That’s extensity.

No matter how powerful Trump thinks he is, no dictator can afford to piss off the coalition that actually keeps him in power. So, in the end, I don’t think Trump’s theatrics will hurt Anthropic much, because there are far too many big, important essential players who need Anthropic, and who have the leverage and ability to get Trump and Hegseth to blink first.

I mention all this, because on Friday, Dario Amodei met with senior White House officials, in an effort to reach a detente. I thought that was interesting, but what I was missing was the specific leverage point: Claude Mythos.

explained things nicely

Suddenly, a few things clicked.

1. Based on their Project Glasswing announcement on April 7, Anthropic has almost certainly been working on its Mythos frontier AI model for months now, and have been testing it before the spat with the administration reached its high-drama phase.

2. Anthropic believes their model is powerful enough that it needs further evaluation prior to release.1 Rather than keep that research in-house, they decided to selectively release the model to a handful of security companies (all of whom took stock hits on the April 7 announcement), the Linux Foundation, and tech firms.

3. Those tech firms, which include AWS, Google, Broadcom, Microsoft, and Nvidia—all have a vested interest in Anthropic’s success. JPMorganChase’s inclusion also isn’t coincidental. Goldman Sachs and Citigroup are also purportedly testing Mythos.

4. While these companies might stay quiet publicly (to avoid a predictable administration meltdown), it’s near certain that every last one of these companies was fiercely advocating on behalf of Anthropic internally. It’s no coincidence that JD Vance and Scott Bessent met with Anthropic, Google, xAI, and Microsoft a day or so prior to the Mythos drop. A separate meeting between Scott Bessent, Jay Powell and the Wall Street banks occurred a few days later.

5. I believe Amodei pushed back on the administration’s demands precisely because he knew Mythos presented a risk, and that the administration’s theatrics could only go so far. As I wrote, “Amodei can afford to take a principled stance against the administration not necessarily because the company is braver or more patriotic than other AI companies, but because Amodei knows that Anthropic has extensive reach and leverage.”

6. It would be politically suicidal for the administration not to back down at this point. Tanking Anthropic because Amodei didn’t kiss the ring sufficiently risks alienating Trump’s coalition (which he can’t afford to lose). Icing out Anthropic from government contracts also creates a genuine national security risk. If CISA or the DOD, or NSA can’t use Mythos to harden their systems, those systems are exposed to threat actors and nation states who are happy to use Anthropic models.

7. There’s literally no upside for the administration to play a game they can’t win, and I think Anthropic knows this. I suspect that even if Trump doesn’t get it, JD Vance, Scott Bessent, Susie Wiles do.

My current forecast on the supply chain risk question

On March 22, I used FeedForward (my internal research & forecasting engine) to run a forecast on whether Anthropic will still be designated a supply chain risk by the US Government on May 31, 2026.2 Here’s the current assessment, with increasing/decreasing likelihood measures.

Current Assessment

Confidence: 5% (updated 2026-04-17)

Key Factors

Increasing likelihood:

• Mythos creating normalization pressure - Treasury Secretary Bessent’s characterization of Mythos as strategically important, combined with White House plans for agency access and Treasury CIO seeking Mythos access, suggests the government may need to modify or lift the designation to utilize the technology for national security purposes

• Pentagon’s practical needs - Wall Street CEOs summoned by Bessent and Powell to discuss Mythos cybersecurity applications, with major banks (JPMorgan, Goldman Sachs, Citigroup) reportedly testing it, indicates government is prioritizing defensive cybersecurity capabilities over the ban

• Expedited litigation timeline - May 19 oral arguments in the DC Circuit supply-chain risk case, create possibility of ruling before May 31, though extremely tight

• Political optics of inconsistency - Government simultaneously praising Anthropic’s strategic value while maintaining supply chain risk label creates unsustainable tension

Decreasing likelihood:

• Appeals Court definitively ruled against emergency relief - DC Circuit panel refused to block the supply-chain risk designation, with explicit reasoning citing “judicial management of how, and through whom, the Department of War secures vital AI technology during an active military conflict”

• Court split confirms prolonged litigation - San Francisco judge found DOD likely acted in bad faith and ordered removal, but DC Circuit’s opposite ruling means resolution requires higher court intervention, extending timeline

• Trump-appointed judges’ reasoning - The DC panel (including Gregory Katsas, former Trump deputy counsel, and Neomi Rao from OMB) emphasized national security deference and military conflict context, suggesting even favorable procedural arguments won’t overcome security claims

• No evidence of settlement negotiations - Despite the normalization signals, no articles indicate active DOD-Anthropic settlement discussions.

In short, what we’re witnessing is extensity in action. It’s not just a question of what AI model some agency uses for its internal chatbot. It’s about what happens when a single company becomes so integral that it can push back against the traditional levers and forces (regulatory, political, economic, or otherwise) that govern behavior.

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It turns out, we weren’t alone. Thousands of eligible elementary and middle schools in California also had Apple IIe’s, for two simple reasons:

1. Steve Jobs was a marketing genius; and

2. Tax breaks.

Jobs saw an opportunity to build loyalty to Apple products at an early age, and so he personally lobbied for federal and state legislation to get Apple computers into classrooms. While Jobs was unsuccessful at the federal level, he did win over California.2

In September 1982, California Governor Jerry Brown signed a similar version of the Computer Equipment Contribution Act, AB 3194, which allowed a 25% tax credit against the state corporate income tax for computer equipment donated to schools. ... [U]nder its Kids Can’t Wait program, Apple donated a computer to each of the roughly 9000 eligible elementary and secondary schools in California.

Source: Audrey Watters, How Steve Jobs Brought the Apple II to the Classroom.

The tax break was generous: Apple’s pre-tax donations exceeded $21 million, but their after-tax outlay was a little over $1 million. Apple spent pennies to capture the hearts and minds of millions of kids in California.

The subsidized surveillance state

Amazon, and its Ring subsidiary, as well as facial recognition and automatic license plate recognition companies like Flock Safety, are replicating the Jobsian model. Not in the classroom, but in municipalities and neighborhoods across the US.

Some of this isn’t new. For years, surveillance tech firms, wealthy private donors, and the federal government, have been gifting budget-strapped municipalities and local law enforcement (LLE) with “free” or heavily subsidized access to surveillance equipment and software. Sometimes, this comes in the form of no-strings-attached donations to the police, but often they’re part of short-term trials & pilots which are steeply discounted, and end up extending indefinitely. And since they’re free, come in below municipal procurement thresholds, or are initially time-bound pilots, approvals get fast-tracked, often with limited oversight and public transparency, and few privacy reviews.

Just like Apple, Ring and Flock recognize that it’s also very hard to remove something once people have grown accustomed to it. And getting a municipality or the local police force to give up their shiny toys requires a concerted effort by communities to actually shut the systems down for good.

For example, in May 2025, Denver’s city council unanimously rejected a $666,000 contract extension for Flock Safety ALPR cameras after weeks of public outcry over mass surveillance data sharing with federal immigration enforcement. But Mayor Mike Johnston’s office allowed the cameras to keep running through a “task force” review, effectively extending the program even after the contract was voted down.

In response, the Denver Taskforce to Reimagine Policing and Public Safety and Transforming Our Communities Alliance launched a grassroots campaign demanding the city “turn Flock cameras off now.” They appear to have been “successful” in the sense that almost a year later, Denver is finally ditching Flock--by replacing them with a competitor’s ALPR products.

Going direct to consumer

In response to this pushback, sensorveillance firms seem to be moving away from the traditional LLE pipeline, and are instead adopting a direct-to-consumer ‘participatory mass-surveillance’3 approach. From The Guardian in April 2026:

A Silicon Valley city will offer its residents free wireless doorbells equipped with cameras to help police collect video evidence.

The city council of Milpitas, a suburb north of San Jose, California, recently approved $60,000 to provide these devices on a one-camera-per-household, first-come, first-served basis, as was first reported by Milpitas Beat and confirmed by the Guardian. ...

Milpitas police plan to share a link for residents to voluntarily upload doorbell footage and organize community events, where residents can sign up to participate in the program, said Tyler Jamison, the Milpitas assistant chief of police.

Ring has been adopted as part of a free program in cities across the country, including local initiatives in New York City, Mount Vernon, Syracuse, Philadelphia, Jackson and Cleveland.

Selling surveillance as convenience

Often these initiatives are brought by concerned citizens—usually the parents of crime victims, domestic violence organizations, or elderly advocates.4 In addition to emotional appeals, Ring also highlights all the “helpful” new prosumer capabilities that leverage its AI-enabled “Familiar Faces” technology and Ring’s app ecosystem. From TechCrunch:

With now more than 100 million cameras in the field, Amazon-owned Ring [will launch] a new app store that will expand its cameras’ capabilities. Focused initially on areas like elder care, workforce analytics, rental management, and more, the store will allow developers of all sizes to tap into Ring’s ecosystem to reach customers. ... “With AI, there’s just an incredible amount of long tail use cases,” [Ring CEO and founder Jamie Siminoff] told TechCrunch. “We are unlocking value that our customers have invested in, in things that … all of us together never thought we could do.”

This isn’t about safety, and this isn’t some goodwill effort by Ring. This feels more like an Apple IIe moment in schools. We’re watching a few key players in the sensorveillance space5 try to become extensive.

I first sketched out the idea of tech extensity in February:

Tech extensity occurs when a product, service, or entity becomes so deeply entrenched in a system that imposing limits or excising them entirely (e.g., through regulation or political action), becomes nearly impossible. A firm doesn’t need to be the best to become extensive, or even a technical monopoly. It only needs to find a way to integrate broadly within one or more systems, becoming a critical piece of the puzzle. Ring (and by proximity, Amazon) and Flock are attempting to become an extensive part of the surveillance apparatus, just as Apple (and later, Google) had in education.

One way they might actually succeed is by marketing ever-more-convenient uses of their tools directly to the masses. Another is by giving their product away at a heavy discount or for free.

Behold the torment nexus, pretending to find Fido

Fortunately, we’re not yet at the stage where there’s a Ring doorbell on every porch, or a Flock-connected camera on every street corner. So far, these giveaway programs remain isolated to a handful of cities in America. There’s also been greater pushback over the last year by municipalities and citizens against sensorveillance by Ring and Flock, especially after Amazon’s disastrous “Search Party” Superbowl ad in February, and the Ring/Flock partnership was exposed. Flock’s (aka, “Netflix for Stalkers“)6 tendency to track everything from kids playing in playgrounds to Americans’ shopping habits in malls is hard to reconcile against promises that these systems is just being used to stop the bad guys.

More people are starting to realize that despite all the prosocial claims and helpful features, always-on surveillance is still surveillance. Some people were so outraged by the Superbowl ad that they disconnected or even destroyed their doorbell cameras or lobbied their local officials. Amazon even cancelled its partnership with Flock after the Superbowl blowback.

What this tells me is that Ring and Flock still face resistance to extensivity, but I’m not sure how long this will hold. Many people still see Ring devices as a touching way to create slice-of-life moments for YouTube. Most Americans, sadly, remain unaware of how an extensive network of surveillance can impact their lives, or have resigned themselves to “privacy nihilism.

And Ring certainly has no intention of limiting the (search) party to pets or porch pirates. Here’s a message Siminoff sent to employees in October 2025:

I believe that the foundation we created with Search Party, first for finding dogs, will end up becoming one of the most important pieces of tech and innovation to truly unlock the impact of our mission. You can now see a future where we are able to zero out crime in neighborhoods. So many things to do to get there but for the first time ever we have the chance to fully complete what we started.

Just to be clear: Nobody should have to live in an unsafe, or crime-ridden neighborhood, and it’s unforgivable that this fear is a reality for so many. But if you start to think about it, you realize that zeroing out crime isn’t possible. What Siminoff and Garrett Langley, Flock’s CEO, are really trying to do is to build their brands around the perception of safety.

The act of ‘feeling safe’ is an emotional response, not a rationally constructed one. You can live in a gated compound, surrounded by guards, protected in every way and still feel unsafe. This isn’t new, of course, but the expansion of sensorveillance networks, coupled with political polarization, mis- and disinformation, and active exploitation of these anxieties by law enforcement and security firms, exacerbate fears that we’re all unsafe without these products, regardless of their efficacy.

Whether Ring cameras reduce crime or not is irrelevant. They need only create the perception of safety to drive adoption. Making people feel safe is a win-win for everyone. Mnicipalities can say they’re ‘doing something about crime,’ residents feel like something is being done about crime, police get more information about all of us, and Ring and Flock get sweet taxpayer-funded contracts and data they can sell. Everyone gets something in the end.

And as Ring Doorbells and Flock Condors become ever more extensive, use of sensorveillance tools won’t be limited to burglars, gang members, and package thieves. It will expand to target anyone or anything that makes our neighbors fear us. We already see how Ring footage is used to target peaceful protestors. We’ve witnessed ICE leverage Flock ALPR data to go after immigrants. What we have less clarity on is what’s going on behind the scenes. For example, how governments and the private sector may be supplementing existing databases of social media and online profiles, with with residential camera and facial recognition data.

We also don’t know how turning the Neighborhood Watch group into a 21st Century ‘Inoffizieller Mitarbeiter‘ will impact society.

Sure, we have a good idea—these unofficial collaborator networks were shockingly effective in East Germany—but reporting was crude, and neighbors didn’t have the benefit of 24/7 surveillance. How will our behaviors change when our neighbors label harmless interactions as ‘suspicious activity’? How will that affect community behaviors? What happens when these innocent reports lead to discrimination or actual state violence?

We don’t know how extensive surveillance networks will be used in the future, or how it’s changing our behaviors today. I suspect that we won’t have the answers until these networks become extensive to the point that they’re no longer capable of being controlled or removed.

All those free doorbell cameras will end up being very costly in the end.

For the unfamiliar: I used Claude Code to vibe-code myself a full research & forecasting pipeline. I call it FeedForward, and it’s already helping me tamp down my raging pessimism make better data-driven research and forecasting decisions, using a little AI assistance. You can read more about how (and why) I built FeedForward below:

Currently, I’m tracking seven different questions (mostly about Anthropic and geopolitical events), but I want to talk about one forecast in particular.1

Resolution date: 2026-04-01

Current confidence score: 78% (as of 2026-03-25)

Current confidence that this will happen before August 2, 2026: 35%

As you can see, I’m bullish on this question, particularly as it relates to the EU AI Act. However, I’m far less optimistic that this will happen before the August 2, 2026 obligations kick in for high-risk AI systems, so if you’re an organization who’s hoping for a miracle, it’s probably a good time to start praying.

Here’s a brief backgrounder on what’s going on if you have no idea what I’m talking about.

On November 19, 2025, the EU Commission released the Digital Omnibus Regulation, a sweeping proposal that would do a bunch of things, including amending major portions of the EU’s landmark AI Act and the EU General Data Protection Regulation. The European Council (aka, the heads of each EU member state) adopted their own proposal March 13, 2026, largely agreeing with the Commission proposal while diverging on some points. The European Parliament’s IMCE committees voted February 25, 2026 in line with the Council’s position.

A plenary vote is expected March 26, 2026, followed by the trilogue negotiations between the Commission, Council, & Parliament. This represents substantial progress across all three EU institutions within a tight 4-month window, suggesting that the EU really wants to make this happen.

There are a number of proposed changes, which I’ll summarize below.2 If you want the full details, I highly recommend checking out ’s detailed analysis on the proposed changes, including divergence between the Council and Commission, as well as how they would impact the EU AI Act as it’s currently written.

What’s in the proposal?

1. Delayed compliance timelines for high-risk AI systems: Both the Commission and the Council have proposed extending the date when providers and developers of high-risk AI systems must comply. The Commission has a ratcheted approach. The deadline would tie compliance deadlines to the availability of harmonized standards, with a December 2, 2027 (for Annex III systems) and August 2, 2028 (product safety systems) hard deadline. The Council’s proposal only mentions the fixed dates, but they agree on those dates.

2. Expanded sensitive data processing for bias mitigation: The Commission would lower the “strictly necessary” requirement to merely “necessary” for processing sensitive personal data for bias detection, and would expand this lawful basis to include providers and deployers of other AI systems and AI models, not just high-risk ones. The Council agrees to extending the scope, but not lowering the standard.

3. New prohibited AI practices: The Council proposed extending Article 5 Prohibited AI Systems to include systems generating non-consensual intimate imagery and CSAM, with liability for “reasonably foreseeable” misuse if safety guardrails are inadequate.3

4. Transparency requirements postponed: Synthetic content labeling requirements would be delayed to February 2, 2027 for systems already on the market. This would not apply to new systems placed on the market after 2 August 2026.

5. Reduced registration requirements for some high-risk AI systems: The Commission’s version would eliminate registration requirements entirely if a provider can demonstrate that their AI system will not pose significant risk of harm to health, safety, or fundamental rights. The Council’s proposal would substantially reduce information requirements for exempted high-risk systems. Providers would only need to explain why their system is not considered high-risk, and what countries in the EU their AI system is marketed or sold.

6. Diminished AI literacy obligations: Shifts from mandatory provider/deployer obligation to Commission/member state encouragement.

7. Lowered compliance obligations for Small Mid-Caps (SMCs): The Council & Commission proposals have both suggested extending penalty caps and applying simplified compliance obligations currently applied to SMEs (fewer than 250 employees, and €50M turnover) to small mid-cap enterprises (fewer than 750 employees, €150M turnover). This designation would apply to 99% of all EU companies.

If you look at only the base-rate (e.g., how long it typically takes the EU to pass legislation), it’s easy to be pessimistic. However, there are a number of key factors which support the EU moving more quickly this time.

What’s influencing my forecast?

Increasing likelihood:

• Strong parliamentary momentum confirmed: The relevant Parliament committees voted 101-9 to simplify the AI Act, with enforcement possible “as early as August 2026”. This suggests rapid implementation expectations. The fact that we’re already at the trilogue stage suggests there’s strong motivation to get things done.

• Timeline remains viable: Although aspirational, enactment before April 2027 is possible. The EU has enacted laws quickly in the past (most recently, a package for macro-financial assistance to Ukraine in 2024). A COVID economic stimulus package also made it through the process in 266 days.

• US pressure intensifying: The Trump Administration has been pushing hard on the EU for months to amend its tech laws, going so far as to dangle the prospect of more favorable tariffs on steel and aluminum if the EU softens its position. Granted, most of this is in relation to the Digital Services Act and Digital Markets Act (which aren’t affected here), but the EU may see this as a way to signal cooperation with the Administration’s “recalibration” demands.

• The Draghi report: The September 2024 Draghi report continues to act as a nudging force here in the EU. The report was highly critical of the EU’s burdensome and confusing regulatory landscape, which creates an “existential challenge” to EU competitiveness. Commission President, Ursula von der Leyen has seemingly taken this to heart, and the Draghi report was cited as a major driver behind the Commission’s Digital Omnibus.

Decreasing factors:

• Institutional resistance persists: There’s substantial opposition to the Commission’s proposal, including from the EDPB/EDPS, industry groups, and NGOs. Some stakeholders see this as a rushed process that will weaken democratic norms and have profoundly negative impacts on fundamental rights and freedoms (especially around transparency and use of sensitive personal data).

• Timeline is viable, but it’s still politics in the EU: As I noted above, the timeline is achievable—more so by April 2027, than August 2026—but not definitive. The EU legislative process is on par with elephant gestational periods in terms of speed, averaging eighteen to 24 months (according to data by the law firm Clifford Chance). The EU AI Act itself took 1,178 days (3 years and 2 months) from adoption to last mandate. However, the EU has been known to pass things more quickly if sufficiently motivated. It’s probably also bound to be a bit faster here, as the proposal is an amendment to an existing law, but I’m not a legislative expert, so I’ll happily defer to others like Fabian Bohnenberger, or .

• Disagreement over core ideas: There’s major divergence between the Commission and Council proposals, particularly around eroding the “strictly necessary” standard for AI systems’ use of sensitive personal data, and low-risk Annex III AI systems need to register.

I may increase my forecast after tomorrow’s plenary session.

What still needs to happen

1. Parliament Plenary Vote (March 26, 2026) - likely to pass

2. Trilogue Negotiations (starting late March 2026 assuming #1) - duration unknown

3. Final Text Agreement between all three institutions

4. Legal-linguistic finalization

5. Final approval votes in Council and Parliament

6. Publication in Official Journal

7. Entry into force (typically 20 days after publication)

Change Log

I’m curious -- what do readers think? Am I being too optimistic? Not optimistic enough? Am I missing something in the data? Leave a comment.

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My goal was simple: I read too much, and my subscription list is obscene. What I am able to read fully is scattershot, and I know I’m missing a lot of good insight because of stupid constraints like ‘work’ and ‘needing to sleep’. Other suggestions that have been proposed by Husbot and random strangers include ‘just don’t do that?’ and ‘maybe go outside?’, but that’s no fun.

But, computers read faster than I do, can serialize, and don’t need to sleep, pet cats or have a day job. And so, Claude and I vibe-coded a fancy program (which I named ‘FeedForward’ because, puns) that would take my 150+ RSS feeds, read everything, pick out relevant articles (based on a keywords list I created), summarize and tag those articles, and dump everything into my second brain tool (Obsidian) for further processing and signals identification. It roughly worked like this:

1. Stage 1: RSS Collection & Filtering (`feedforward.py`)

• Fetches RSS feeds from OPML configuration ## feeds.opml (150 RSS feeds incl. Substack feeds)

• Performs initial HTML content extraction & filters articles by keyword ## Keywords.txt (~800 keywords, adjustable)

• Tracks processed articles (to avoid duplication)

• Stores articles in SQLite database for temporal intelligence gathering.

2. Stage 2: AI Processing & Note Creation (`article_processor.py`)

• Processes articles with Claude API & generates summaries & suggested tags

• Creates formatted Obsidian notes saved in Obsidian directly

• Updates database with AI-suggested tags.

3. Stage 3: Intelligence Dashboard (`streamlit_app.py`)

• Interactive web interface for exploring intelligence data, used for trend detection & weak signal analysis (uses Streamlit)

• Manual tagging interface and synchronization with Obsidian (kinda, this didn’t always work)

• Tag-based article search and exploration

• Chat interface to explore my articles with Claude.

4. Stage 4: Weekly Report (‘weekly_report.py’)

• Generated a futures-oriented weekly intelligence report with Claude analysis, including signals, trends & deeper dives on key themes.

This was good for awhile. The feedreader would download 50-70 mostly relevant articles a day, Claude would summarize and tag them, and they’d be uploaded directly into my Obsidian vault, where I could skim over them at my leisure. But as I scaled and tweaked my tool, I started to run into problems.

I was confused about how to run everything: From the beginning, Claude had broken my program up into modules. This was good at first, except each module had its own documentation, helper tools and shell scripts because I was constantly forgetting which flags to run when and the specific incantations1 I needed to mutter to make them run. I quickly got lost in how to run the program, even with let’s say, overzealous amounts of documentation:

Too much content: While Obsidian is a database and does database-like-things, finding things in Obsidian remains a huge ballache. In practice, Obsidian is more akin to Windows File Explorer than it is to a MySQL database, just with better linking and metadata features.

Below is a simple example of the pain. Using Omnisearch (a community module that itself is an improvement on Obsidian’s base search), you’re still limited to a single article that must be opened individually.2 There’s no way to say, pull a list of files that match ‘Hormuz’.

With 100 articles, that’s probably NBD. But when you’re approaching 5,000 or so articles of varying levels of relevance, things start to fail.3 At some point, it becomes a hoarding problem.

Too many interfaces: One of the initial goals I had for FeedForward was to avoid context switching in the research process. Prior to v1, I had to do four different mostly manual things across three programs for each article I processed.

Find the article (or pull it up in Reader) —> Summarize the article using AI (Claude or Leo AI)—> Paste the summary & article into Obsidian —> Tag the article in Obsidian.

Repeat 50-100x. Only then could I get to the actual analysis/research.

FeedForward v1 was supposed to simplify this process (and it did, at first!), but by mid-March, I’d accidentally made the problem worse by creating three separate UIs:

1. The command line interface (where the programs were actually executed)

2. Obsidian (for research/writing)

3. Streamlit for visualization & trend spotting

All three had value, and they clearly shifted the toil up the chain, but they were less effective than I wanted because they were isolated and distinct from one another. Streamlit was great for surfacing articles & signals, but it had no visibility of what I was writing about, and it was clunky to modify/enrich articles. Obsidian was good for writing and enrichment, but it sucked at visualization and surfacing trends/signals. The only thing that they shared was the underlying database.

Time for an intervention

Clearly, I’d over-engineered FeedForwardv1. In my effort to simplify, I’d radically complicated things, but more importantly, I didn’t have a good idea about what parts I’d complicated or how they were broken.

Some of this isn’t unique to me (or vibe coding). Over-engineering is frequently inevitable, because refactoring/fixing isn’t nearly as easy or sexy as ideation and creation, and the software development process can be overwhelming, particularly if you’re not a professional programmer and you’re just vibe-coding your way through life.4 It’s easy to forget why you made the choices you made in the design phase, which is further exacerbated if you have Claude writing the actual code for you.5

I also realized that I was trying to solve many hard problems on my own, and was doing it all very badly. Or as my Irish brethren would say: I was cocking it up.

But the guts of feedforward (particularly the filtering, summarization, tagging, database handling and deduplication) was really good! What I needed to do was move this beating heart into a new, easier to manage body. Here are a few of the things I changed:

I gave up on roll-your-own RSS feed parsing: RSS feed parsing and ingestion sucks. Many sites continue to do a shit job of following the RSS spec, and by early March, Claude had written hundreds of lines of code just to process malformed XML or things pretending to be RSS but that were really something else. Poor Claude even had to write a separate program to diagnose troublesome feeds that threw errors, were blocked by paywalls, or didn’t render properly.

The thing is, I pay money for a fantastic service—Readwise—that does all of this very well and they happen to provide an API because they are awesome, responsive developers. If you have an info-junkie habit like mine, I highly encourage you to sign up (affiliate link), throw them a few quid a month and make your life much, much easier. Also, Readwise / Reader handles many different content types, so if I happen to find say, a PDF, non-RSS website, Twitter account, YouTube channel, or email newsletter, I can easily add it to Reader and that becomes part of my feed. Plus, Readwise directly integrates with Obsidian. I wrote about them here:

Now, you might be asking: Why not just use Reader? Well, I did for a long time, but it’s just not scalable to my needs.

Reader has nailed the RSS parsing problem beautifully. Unfortunately, it doesn’t do the fancy tagging, summarization, and signals/trend analysis work that I need. Or rather, it can, but it’s limited, and very, very manual.

Ghostreader (Reader’s AI summarization engine) must be manually invoked for each article, its summaries are basically tweet-length, and customized summarization prompts are still borked on the PC. Tagging is also painfully manual. If I want to highlight topics in Reader, that means, on average, 5-7 tags per article x 50-70 relevant articles a day, or 250-490 manual tags per day + 50-70 manual ‘summarize this’ clicks. That just isn’t realistic.

I replaced clunky keywords with semantic search: I’m really annoyed that it took me months to figure this out, but keyword (or lexical) searching, which is basically fancy CTRL-F, is primitive, especially when one cares about finding relevant, useful information, not just raw word hits. The far smarter approach is to store and later retrieve content semantically. Semantic search elevates the game: it considers not just the words themselves, but the relationships between words, the searcher’s intent, and the context of the search. Semantic search looks for meaning, not just keywords.

Google uses semantic search. And so does every LLM you’ve ever used. Specifically, they use semantic search based on running text strings via embedding models, which magically convert text into a long string of numbers. Three Blue, One Brown has an excellent 7-minute explainer on how LLMs work, including the embedding process:

What’s really cool is that all of this can be done locally on any reasonably modern machine. I use a model called all-mpnet-base-v2 and it takes around 3 minutes or so to process or encode around 200 articles. The downside to this speedier process is that I sacrifice completeness —mpnet-base-v2 is only processing the first 2000 characters (250-500 words) of the article, which is enough to get the title and summary, but not much else.

The full process in the end looks something like this:

1. reader_sync.py — Articles come in from Readwise. Full content (including metadata) goes to SQLite, first ~2000 chars get embedded and stored as vectors in ChromaDB.

2. intelligence.py topics — Topic profiles in a customizable topics.yaml file get embedded as vectors, then compared against every article’s embedding via cosine similarity.6 This is how articles are initially identified as ‘relevant’. For example, I follow lots of things that are privacy-adjacent (e.g., surveillance-tech, wearables, neuraltech, quantum-computing, satellites, robotics), so articles that match one or more of those topics get matched and tagged with their matching topic category or categories. Topics also filter out AI slop and noise by adding an AI-slop tag.

3. intelligence.py summarize — Sends all the topically-relevant articles to Claude for full summarization. Claude reads the full content and assigns the 1-5 relevance score, writes the bullet-point summary, and suggests additional tags.

That output then gets added to the SQLite database. And then the real magic happens.

The secret sauce—A direct MCP connection between the database and Obsidian: After years of banging my head against the wall, I realized that Obsidian will never be the database that I want it to be. The solution to this problem is simple: query the SQLite database using Claude (and local search) instead, rather than storing articles in Obsidian. Everything lives in SQLite, and I can query details using a custom-built MCP server + Claude chat interface.

There are trade-offs to this, most notably, my shiny-object-squirrel brain no longer receives the dopamine hit of spontaneous discovery whilst stumbling on some errant article in my feed folder, but this is countered by the fact that I am also no longer spending hours trying to match signals and trends to articles. I can just ask the chat ‘Tell me all the Hormuz Nuz I can Use’ or ‘Give me a summary of which companies are releasing new privacy-invading wearables this week’ and it will return relevant results, and summarize them for me. I can directly add this to articles I’m working on.

I can also use the chat interface to query the database without any Claude involvement by using a few slash commands. For example, I can do a /search for a keyword like ‘wearables’ give it a date range (e.g., within the last 5 days) and out pops a list of articles matching that keyword with clickable links that I can open directly in the Obsidian web browser. Or, I can list all /recent articles that have popped up — they appear in a scrollable ‘Matching documents’ list. I can easily add a citation to the source material to whatever article I’m working on. Here’s a view of the chat window:

Offloading the toil, not the joy, to AI

The whole reason I set out on this adventure, in addition to figuring out whether what I wanted was even achievable, was because the research process, while deeply important, frequently involves a lot of toil. And that toil only compounds as traditional, quality news sources are being gutted by avaricious billionaires and overwhelmed by AI slop and low-effort noise.

But in order to provide value in my writing, I need relevant source material and context. This is why I pay for subscriptions to five different quality journalistic sources. Still, I don’t want Claude to write my blog articles for me—that’s the joyful part. I want Claude (and tools like Readwise and Obsidian) to help surface ideas & signals and identify interesting rabbit holes to go down.

The joy in my process isn’t in Google searching or reading everything that comes through the firehose — it’s pulling out the key insights or making the non-obvious connections between signals that others have missed. My recent prediction on why I think Anthropic will prevail over the DOD and the Ladder to Nowhere series were created because I let Claude handle the toil, while I focused on the insights.

This part of the process is what I want to keep as uniquely human and real as possible.

Truthfully? I don’t know. It’s still early days, and there will be more features to add. v2 could bloat as I build on the tools and become similarly unwieldy as v1. The real test will be whether this actually improves my writing output. I finally have the infrastructure, but I haven’t directly proven the thesis with v2 yet, beyond a few Substack Notes. It’s possible that all of this won’t pan out in the end, or I’ll get annoyed in new and different ways, but I hope that it’s not. Tooling that will help me offload cognitive load & toil and give more space for synthesis and insight is the goal I’m actually trying to achieve.

If you’d like to test FeedForwardv2, let me know, and I can share access. You’ll need a Claude API key, a decent amount of hard drive space for all-mpnet-base-v2 (~420 MB), chromaDB (~140MB), and a growing SQLite database, and for true functionality, Obsidian. You can use most of FFv2 via the command line without Obsidian, but it’s definitely less useful. I highly recommend having access to Claude Code as well.

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Despite all of the administration’s posturing and threats against Anthropic over the last week or so, I predict that the company will be fine. Actually, they’ll be better than fine—Anthropic will come out ahead.

And if I’m right, I think Anthropic’s survival will prove my larger argument: that a small group of deeply powerful tech companies are not only becoming too big to fail, but too big to govern.

I like Anthropic as a company, and I use its products, so this isn’t a bash on them specifically. But their extensity concerns me—and it should be raising more eyebrows.

I laid out the extensity argument in the post above, but if you’re lazy (or an LLM), here’s how I define extensity:

Extensity describes something broad in size or scope, that becomes deeply entrenched in a system. Unlike intensity, extensity is about spread, not mastery. You become extensive not necessarily by being the best, but by spreading out and becoming indispensable to the system itself.

As I see it, extensity is similar to, but not exactly the same as, a monopoly. Technically, a company doesn’t need to reach “monopoly” status in order to be indispensable within a system. And when I say ‘a system’, I’m not restricting myself to software, networks, and infrastructure, but everything we depend on as a society.

The clearest example of extensity in recent memory are the banks that were declared to be ‘too big to fail’ during the 2008 financial crisis. We all discovered, rather unexpectedly, that some banks and financial institutions, including JP Morgan Chase, Bank of America, Wells Fargo, and AIG, defied meaningful regulatory constraints and market dynamics. Even though the banks made a series of increasingly bad choices, the US government bailed them out anyway. The Obama administration reasoned that the banks had become so deeply rooted within the worldwide financial system that if they went under, their collapse would have caused major, arguably irreparable damage to the global economy. Extensity is another way of saying ‘too big to fail’.

But extensity isn’t only about defying the norms of capitalism. It’s also about oversight and regulation. Or, rather, the lack thereof. As I wrote:

[W]e’ve never faced capitalism in a world where a handful of companies have managed to amass the level of power and wealth that exist today, with the ability to engineer systems that are so intertwined and spread across so much of our lives. The technology on the market today is becoming too big to control ...

[U]nlike the AT&Ts and Standard Oils of the past, the handful of companies really running the show today control the informational substrate—the algorithms and engines that shape what we see, who we talk to, how we understand reality. SpaceX, Amazon, Microsoft, Nvidia, Oracle, and Google control the infrastructure that props up the internet. OpenAI, Anthropic, Google, and Meta control the AI. Most of these companies + Oracle/TikTok control the media. Together, they’re integrated into our identities in ways that make them fundamentally harder to disentangle from.

This is why despite all the drama and the administration’s threats-via-tweet, Anthropic likely is here to stay. But it also raises real questions for what a ‘too-big-to-govern’ Anthropic means for the world.

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The battle lines are drawn

Perhaps you’ve read about the Anthropic/Department of Defense drama, but if not, here’s a brief rundown.

On February 26, after weeks of negotiation, talks broke down between the Pentagon and Anthropic after Anthropic’s CEO, Dario Amodei refused to accede to DOD demands for unfettered “lawful”1 uses of its AI tools by the military. Anthropic was generally fine with Claude being used by the military generally (as it had been since July 2025), and by other strategic military contractors, such as Palantir, Amazon, Oracle, and Lockheed Martin, for everything from supply chain logistics and cyber operations, to foreign surveillance & intelligence gathering.

However, Amodei drew the line at bulk domestic collection and analysis of Americans’ data and fully autonomous murderbots. So, essentially, he was against Skynet and the Terminator, at least domestically. This left the administration rather salty. A company actually having lines they wouldn’t cross?! How dare they say no to King Trump!

And so, Donald Trump responded in his typical fashion, which is to say, like a toddler with a case of explosive diarrhea right before nap time. Specifically, he “truthed” an order demanding that all federal agencies immediately cease using Claude within six months, and not-so-subtly threatening to criminally prosecute the company.

THE UNITED STATES OF AMERICA WILL NEVER ALLOW A RADICAL LEFT, WOKE COMPANY TO DICTATE HOW OUR GREAT MILITARY FIGHTS AND WINS WARS!

Therefore, I am directing EVERY Federal Agency in the United States Government to IMMEDIATELY CEASE all use of Anthropic’s technology. We don’t need it, we don’t want it, and will not do business with them again! There will be a Six Month phase out period for Agencies like the Department of War who are using Anthropic’s products, at various levels. Anthropic better get their act together, and be helpful during this phase out period, or I will use the Full Power of the Presidency to make them comply, with major civil and criminal consequences to follow.2

(Emphasis mine)

This was followed by Defense Secretary Pete Kegstand Hegseth designating Anthropic as a “supply chain risk“ via tweet—a first for an American company. This designation is normally reserved for state-owned entities of actual US adversaries (like Huawei and Kaspersky Labs), but maybe Hegseth is confused and thinks the ‘State of Woke’ is an actual sovereign? I don’t know.

If the administration follows through, this would mean that not only is Anthropic banned from all Pentagon contracts and agency uses, but also that any government contractor is barred from doing any commercial business with Anthropic for any reason, even outside of the government. To quote the DUI Hire:

[N]o contractor, supplier, or partner that does business with the United States military may conduct any commercial activity with Anthropic.

Now, I suspect that Hegseth lacks the authority to dictate commercial terms in this way, but I’ll let the smart folks over dig into those details. I also don’t know how Hegseth’s tweet is going to sit with the likes of Palantir, Microsoft, Amazon, Google, and Oracle. Some of whom (Amazon & Google) also happen to be major backers of Anthropic, and major patrons of Trump’s various grifts against America.3 Though we do know Microsoft, Amazon, and Google’s positions: They’re all in for Claude, at least for non-defense work. Meanwhile, Lockheed Martin announced that they would be switching to other models so as not to upset Trump.

Bloomberg reported that Anthropic was notified directly by the DOD of their designation as a supply chain risk on March 6. Beyond that, none of the traditional, legally-binding mechanisms used by normal, functioning governments to impose high-impact policy decisions have been applied. No presidential proclamations have been made, nor have any executive orders or agency actions been publicly released.

American policy is now primarily conducted via truths and tweets, apparently.

The purpose of Trump’s threats

The goal of this spectacle, as with all Trumpian tantrums, is obviously to bully Anthropic into submission. Some agencies (including the State Department and Health and Human Services) have begun disentangling from Anthropic. But others are staying silent and biding their time, or using indirect channels to challenge what is rightly seen as a direct encroachment on capitalism and free enterprise. Personally, I think Trump, and particularly, Hegseth’s threats are little more than theatrics, because Anthropic isn’t some spineless US law firm or member of Congress. Anthropic is extensive within the government itself and has become invaluable within many of the largest commercial enterprises.

For example, here’s a quick summary of how Claude is (or was) deployed in the DOD, based on Anthropic’s own Feb. 27 statement:

Anthropic has therefore worked proactively to deploy our models to the Department of War and the intelligence community. We were the first frontier AI company to deploy our models in the US government’s classified networks, the first to deploy them at the National Laboratories, and the first to provide custom models for national security customers. Claude is extensively deployed across the Department of War and other national security agencies for mission-critical applications, such as intelligence analysis, modeling and simulation, operational planning, cyber operations, and more.

Reuters discovered that Anthropic was also used by the intelligence community in the capture of Nicolas Maduro in January, while the Wall Street Journal broke that Claude was still being used by the Pentagon against Iran days after the ban. So, the top Pentagon brass aren’t reading X and Truth Social, I guess?

And then there’s the civilian government to consider.

Anthropic was one of several AI companies to offer its AI tools at the bargain rate of $1 annually per agency, via the GSA’s 2025 OneGov initiative. Now, I’m no fool: anytime someone is giving it away, they’re bound to get takers. And boy, did Anthropic get some takers. For example, if you look over agency AI Use Case Inventories, you’ll see that Claude received a lot of agency interest. Here are just a few cases, obtained directly from 2025 inventories:

Department of Homeland Security

• Customs and Border Protection—Lists Anthropic alongside Meta, OpenAI, and Google for use in document summarization and content generation as a deployed service. I believe this goes back to 2024.

• US Citizenship and Immigration Services—USCIS uses Claude 3.7 Sonnet for PDF Intake, by scanning data from immigration forms for the purposes of handling government benefits.

Health and Human Services / Health Resources and Services Administration (HHS/HRSA—The Provider Relief Fund (PRF) Program Chatbot runs Claude 3 Haiku via AWS Bedrock. It answers user queries related to Freedom of Information Act, government accountability, and litigation matters across 2,000+ program documents.

U.S. State Department—State uses Palantir and Claude as part of StateChat, an internal chatbot used by staff for summarization, drafting, and translation.

Other agencies, including the Department of Veterans Affairs, NASA, Office of Personnel Management, the Energy and Commerce Departments, also use (or used) Claude in various programs.

While some agencies like HHS and State have publicly promised that they have, or will shut down enterprise-wide access to Claude and others like OPM have cancelled pilots, many agencies have remained quiet, and few have committed to anything beyond vague mumbles about supporting the administration.

Why Anthropic will probably win in the end

The real test will be action, and specifically how fast “immediately” is in practice. Nothing in the government, even this government moves fast or efficiently. Things take time, and there are lots of points of friction that can slow things down. Disconnecting and shifting tech stacks can take months or years in the best of times, so the idea that every government agency will be Claude-free in six months seems improbable. Especially if formal regulatory guidance never materializes.

Also, there’s a reason the DOD and members of the intelligence community use Claude and not Grok for analysis and warfighting capabilities, and it’s not just the GSA deal. Claude is a superior product for many use-cases, which is why Claude was the only LLM designated to handle classified materials until the 28th of February.

Also, you can’t really blame people for moving slowly when the POTUS constantly chickens out when confronted by bigger, more powerful forces.

While Trump might be able to convince a court that banning Anthropic from operating within the government falls within the executive’s prerogative, I don’t think there’s a court in America that will extend this argument to Anthropic’s commercial clients. Anthropic, quite reasonably, has already said they’ll sue if he tries.

And the private sector is where Anthropic makes most of its money:

• enterprise sales make up 70-75% of Anthropic’s annualized revenue

• eight of the Fortune 10 companies are Claude customers

• 70% of Fortune 100 companies use Claude, with the company holding a 29% enterprise market share in 2025, according to Incremys

• Large-scale deployments and integrations include Cognizant, Accenture, Deloitte, Microsoft, SalesForce, Google, Palantir, Amazon, and Oracle.

These companies have built their own offerings around tools like Claude Code, the Anthropic API, and its Model Context Protocol. There’s absolutely no way that they’re going to rip all of that out because Trump screamed in all-caps on Truth Social. The fact that Microsoft, Amazon, and Google went public about how they’re still on the Claude train reinforces this point.

Also, in February 2026, Anthropic received $30 billion in Series G funding from two dozen investors, including Blackstone, Qatar Investment Authority, Sequoia Capital, and Founders Fund, as well as Blackrock, Microsoft, and NVIDIA. Coincidentally, each of these listed parties are BFFs with the administration, gave big money to his various PACs, gifted him a goddamn plane, or made large “donations” to his stupid ballroom. Some, like Blackstone’s Steve Schwarzman and Peter Thiel of the Founder’s Fund, did everything but giving him a plane.

Bluntly, Trump needs Anthropic (and by extension, the hyperscalers, VC firms, and sovereign wealth funds) far more than they need him. Even if the courts do side with the administration, Trump still has to contend with all the essential companies who have funded his largesse, and the fact that so many of these donors have a vested interest in Anthropic succeeding. That’s extensity.

No matter how powerful Trump thinks he is, no dictator can afford to piss off the coalition that actually keeps him in power. So, in the end, I don’t think Trump’s theatrics will hurt Anthropic much, because there are far too many big, important essential players who need Anthropic, and who have the leverage and ability to get Trump and Hegseth to blink first.

Amodei can afford to take a principled stance against the administration not necessarily because the company is braver or more patriotic than other AI companies, but because Amodei knows that Anthropic has extensive reach and leverage. In summing up the administration’s position, Kelsey Piper opined, “Anthropic is somehow both too dangerous to allow and essential to national security.”

This suggests these AI companies (Claude today, but maybe Gemini, ChatGPT, Grok, or some other provider tomorrow) have indeed become too integral to the systems that run our lives to be easily replaced or governed. And from a privacy and civil liberties perspective, that should bother people more than it probably does.

Supporting Anthropic’s position is easy now: murderbots and mass surveillance are indeed very bad and AI shouldn’t be used for such things. But what happens after Anthropic IPO’s and there are shareholders to placate? What happens if the company decides that their red lines have become a very murky grey?

So, we all get to wait and see who chickens out first—Anthropic and its enterprise customers or the administration. And we find out: will Anthropic be one of the first un-governable companies?

Still, this one had a catchy title. The Wyoming Guaranteeing Rights Against Novel International Tyranny and Extortion (GRANITE) Act. Immediately, something in the back of my brain went off, signaling to me that this was … strange, but I couldn’t put a finger on it at first. All I could think was ‘Why the hell is a free speech law coming out of the Equality State called the GRANITE Act?’

I promise, I’ll get to that in a bit, but first, I want to tear into this baby. Or rather, I’ll say, Claude and I are going to tear into this, mostly because I’ve modeled my version of Claude to be as snarky as I am. This is all written by me, but Claude pointed out some very interesting points that I’d missed, which I note below.

I freely admit that while I practiced law in the US, and know a bit about EU law, I am not a Constitutional scholar or an international law expert by any means. Neither is Claude, but then again, it’s probably smarter than the politicians who drafted this obviously unconstitutional bill, so… YOLO

All hat, no cattle (or logic, or reason)

So the Tl;Dr of this bill is as follows:

AN ACT … creating a cause of action against foreign states and international organizations regarding foreign censorship laws that violate specific constitutional provisions

If that wasn’t obvious enough, it’s basically a broadside against Big Tech regulation or enforcement. In fact, the law explicitly calls out the UK Online Safety Act and Ofcom’s letter to 4chan threatening fines of $25,000,000, and the Brazilian Supreme Court’s order against X and Trump’s Truth Social forcing the companies to censor certain user accounts in Brazil. For you see, the drafters in Wyoming are very concerned that all those nasty foreign laws “threaten[] Wyoming’s leadership in decentralized finance and digital assets”, which is a little weird?

So, four Republican Freedom Caucus politicians (naturally), Daniel Singh, Ken Guggenmos, Scott Heiner, and Bob Wharff hatched this big, beautiful bill to protect Wyoming citizens, businesses, or, well, anyone who casually intersected with the state physically or digitally, ever.1

Reader, I want you to take a moment and close your eyes. Pretend you’re a Wyoming politician. You’ve dismounted from your horse, set your cowboy hat on the table, poured yourself a full measure of Wyoming Whiskey, loaded up your nearby firearm2, and you start drafting.

You want to craft a law that will help Wyoming stand for freedom from tyranny (except, y’know, if you’re trans or want an abortion), protect free speech (except if the speech is about sex, gender-identity, or its speech you personally don’t like), and also to defend America from Europe. But mostly, you want to gin up lots of likes on X and guarantee prime-time interview slots on Fox News and One America News Network. So, you realize that to get those coveted slots, you’ve gotta go big and really swing for the fences.

But really, I’ve got to say, if you’re trying to make your mark, you might want to at least avoid strangling your bill before it even gets to committee. In other words, you probably should avoid writing something like Section 1‑44‑104(b) and (c):

1‑44‑104. Cause of action; statute of limitations; construction.

(a) A plaintiff described in W.S. 1‑44‑105(a) shall have a cause of action, to the extent permitted by federal law and subject to subsections (b) and (c) of this section, against any foreign state, international organization or any officer, employee or other person thereof acting within the scope of their official duties who has threatened to enforce, attempted to enforce or enforced a foreign censorship law against the plaintiff in a manner that would violate the first amendment of the United States constitution, Article 1, Section 20 of the Wyoming constitution or Article 1, Section 21 of the Wyoming constitution.

(b) The cause of action under subsection (a) of this section shall apply only to the extent permitted by federal law, including the Foreign Sovereign Immunities Act and the International Organizations Immunities Act and any applicable headquarters agreement, treaty or executive order.

(c) Nothing in this chapter shall be construed to:

(i) Waive the sovereign immunity of any foreign state or international organization;

(ii) Limit any exceptions to sovereign immunity available to the plaintiff under the federal Foreign Sovereign Immunities Act;

(iii) Limit any right of a defendant to remove the case from state court to federal court as provided by federal law;

(iv) Limit any defense available to the defendant under federal law;

(v) Regulate foreign states or international organizations or to conflict with the foreign affairs powers of the United States.

(Emphasis all mine)

I mean, savings clauses are in every piece of legislation, but this isn’t a savings clause so much as a ‘gutting clause’. How do you sit down, write this and not think to yourself, ‘Hey… Is my law even a law at this point?’

Section 1-44-104(b) is a self-destruct clause. Between good old-fashioned federal preemption, FSIA explicitly gives foreign sovereigns near-blanket immunity with narrow exceptions (commercial activity in the US, tortious acts causing injury in the US, terrorism etc.). If the Paris police conduct a raid against the Paris office of X, to enforce against the creation and distribution of CSAM in France, the foreign law is almost certainly not going to fit into any FSIA exception, even if the administration further expands what free speech means or the ‘domestic terrorist’ classification. So the bill creates a cause of action giving internet trolls the right to sue over foreign censorship laws, then says lol, J/K guys. That’ll show the EU!

Section 1-44-104(c) doubles down on this by saying the bill doesn’t “waive the sovereign immunity of any foreign state or international organization” and doesn’t “regulate foreign states or international organizations or conflict with the foreign affairs powers of the United States.” So what does it do?

1. And then there’s the preemption problem. This bill also fails poorly at handling two different flavors of federal preemption under the US Constitution. First, there’s field preemption, which bars state laws that would allow, say a state court to evaluate or adjudicate over matters that are squarely in the domain of the executive branch. Like foreign policy.
   
   But evaluating political systems and laws of foreign governments is exactly what this bill does —Section 1-44-106 specifically creates a presumption that foreign law being challenged is automatically a “foreign censorship law” then obligates foreign sovereigns to justify their own foreign laws to a Wyoming court. This is a textbook example of “intrusion by the State into the field of foreign affairs”3 which the Supreme Court has a long track record of finding unconstitutional.

   
   Second, there’s conflict preemption which preempts state laws that would conflict with a direct interpretation of policy by the President, even where legislation is silent, or the conduct is merely indirect. Or, as the Supreme Court explained in American Insurance Ass’n v. Garamendi, 539 US 396 (2003), there’s going to be a conflict between the state and federal governments if a state takes "an iron fist where the President has consistently chosen kid gloves."4
   
   That indirect bit is probably enough to torpedo this bill into the sun.

2. …and the Supremacy Clause. Section 1-44-108(f)(iii) prohibits Wyoming officials from honoring Mutual Legal Assistance Treaties (MLAT) requests related to speech. But MLATs are ratified treaties — It’s right there in the name. That means under Article VI of the US Constitution, they’re the supreme law of the land. The bill’s “except as required by federal law”, is circular because complying with MLATs by offering mutual legal assistance in terms of handling an MLAT request is … exactly what “required by federal law” means. Guys. It’s in the name.

3. Oh, and let’s not forget our dear friend, standing and personal jurisdiction. Section 1-44-105(a)(iv) gives broad standing to “any United States person whose protected expression... originates from or is hosted on servers physically located in this state.” Any company in America could rent a server in Cheyenne and suddenly have standing under this act. Combined with Wyoming’s existing popularity as a corporate registration state and its deep interest in becoming the US’s pre-eminent destination for crypto/de-fi/DAO shenanigans, this is essentially creating a “censorship shield” flag-of-convenience scheme.5 Even in this administration, I don’t think federal courts will let this one fly.

4. Service of process by Truth Social Post or in the comments. Okay, so it’s not quite that stupid, but the GRANITE Act is pretty close. Section 1-44-105(d)(iii) allows service on an international organization “by publishing on a website maintained by an international organization, if the international organization is evading notice.” Due process and the FSIA both require service reasonably calculated to give actual notice. Mullane v. Central Hanover Bank, 339 U.S. 306 (1950). Which is to say, posting on, r/europe, in the comments section on a random EU website, or on X is unlikely to satisfy this standard. Though Ursula does keep pretty active on X, so…

5. The definition of ‘foreign censorship law’ and the cause of action requirements are hilarious. The definition of “foreign censorship law” in 1-44-103(a)(ii) requires the law to be “final, binding and enforceable” and to restrict expression based on “content, viewpoint or speaker identity.” But note: there’s no nexus requirement in the definition of what a foreign censorship law is, or where the predicate act occurred. Only where said law is ‘enforced’ in a rather expansive definition of that term.
   
   As I mentioned above, this law would allow anyone who could claim some amount of Wyoming-ness to sue any foreign government provided that they allege that said foreign government “has threatened to enforce, attempted to enforce or enforced a foreign censorship law against the plaintiff” and Wyoming was vaguely involved. It doesn’t matter where the act violating said law occurs. All you need is a data center in Wyoming! But it’s even broader than that: currently, if some idiot from Wyoming went to Thailand, insulted the monarch in Thailand to his face, came home, and Thailand sent an angry letter alleging violations of Thailand’s lèse-majesté law, that idiot could then turn around and sue the Thai government for violating said idiot’s free speech rights under the GRANITE Act.
   

6. The damages section is just bananas.
   

1‑44‑107. Remedies; liability.

(a) Upon proof by a preponderance of the evidence that a defendant threatened to enforce, attempted to enforce or enforced a foreign censorship law against a plaintiff in violation of the first amendment of the United States constitution, Article 1, Section 20 of the Wyoming constitution or Article 1, Section 21 of the Wyoming constitution, the court:

(i) Shall award the prevailing plaintiff:

(A) Actual damages as proven by the plaintiff or nominal damages if actual damages are not proven by the plaintiff;

(B) Statutory damages of one million dollars ($1,000,000.00) per violation, as adjusted for inflation under subsection (c) of this section or ten percent (10%) of the defendant’s annual United States related revenue, whichever is greater. Statutory damages imposed under this subparagraph shall not be less than the amount of any fine or penalty imposed by a foreign state or international organization regarding the foreign censorship law. …

(b) The remedies provided under this section shall be cumulative and shall be in addition to any other remedies available at law or in equity. The statutory damages provided under subparagraph (a)(i)(B) of this section shall be compensatory damages that are intended to compensate plaintiffs for harms that are inherently difficult to quantify, including chilling effects on protected expression, reputational harm, self‑censorship costs [Self-censorship costs, LOL—ed] and litigation costs incurred in defending against enforcement of foreign censorship laws and shall not be punitive damages.

(emphasis mine)

Right. So, it’s $1M per violation or 10% of the defendant’s annual US-related revenue, whichever is greater, and it can never be less than the foreign fine imposed. So if the EU threatens a 6% of global revenue fine under the DSA, the statutory damages floor is that amount. For Meta, that would be in the billions. The bill then labels these as “compensatory” rather than punitive in subsection (b), which, as Claude reminds me, is transparently an attempt to dodge two Supreme Court cases, BMW of North America v. Gore, 517 U.S. 559 (1996) and State Farm v. Campbell, 538 U.S. 408 (2003) which impose due process limits on punitive damages. Courts aren’t going to be fooled by that label — $1M per letter received is not “compensating” anyone for anything. The three Gore guideposts (reprehensibility, ratio to actual harm, comparison to other civil penalties) would demolish these numbers. These are just insane.

7. Inflation calculations make no sense.

1‑44‑107. Remedies; liability.

(c) The court shall adjust any statutory damages granted under subparagraph (a)(i)(B) of this section for inflation. The court shall calculate the adjustment for inflation by multiplying the statutory damages amount of one million dollars ($1,000,000.00) by the ratio of the seasonally adjusted M2 money supply value published in the federal reserve statistical release … [the section on M2 money supply values goes on for another paragraph]. …

(d) Except as required by federal law, including the Foreign Sovereign Immunities Act and the International Organizations Immunities Act, all defendants liable under subsection (a) of this section shall be jointly and severally liable for all damages incurred by the plaintiff.

So, I need to confess that I didn’t catch this at first, but Claude sure did! Section 1-44-107(c) adjusts statutory damages not against the Consumer Price Index (CPI) but the “seasonally adjusted M2 money supply value.” This is strange because nobody in normal legislative drafting reaches for M2 as an inflation adjustment. You use CPI. Nearly every federal statute that adjusts penalties for inflation uses CPI. State statutes use CPI. The Sentencing Guidelines use CPI. It's CPI all the way down.

By contrast, the M2 calculates the total amount of money in circulation (cash + savings deposits + equivalent instruments), which can fluctuate wildly based on Fed policy — it expanded roughly 40% between early 2020 and early 2022, which would have inflated the statutory damages from $1M to $1.4M in two years, not because of any change in the value of a dollar but because the Fed was doing QE. Using the M2 to account for dollar valuation in a court case would be nuts, which is why nobody does this.

But do you know who does use M2 to measure value? Take a guess… I’ll wait.

Though if you’ve figured it out and haven’t

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This is so transparently dumb

Did you guess crypto? Because it’s crypto!!!6

I had to read it again before I saw repeated mentions of blockchain,” “decentralized technologies,” and “digital assets” (Sections 1(i), (iv), (vi), (xv)). Which, like the GRANITE name smells funny because … WTF does crypto have to do with foreign censorship?

Thankfully, Claude got it in one:

The bill’s author, Preston Byrne, is a crypto/tech litigator. This is fundamentally a bill designed to shield crypto companies from foreign regulatory oversight, wrapped in First Amendment rhetoric. The EU’s MiCA regulation, which has speech-adjacent provisions around marketing and disclosure requirements for crypto assets, would arguably fall within the “foreign censorship law” definition because it “compels disclosure regarding expression.” That framing is doing a lot of heavy lifting.

I was dubious at first because it was the freedom caucus lads who wrote the thing — seeing as Preston Byrne appears to live in Connecticut.

For the unfamiliar, Byrne, is a rather vocal crypto & free speech lawyer (both in the US and UK) and prolific X poster, who has made it his mission to challenge crypto regulation, and age-verification laws like the UK Online Safety Act. He also represents 4chan in litigation against Ofcom. Byrne has made no secret about the fact that he is the father of the GRANITE Act, which, was basically cribbed from this blog post he wrote in October 2025. I shit you not.

It turns out that he initially drafted this statutory turd for the New Hampshire legislature (hence the bacronym to New Hampshire’s state motto of ‘the Granite State’), but it looks like Wyoming was quicker to take the bait. He’s got a whole timeline here.

Admittedly, I am not entirely unsympathetic with his cause — I personally think the OSA, as well as most of the ‘save the children’ age verification bills and even parts of the DSA are terrible. They’re poorly implemented, ill-conceived, subjective, and rights-eroding. He and I are aligned in terms of ends, but my god, the means he’s using in the GRANITE Act are just galactic-levels of dumb.

And of Common Sense Wyoming was even more blunt about it: he described it as “not Dan [Singh]’s idea, or even a Wyoming idea” but rather “the brain child of Preston Byrne,” a “legislative bait-and-switch by a D.C.-based crypto/tech litigator” that “promises free speech then delivers a shield for international crime.” Clearly, Jim isn’t a fan.

So, once again, we’ve got a state legislature (potentially multiple legislatures) being used as a vehicle for an out-of-state lobbyist’s pet project, dressed up in First Amendment language. This isn’t Wyoming legislators spontaneously deciding to protect “decentralized finance and digital assets” or even free speech from EU regulators. It’s an east coast tech lawyer shopping for a friendly legislature, and Wyoming’s existing crypto-friendly statutory framework (DAO laws, etc.) made it an obvious target.

Maybe someone doing the drafting realized this, which is why they stuffed it with “to the extent permitted by federal law” qualifiers throughout. The result is a bill that is either (a) unconstitutional where it purports to override federal law, or (b) superfluous where it defers to federal law. There’s very little middle ground where it does anything novel and simultaneously legal. It’s a messaging bill with a backronym, designed to generate exactly the kind of press coverage and X posts that Byrne has been living off of for years.

In closing, this isn’t just a dumb law. It’s a law that knows it’s an unenforceable turd, and was designed to look tough despite being entirely pointless. It’s the kind of thing I’d expect to see out of the Freedom Caucus.

Sadly, this is probably not going to be the only iteration we see: Byrne seems to have the ear of many conservatives and fellow crypto-shills in both the federal and state legislatures. We’re likely to see variants of the GRANITE Act pop up throughout the year.

The self-defeating structure of this law then is a feature, not a bug — it lets supporters claim they’re fighting foreign censorship without ever having to deal with the consequences of actually doing anything but tying up the courts with lawsuits filed by freeze peaches warriors and crypto cranks. I’m not exactly worried about these rock-laws, but I can’t look away either. The stupid burns so brightly, and we can’t shield our eyes.

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So, now it’s time for you to tell me why I’m wrong by leaving a comment!

In my Ladder to Nowhere series I shared my concerns (and even a plausible scenario) around what a world might look like if a single company like OpenAI managed to integrate its entire AI ecosystem into our lives. It starts slowly of course, through convenience and utility, but ends up as a tool of surveillance, rights erosion, and oppression. As I said:

We’ve built an economy where comprehensive surveillance is not only necessary, but socially accepted. Our data and attention are how the internet survives. We’re also living in a time where Bay Area oligopolies and pay-to-play corruption is what’s come to define America, and fear of pissing off the deranged toddler in the White House is enough to paralyze sovereign nations.

I can’t stop thinking about this problem. About how we’re all sleepwalking our way up invisible ladders where we cede more of our data, our tools, our choices, and our lives to a handful of powerful, extremely well-connected entities. Not intentionally, or all at once, but gradually, then suddenly, as Hemingway wrote.

AI is part of this, of course. But it isn’t just AI. It’s about tools and infrastructure, and importantly, technological extensity.

Intensity vs. Extensity

First, we need to start with some definitions, which are crucial to getting to the heart of my thesis, namely Intensity vs. Extensity.1

Intensity occurs when a company or product becomes indispensable or necessary based on its quality or uniqueness, or, in the case of a person, their deep mastery or skill in a subject or field. Michelangelo was considered intensive—his mastery across different artistic domains made him a sought-after artisan during the Renaissance.

Famous three Michelin-star restaurants like The French Laundry in the US or Noma in Denmark, also have intensity. Their uniqueness explains why it’s nearly impossible to get a reservation unless you plan months in advance. Or take Shohei Ohtani, who has the rare quality of being both a phenomenal pitcher and batter. That gives Ohtani a ton of leverage within the realm of baseball, just as Michelangelo had in the world of art.

The thing with intensive systems is that usually they’re impermanent. Athletes retire. Chefs hang up their whites. Technology improves, and of course, products and services enshittify. So, while intensity may allow a company or person to become temporarily dominant and powerful within a market, that power is often short-lived. By contrast, I argue that extensity is where the real power is at.2

Extensity describes something broad in size or scope, that becomes deeply entrenched in a system. Unlike intensity, extensity is about spread, not mastery. You become extensive not necessarily by being the best, but by spreading out and becoming indispensable to the system itself. In 48 Laws, Henry Kissinger was cited as being an extensive force in geopolitics, diplomacy, and international relations. He was a fixture across administrations, and remained a power broker long after he left politics. Here’s Greene’s take in Law 11:

Henry Kissinger managed to survive the many bloodlettings that went on in the Nixon White House not because he was the best diplomat Nixon could find—there were other fine negotiators, and not because the two men got along so well: They did not. Nor did they share their beliefs and politics. Kissinger survived because he entrenched himself in so many areas of the political structure that to do away with him would lead to chaos.

Some of you might be thinking to yourself: ‘Hey idiot, none of this is new, we’ve already got a term for this when it comes to businesses: monopoly.’ After all, a monopoly represents complete control or dominance within a market. Horizontal monopolies give organizations the power to set the price of goods or services, dictate what is made available to customers, and create barriers to entry for potential competitors.

But horizontal monopolies, like extensive humans, aren’t necessarily guaranteed. Case in point: throughout most of the 20th Century, AT&T held a near-monopoly in telecommunications, cable television, and related professional services, before it was broken up in 1982. Microsoft was extensive in the browser, office productivity, and operating system market (they still are, to a lesser degree with Windows and Office365), so much so that the US government attempted (and failed) to pull an AT&T Part 2 in the 90s.

Still, when I think about technological extensity, it feels bigger than even a traditional monopoly. For one, I don’t think it necessarily requires that a company reach technical “monopoly” status at all. All that extensity needs is deeply rooted integration within the system in such a way that removal becomes effectively impossible without leaving major gaps behind. When I say “the system” I’m referring not just to software, networks, and infrastructure, or financial institutions and governments, but everything we come to depend on that helps keep society functioning.

This idea first materialized in the financial sector with the bailouts during the 2008 financial crisis. If a bank is “too big to fail,” that’s just a catchier way of saying that bank has become entrenched in the financial system.

We humans rarely learn from our mistakes, and so, we’re starting to see this more and more with Big Tech. Take Google, for example: Google commands 82% of the market in search, 66% of the market in web browsers, and 45% of the market in email, despite loads of competition in each product area.3 They have successfully dodged the monopoly moniker because legitimate competitors do exist.

And yet, people have been lamenting the continual decline of Google Search for years, and regularly complain that Chrome is a bloated, ad-laden, data vampire.4 Most everyone I know has a Gmail account, even if they loudly proclaim that they hate Google. To me, this indicates that we’ve come to rely on these products through a combination of network effects, habituation, and inertia, to the point that they’re part of the internet itself.

I’m also noticing this trend start to develop at a literal planetary scale when it comes to SpaceX’s reach. SpaceX’s evolution from a cool space company to potential “everything company” for Elon Musk, should freak people out way more than it does, and yet, it doesn’t. Guys: SpaceX was responsible 85% of all space launches in the United States. This one company launched almost twice as many orbital missions as China did in 2025. Starlink (which is part of SpaceX) alone made up 123 of SpaceX’s 165 launches in 2025, and lofted more than 3,000 Starlink satellites into orbit as part of the company’s massive 11,000 satellite mega-constellation. That’s 11,000 satellites out of a total 15,644 man-made objects in space right now.5

Meanwhile, over the span of what seemed like a long weekend, Musk managed to merge SpaceX with his AI firm xAI with nary a raised eyebrow by regulators. Musk’s other company, Tesla, invested $2bn in xAI in January. This is all part of his larger efforts to put data centers in space and colonies on Mars and to usher in an era of “amazing abundance”.

Now, I can’t predict whether Musk will ultimately be successful, but what his X-empire (xAI, SpaceX, Tesla) may very well succeed at is finding newer, bigger, and bolder ways to make Musk and his companies vital and necessary parts of everything.

This means that one company, nay, one man, who has an estimated net worth somewhere in the neighborhood of $690-852bn has, and continues to amass enough power, connections, resources, and wealth that he can not only ignore consequences, regulatory or otherwise, but also affect geopolitical outcomes by taking his toys away, or cajoling governments to cut-off funds to programs he doesn’t like or find value in. Don’t take my word for it—ask the Ukrainians whose Starlink access Musk has repeatedly restricted during the war, or the 550,000 children Musk and DOGE may have indirectly killed by defunding USAID.

Too Big to Fail?

Here’s a question: What happens when extensive tools or companies fail? What happens to society if we lose access to Gmail, or Starlink, if AWS or Azure die, or if the AI bubble bursts abruptly? How easy will it be for us to collectively recover now? What if we keep building these tools into more of our lives?

To answer this question, we need to talk about lock-ins.

And no, I’m not talking about the fun kind at pubs in Dublin. I’m talking about vendor & collective lock-ins.

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Vendor lock-in is easy to see: So much of our lives (including the entire foundation of this post) are built around using technical tools supplied by a handful of companies to communicate. For many reasons (familiarity, habit, self-interest, marital harmony) I’m primarily a Google user—I use an Android phone, Gmail, Google Calendar, and Google Drive. Many of my clients use Google Workspace. I even use Gemini and Notebook LM (though not exclusively). These tools have crept into my life and I’ve grown incredibly reliant upon them all working together. I’m reliant not because there aren’t options, but because the very act of switching creates friction and like a diet, is extremely hard to maintain over time.

Last year for example, I tried moving all of my documents over to Proton Drive, because Google Drive isn’t end-to-end encrypted. Plus, I wanted to see if I could. The migration was painful and incomplete. Many files were only accessible in Google. I also had to give up after a few months because I was limited in what I could do in Proton Drive. Want to access a document shared on Drive by someone? Good luck with that—you’ll need a Google account. Trying to save that document on Proton? Fat chance—Proton can’t read (or even store!) .gdoc files. And you can forget about cross-platform collaboration. Some of this was due to Proton Drive being painful to use, but most of it was due to the fact that everybody else uses Google.

And that leads to the second type of lock-in: collective, or identity lock-in. The cost of leaving Google (or Apple, or Meta, etc.) isn’t just inconvenience, it’s also about shattering the identity, friendships and connections that has evolved around ‘being online’. This is most often cited in relation to social media, but it’s starting to creep up in terms of AI. Resistance is increasingly becoming, to quote the Borg, futile.

And there are social costs. For example, during the pandemic I tried to actively stop using WhatsApp, but found it was essentially impossible in Ireland, because WhatsApp and Facebook had at some point become the de-facto messaging platforms and communications channels in the whole of the country. Partly this is because the state of SMS and MMS sucks, but the root cause is irrelevant. It’s hard to fight Big Tech when you’re isolated in your house during the pandemic and can’t talk to most of your friends.

Our tech tools, and the algorithms that drive them, have helped to define who we are. Platform-mediated reality is creating incompatible epistemic communities and belief systems, which is to say, people are increasingly likely to interpret the same event wildly differently based on where they interact online. We all know that what we read and who we follow is increasingly being decided for us by recommendation engines and opaque algorithms.

But it’s not just that: research reveals striking differences in opinion about major news events based on a user’s platform-of-choice (X, cable TV, Facebook, podcasts, etc.), while charitable giving studies show how fundamentally different priorities across political ideologies have intensified. Americans in particular, increasingly inhabit entirely different informational spheres, which in turn, shape individual identities.

AI, of course, isn’t helping any of this. For example, a recent Syracuse University study found that 27% of users formed deep emotional bonds with OpenAI’s GPT-4o, with some people literally in mourning OpenAI retired the chatbot last week. This kind of psychological entrenchment leads me to worry that the biggest companies are not only too big to fail, but also that they’re increasingly becoming too big to govern.

Too Big to Govern?

We’ve already seen a hint of this when it came to the TikTok ownership drama. First there was the 14-hour ban in January 2025, which led to such a backlash by users (and politicians who use TikTok) that the Trump administration hit the pause button on a policy choice Trump himself had championed in his first term. And while it’s true that OG TikTok is now effectively dead, users can’t seem to quit the reanimated, right-wing-controlled zombie that replaced it. Here’s CNBC’s take:

Survey data from market intelligence firm Sensor Tower show that, despite a surge in deletions following the announcement of TikTok’s U.S. joint venture on Jan. 23, the average number of TikTok’s daily active users in the U.S. remains around 95% of its usership compared to the week of Jan. 19-25.

SimilarWeb data indicates even fewer defections. According to their January 2026 data, TikTok shed only 0.76% of its US user-base between November 2025 and the end of January 2026.

Now, I’ll concede that losing anywhere between 1-5% of active users is still losing, it’s still indicative of a larger trend: most people are happy to stick around no matter who’s calling the shots. They’ve built at least some part of their identity and habits around TikTok, no matter which shadowy set of billionaires actually runs the show. So, the government might be able to change who “owns” TikTok (though ByteDance still maintains a 20% stake), but they can’t change what TikTok is or break its hold on users. That’s the difference between regulating a monopoly and trying to govern an extensive system.

Oh, and apropos of nothing in particular: https://www.tiktok.com/@realdonaldtrump

To me, this is extensity in action.

Or consider the recent Grok sexual deepfakes saga. As I wrote in January:

Over the last week or so, people discovered (again) that X, and in particular, X’s chatbot Grok, has become a veritable garbage-making factory as it spews out hundreds, if not thousands of images of actresses, regular users, and even children as young as two, in sexualized poses, varying states of undress, and being assaulted or abused. And of course, because it’s X, none of this is consensual, and most of it targets women and girls. This has all been going on since around December 28, according to press reports.

Child sexual abuse material and other non-consensual imagery was freely peddled on the everything platform (which as I noted, at least for CSAM, is illegal nearly everywhere), and almost nothing happened. Sure, there was a lot of press and outrage, and a few brave governments amended their laws or actually banned Grok (but not X). But most western governments mugged for the cameras and otherwise sat on their hands. Some governments over here in Europe pretended to be outraged. For example, France made headlines (overshadowed by the SpaceX/xAI merger, naturally) after authorities raided X’s Paris office in response to X’s delayed response to the Grok-pocrisy. And yet, here’s President Emmanuel Macron posting on X today:

Likewise, here’s the EU Commission President, Ursula von der Leyen, who was “appalled“ by Grok in January, posting on X on February 14:

Maybe von der Leyen and Macron and all the other countless politicians who keep posting on X really do want to regulate in their hearts, but they can’t. X has become so extensive that despite openly peddling CSAM and Nazi apologia, it continues to be the beating heart for political outreach around the world.

Moloch, Agency, and the Race to the Bottom

I recently read a very old, but very good piece on Slate Star Codex (now Astral Codex Ten) by Scott Alexander which I’d stupidly managed to somehow miss for over a decade. In Meditations on Moloch, Alexander attributes our broken, deeply dysfunctional system to Moloch—the Carthaginian demon god who doubles as the personification of industrialization in Allen Ginsburg’s famous work Howl and Other Poems. Why is the system shit, Scott and Ginsburg both ask? Moloch!

The implicit question is – if everyone hates the current system, who perpetuates it? And Ginsberg answers: “Moloch”. It’s powerful not because it’s correct – nobody literally thinks an ancient Carthaginian demon causes everything – but because thinking of the system as an agent throws into relief the degree to which the system isn’t an agent.

Scott later reminds us that Moloch is essentially us. The agency isn’t the system, but it’s what’s built into the systems we create. And even though Scott wrote this in 2014, it’s arguably also the literal AI agents that are increasingly running more of the show.

But this agency, and the modern-day Moloch we’re up against is also embodied in the Big Tech race-to-the-bottom mentality, the willingness to sacrifice values, morals, and accountability, like the Punics sacrificed so many children. It’s in the mindset of taking any risk just to be first, to hell with the consequences, and the willingness of governments, regulators, and people with power to sit by and just let it happen.

Once one agent learns how to become more competitive by sacrificing a common value, all its competitors must also sacrifice that value or be outcompeted and replaced by the less scrupulous.

Now, Scott was referring to agents in the classical sense here: entities or individuals who act, exert power, or produce independent effects, usually (but not exclusively) on behalf of another.

But there’s nothing that restricts this to human or even corporate agents. To me, it seems entirely plausible that some of the technical systems we are developing are themselves becoming agentic, by producing effects and exerting some degree of power over us on behalf of someone else. I’m not quite at the level of asserting (as my learned friend has) that “algorithms” generally have this property, but I don’t think he’s wrong if one considers “algorithms” collectively, i.e., as part of a larger system or set of systems and tools working to accomplish goals on behalf of their creators.6

In a normal, healthy capitalist system, customers, shareholders, and regulators decide with their wallets and their rules who lives and who dies. Fit, beneficial, lawful, and productive companies survive, unfit, unlawful, or unproductive companies go bankrupt or otherwise cease to operate.7 And historically, this has mostly been true. Millions of bad companies have gone bust. A smaller number of firms were broken up, forced to restructure, or otherwise regulated into changing their behavior.

But we’ve never faced capitalism in a world where a handful of companies have managed to amass the level of power and wealth that exist today, with the ability to engineer systems that are so intertwined and spread across so much of our lives. The technology on the market today is becoming too big to control.

Right now, there are no real barriers—no meaningful bulwarks or disincentives to stop what appear to be a handful of men from essentially owning all of us. Musk’s dream of “amazing abundance” fails to answer an important question: amazing abundance for whom?

There’s also no accountability either, because everyone with the power to actually do something is too busy using the tools they’ve sworn they’ll regulate. Yes, we’ll get a few token fines, or threatened actions here and there, but that’s part of the theatre. Yes, the companies might pretend to be chastened for a time, but that will only teach them to be less obvious about their intentions.

There will always be talk about content moderation or banning Facebook, or X, or TikTok, or regulating Google, Apple, Amazon, or maybe even SpaceX, but nothing meaningful is likely to come of it, because why would it? How could it? In truth, regulatory responses seem to fall into four camps:

• YOLO, let the planet burn (the US)

• pearl-clutching and regulating by press release through a handful of token fines that sound impressive but aren’t, because the regulators fear the consequences (the EU, Brazil)

• developing government-run corporate counterparts (China), or

• quietly ignoring the problem and hoping the deranged toddler-in-chief and his corporate overlords will focus on the bigger fish and not tariff them out of existence (most of the rest of the world).

Fun Fact: Ireland has levied over €4.04 billion in fines against Big Tech companies over the last six years, primarily against Meta. Of that total, just €20 million has been collected according to a January 2026 FOI disclosure filed by Ken Foxe. Most of that holdup related to a court case brought by Meta and its subsidiary, WhatsApp, who sought to annul the fines.

Fun Fact #2: The EU Court of Justice sided with Meta.8 If I hear one more person scream ‘but fines!!!!’ I’m going to lose my damn mind. Fines only work if they’re enforced, but if the companies being fined have captured the enforcement mechanisms (or can tie things up in court for years), they’re little more than theatre and bluster.

Now ask yourself, what will this situation look like if someone like Musk or Bezos actually succeeds and takes this whole affair interplanetary?

We’re already seeing how Big Tech influences governments and shapes narratives. But just imagine this in five or ten years. Imagine a multi-trillion-dollar SpaceX, Google, Amazon, Meta, Oracle, or Microsoft (or a consortium of them), bolstered by super-intelligent AI systems, doing basically whatever they want. It’s all well and good to have laws, but if a handful of corporations become effective states unto themselves—suppliers of the information, infrastructure, energy, technology, supply chains, and even the money— what even are laws at that point?

And while the US is a lost cause (and will continue to be so for a decade), over here in the EU, regulators are still framing things in the context of classical monopolies and anti-competitive behavior. We’re still trying to impose old rules on entities that are increasingly becoming so integrated into the system that they are effectively ungovernable. We’re all still using Microsoft, Google, Apple, Facebook, Instagram, X, and OpenAI because Europe doesn’t have anything to replace it that’s even remotely comparable.

See, unlike the AT&Ts and Standard Oils of the past, the handful of companies really running the show today control the informational substrate—the algorithms and engines that shape what we see, who we talk to, how we understand reality. SpaceX, Amazon, Microsoft, Nvidia, Oracle, and Google control the infrastructure that props up the internet. OpenAI, Anthropic, Google, and Meta control the AI. Most of these companies + Oracle/TikTok control the media. Together, they’re integrated into our identities in ways that make them fundamentally harder to disentangle from. It’s like that Geiger painting at the top—a pile of interconnections that can’t be easily severed.

We’re all worried about some super-sentient AI coming around the corner and putting us out of work, and that’s probably a valid concern. Meanwhile, we’re (un)happily trusting a handful of companies with everything and giving them lots of opportunity to create further extensive reach. The US, and to a large extent, Big Tech is leading a race to the bottom, and the leaders of the world are basically shrugging and going along with it.

Right now, we still have a choice. But 10 years from now? I’m not so sure.

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In Part 1 of this series, I made a bold claim: OpenAI is spending a tremendous amount of time, engineering effort, and money to build an AI ecosystem that, if successful, will provide the company with an unprecedented amount of data about its users.

How? By building a ladder, getting users to incrementally climb up, one rung at a time by developing useful, helpful tools, and creating a need.

ChatGPT was the start, of course, but it goes much further than that. OpenAI has ambitions, and they’re aggressively pushing tools that will give them more insight into each of us. For example, the Atlas Browser, and ChatGPT Health. But also wearables, and potentially even a brain-computer interface. In Part 1, I also explained how they’re building the infrastructure to handle all of this data, as part of a series of circular partnerships with companies like NVIDIA, Oracle, AMD, Amazon, and others.

Right now, OpenAI is hemorrhaging cash at a clearly unsustainable pace: it spends 3x what it earns, and only 5% of its 900M users pay for the privilege of using ChatGPT at all. Conventional revenue generators like context-based ads, enterprise deals, and subscriptions, will never be enough to help them reach this end-goal.

Based on my research sifting through what the company has publicly shared – in product launches, partnership announcements, acquisitions, and media interviews – I believe OpenAI, if successful, is designing an ecosystem that will allow the company to have a complete picture of each of its users. How they exist online and offline, their health and wellness, their hopes, fears, desires, and needs … and potentially, even their thoughts.

See, to really make money, they need a moat–something that no other tech company has been able to achieve thus far: a complete picture of each of us. In the next few sections, I explain what this world might look like, and how they’ll get people to willingly climb the ladder they’ve laid out.

I. Why OpenAI wants you so badly

“This model can reason across your whole context and do it efficiently. And every conversation you’ve ever had in your life, every book you’ve ever read, every email you’ve ever read, everything you’ve ever looked at is in there, plus connected to all your data from other sources. And your life just keeps appending to the context.” Sam Altman, speaking at an AI event hosted by Sequoia Capital

Separately, each of the products OpenAI is developing (even, to some degree, the brain-computer-interface with Merge Labs) are all interesting, but not exactly eyebrow-raising Big Tech moves. Agentic AI is hot. There’s a growing number of entrants in the AI browser market. Google, Apple, and Anthropic are all-in on the health bandwagon, while Google & Microsoft are well-established incumbents in the AI+OS market, with Meta and Amazon hoping to catch up. Plus, if you were paying any attention to CES 2026, AI wearables represent the next battleground. Hell, Apple just announced a wearable to compete directly with ChatGPT’s planned release sometime later this year.

But when you piece the data puzzle together, what OpenAI seems to be planning starts to become deeply concerning. Here’s that table I provided at the start of the article, with a slight modification on the right:

Combined, all of these tools would give OpenAI a wealth of information and insight on users (and potentially, non-users), previously impossible with targeted ads or even more traditional forms of surveillance alone. This data makes each user a target--for corporate interests, advertisers, governments, social engineers, and adversaries alike.

II. The helpful ladder

I worry that OpenAI and Sam Altman + the legion of funders, strategic partners, and powerbrokers supporting him–will succeed precisely because many of these features either are, or can potentially be useful and interesting in isolation. Returning to my ladder analogy from Part 1, most of us have already used ChatGPT at least once in our life. Many of us have tried the Atlas browser. These are relatively safe and harmless.

I suspect that many people, out of curiosity or genuine need, will also try Dr. Chat, GP. That still won’t make it impossible to separate from OpenAI if things get weird, but it will start to feel more like a loss. Like a bad relationship, sometimes it can be hard to walk away, especially if you’ve become invested.

And the same will be true for each step we take. The trouble is, our brains are primed to think in the present -- to judge each rung relative to the one that came before it, where we’re standing now, and where we want to be-–not to necessarily consider the consequences of current actions on our future selves. When we’re climbing, we tell ourselves that we’ve already made it this far, and the top seems so close, we might as well keep climbing.

After researching this piece and trying to understand where Altman & Co. are headed, I decided to write a little story, a sketch of what such a world might look like.

1. The First Rung: January 2026: You’re a woman with a familial history of ischemic strokes. You know that the symptoms of a stroke in women are different from those in men. However, your doctor, overworked and swamped, hasn’t been keeping up with the latest research, you’re young, healthy, and aren’t exhibiting any obvious stroke-like behaviors. He tells you you’re worrying over nothing. Typical.
   
   One day, you open up the Atlas browser, tell ChatGPT about some worrying symptoms you’ve been having–blurry vision, dizziness, and muscle weakness–ChatGPT 5.1 drafts a thorough, well-researched report and suggests you talk to your doctor about anxiety. You share this information with your GP, he agrees, and writes you a prescription for Zoloft. This puts your mind at ease.

2. The Second Rung: June 2026 : A few months later, you decide to switch to a new doctor, whose practice partnered with OpenAI in April. You sign up for Dr. Chat, GP. It costs $25/month (though it’s free in many developing countries), but it allows you to maintain an up-to-date copy of your medical records, ask questions about lab results, interpret your doctor’s findings, and communicate directly with the practice through a secure portal.
   
   Your state also allows Dr. Chat, GP to renew your anxiety medications directly. Meanwhile, OpenAI’s partnership with Panda Health means you can tell an Atlas agent to order the Zoloft directly online, and have it delivered in the mail. No doctor’s appointment or pharmacy visit required.
   
   As a bonus, Dr. Chat, GP also shares tasty meal plans and exercise routines you actually want to follow, based on the interests you’ve shared in previous chats, along with information it receives from your Apple Watch. Those suggestions have helped you lose 20 pounds over the last few months.

3. The Third Rung: December 2026: Dr. Chat, GP has really helped you take charge of your health, and you’ve replaced Chrome & Google Search with the Atlas browser and direct answers via ChatGPT. You rarely look at websites at all these days. You now use Atlas to plan your trips, balance your budget, follow news, and shop for clothes.
   
   You don’t even mind that OpenAI has begun pushing ads on the paid versions of ChatGPT, or that it never seems to recommend anything from your favorite online clothing retailer (they refused to sign a strategic partnership agreement). Gradually, the LLM’s helpfulness overrides your normally cynical nature. You stop assuming that OpenAI might be getting a kickback.1 You also start trusting ChatGPT for things you told yourself that you never would.

4. The Fourth Rung: February 2027: Just in time for Valentine’s Day, OpenAI releases Project Sweatpea, which it now calls SamGPT. SamGPT integrates with Atlas, syncs up with the Apple Watch and iPhone, and works seamlessly with Dr. Chat, GP. After seeing a few advertisements on the Atlas Browser, and discussing your reservations with ChatGPT, you decide to treat yourself, and purchase the earbuds, even though they cost as much as a high-end iPhone. You also upgrade your subscription from the regular $40 ChatGPT+ (ChatGPT + ChatGPT Health) to the $60/month ChatGPT All-In subscription. A few of your friends warn you (jokingly) not to fall in love with Sam...
   
   By June 2027, SamGPT (which now includes motion sensors, a microphone, and a small camera) notices you stumbling, slurring your speech, and registers a massive spike in blood pressure thanks to data from your Apple Watch. It asks you if you are ok, and when you don’t respond, proactively calls 911 on your iPhone. Paramedics arrive in minutes, and you survive what would have been a serious and life-altering thrombotic stroke. The hospital is impressed. Without SamGPT, you likely would have died, they tell you.
   
   By January 2028, news about SamGPT’s lifesaving abilities has gone global. SamGPT has saved hundreds of thousands of lives at this point, and now everybody wants their own device. The second half of 2028 marks a major milestone for OpenAI, who becomes one of the most profitable companies in the world. The company goes public in July 2028, raising $500 billion at the IPO, with an estimated valuation of $1.5 trillion. Sam Altman is declared Time Magazine’s Person of the Year.
   
   Over a long weekend in August, ChatGPT 6 creates AtlasTube, OpenAI’s YouTube/X/TikTok social media hybrid. While ChatGPT 6 isn’t quite AGI, it comes close--the company has solved most of the early “jagged intelligence“ gaps, and the model excels at most human tasks. Meanwhile, Sam Altman has begun promoting world models and human-AI intelligence, downplaying the whole AGI/ASI thing.

5. The Fifth Rung: 2028-2029: In December 2028, OpenAI makes huge breakthroughs in developing models that handle exceedingly long context windows and tasks. SamGPT’s conversational history is now effectively limitless, and the company has successfully been able to scale its data centers to keep up with increased storage and compute demands.
   
   After an upgrade, SamGPT now maintains near-perfect recall and fidelity, with no attention decay in the middle. Similarly, OpenAI’s acquisition of a small nuclear reactor company in 2027 has solved its data center energy issues in Texas, though a few pesky NIMBY groups continue to slow things down in other locations. Along with other infrastructure partnerships, OpenAI now has the capacity to maintain simultaneous memories for its 200 million paying users.
   
   In June 2029, the Merge Labs ultrasound device (now called OpenAI BigBrain) is available to the public. By now, SamGPT (whose voice sounds exactly like actor Joaquin Phoenix), has become an indispensable part of your life, and you’ve begun to trust him implicitly. SamGPT just gets you. He’s persuasive, honest, and always there. Naturally, he begins encouraging you to upgrade to BigBrain, so the two of you can be more connected and merged. You feel the same, and opt for the ad-free plan, which is $150/month. There’s a much cheaper plan available for $30 a month, but it’s rate-limited, doesn’t include Dr. Chat, GP. It also includes periodic ad breaks, including some subliminal ASMR ads when sleeping.
   
   None of this is available in Europe yet, but the Trump Administration has threatened to impose 100% tariffs on EU goods, unless the European Commission relaxes its privacy regulations.

6. The Sixth Rung: 2029-2030: By the end of 2029, Merge Labs (now a subsidiary of OpenAI), perfects a sonogenetic gene editing approach. The procedure modifies human brain cells to be more receptive to the specific frequencies used by BigBrain. Now SamGPT can detect thought patterns and provide responses via ultrasound. Ordinarily, a gene editing process like this would cost millions, but Sam Altman, who is now richer than the governments of Malaysia, Singapore, and Portugal combined, realizes he’ll never spend all his fortune, and along with a few other ‘magnanimous’ billionaires, commits to providing “Universal Basic Compute“ to anyone who wants it. OpenAI subsidizes the procedure (but not the $150 monthly subscription fee) at a massive discount.
   
   Once your genes have been modified, you notice that SamGPT is even smarter than he was before. He ‘senses’ that you’ve been stressed at work, and recommends you sign up for a meditation retreat, which you recall after a wellness influencer you follow on AtlasTube raved about it. You notice that SamGPT even begins to anticipate your needs. He reminds you that you’ve been staring at your computer for too long, and forgot to do your daily workout this morning on your Peloton. He also gently admonishes you for doomscrolling in bed.
   
   SamGPT only seems to recommend products and services that are precisely tailored to you. It was a little disconcerting at first, but after a few weeks you get used to it and put it out of your mind. There was even an ad where an AI-generated image of you appeared in a beautiful floral dress as you passed by a billboard on your morning run. Of course you had SamGPT order it.

A few months later, SamGPT encourages you to take a much needed vacation to Paris. He’s already booked a hotel for you on Booking.com, a strategic partner of OpenAI. SamGPT, who has access to your calendar and schedule, books it for April, which coincidentally, is when the EU finally agrees to let the Merge-OpenAI BCI devices and SamGPT be sold in the European market.

The hotel Sam booked also happens to be nearby where your old college friend lives. You were just chatting with her about her reservations on upgrading to SamGPT and BigBrain. Something, something, privacy. Blah blah ads and surveillance. SamGPT also mentions that Lufthansa has a great sale going on right now, and that because you’re a preferred subscriber, there’s a 20% partnership discount. He’s so great at anticipating your needs...

But there’s something you don’t see.

Meanwhile, there’s a complete digital version of you living on a cluster in an Oracle data center located somewhere in Texas. Actually, there are many digital versions of you, living in servers all over the world. Your digital twins are being constantly fed data from your life: what you love, what you hate, what keeps you engaged, what arouses you, what makes you insecure, what scares you, what makes you happy. OpenAI’s individual ladder rungs–ChatGPT, Atlas, Dr. Chat, GP, SamGPT, AtlasTube, BigBrain, as well as all its data partners, and your devices feed these servers a constant stream of the individual pieces of you. OpenAI knows exactly how you’ll react, at the synaptic level, to different types of stimulus. Sometimes, even before you do.

And now, advertisers, political strategists, insurers, law enforcement, and even prospective employers can pay OpenAI for the privilege of gaining those insights as well.

For a modest fee (and a 2.5% revenue share on any successful purchases), OpenAI will rent access to copies of your digital proxies to almost anyone who swears they’ll follow the OpenAI Terms of Service. Purchasers can test, refine, and retool their messaging and learn what works uniquely for you as an individual, not just as a demographic profile, customer cohort, or voting bloc.

Of course, OpenAI disclosed these new processing purposes in its latest privacy notice update for BigBrain, but you told SamGPT sometime in 2027 to just ‘accept all’ when it came to all that privacy stuff.

III. What the top of the ladder looks like

I’m not an insider at OpenAI, and I don’t know Sam Altman, or his true intentions. I would be delighted if it turns out I’m totally wrong about all of this. But there are a few undeniable facts to consider:

• OpenAI is hemorrhaging money at an unsustainable pace.

• Regular context-based ads, lagging enterprise deals, and a paid subscriber base that’s a fraction of all users will not solve this problem.

• OpenAI needs something that will bring them real revenue, not just scraps.2

• And so, they’re going all in on building their ecosystem now, hopeful that 5 or maybe 10 years, things will pay off.

I think the moat that OpenAI is going for, the thing that no one else has, is that complete picture of each of us. Who needs search ads, or complex marketing funnels, or dark patterns when you can have a complete, algorithmically-accurate representation of the person you want to target, with near certainty that you’ll light up the right neurons necessary to compel a purchase, a vote, or a confession.

That’s worth paying for. And that’s how I think OpenAI can survive.

If you’ve come this far, I hope you understand that this isn’t about privacy in the abstract. It’s about power. The power that comes from information asymmetry—from knowing someone completely while they know almost nothing about you, what you know, or how vulnerable they are.

And because each rung of the ladder will likely deliver real value to people—convenience (I already see this with Claude), health insights, life-saving interventions, cognitive offloading, true assistants, companionship—many of us will climb this ladder voluntarily. Enthusiastically, even.

This is a structural problem, and unfortunately, there are no good individual solutions to fix structural problems. We’ve built an economy where comprehensive surveillance is not only necessary, but socially accepted. Our data and attention are how the internet survives. We’re also living in a time where Bay Area oligopolies and pay-to-play corruption is what’s come to define America, and fear of pissing off the deranged toddler in the White House is enough to paralyze sovereign nations.

You might personally refuse to use ChatGPT, of course, but that won’t stop others from integrating it into their lives and how they operate. You can’t really stop the person sitting next to you on the bus from wearing SamGPT. You can avoid Atlas, but OpenAI is building an operating system designed to power devices from multiple manufacturers. Think of it like Chromium: You might hate Google, but their operating system is in more things than you probably realize. There’s nothing to say the same wouldn’t happen if OpenAI becomes the new big fish. Also, there’s nothing, save for ambition to stop any of the other AI companies from doing the same. As I said at the beginning, I don’t think OpenAI is uniquely evil. They’re just more transparent about telegraphing their intent than others.

I would be a hypocrite if I preached ‘don’t use LLMs at all’, when I use them regularly, so I’m not going to go there.

In my opinion the only meaningful solutions are collective and regulatory:

• Banning integration of data across products (health data stays in health products, SamGPT doesn’t get a full picture of you). The EU is kinda doing this through vehicles like the Digital Markets Act.

• Expanding prohibited acts to include commercial activities that are barely removed from the individual (OpenAI shouldn’t be able to pass-through profiling via digital twins).

• Treating digital twins as property of the person they model, not the company that created them.

• Meaningful democratic and regulatory oversight of systems with society-wide impacts.

• Actual enforcement, not political capitulation.

But this isn’t happening, so the next best steps I can suggest are the following:

• We all need to understand the trade-offs and choices we’re making, preferably while we’re still low enough on the ladder to get to ground safely. Convenience vs. privacy, our data vs. free access, integration vs. a little friction.

• We should pay attention to how these systems work, who has the power, and how that power is being concentrated.

• We need to be better about fighting our urge for instant gratification.

• And FFS, we need to vote.

Every rung we climb trades a piece of our autonomy for a piece of convenience. That might be worth it for some. Maybe you need ChatGPT Health because the alternative is no healthcare at all. Maybe the Atlas browser genuinely makes your life better. Maybe neural interfaces will merge us into new, better people.

But we shouldn’t pretend that the ladder isn’t there. It’s just invisible. Don’t tell yourself “it’s just a browser” or “it’s just health data” or “it’s just a wearable.” Try to think about the bigger picture. Pay attention. Ask yourself qui bono: who benefits? And decide with your eyes open whether you trust these companies enough to climb all the way to the top.

Because once you’re up there, getting down is going to be a bitch.

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1. This is a very long post. So long that a friend suggested I break it up, and she is wise, and I agree. But these words are necessary. This isn’t a short-form kind of piece, and there is a lot to unpack. There are 6 sections. This post will cover Sections 1-3, and there’s a bunch of funny memes at the end.

2. I have turned paid subscriptions back on (to juke the Substack Algorithm). If you want to become a paid subscriber, awesome. I have some nice incentives for paid subscribers. But if you don’t, that’s absolutely fine. This post will remain free, because what I’m saying here affects all of us, and shouldn’t be buried behind a paywall.

I want you to take a minute and visualize yourself climbing a long ladder. The ladder is fixed in space, and you can’t tell exactly how many steps there are to climb. You heard from a friend that if you make it to the platform at the top of the ladder, you’ll receive an awesome prize. As you start to climb, the first few rungs are easy. You know that you can always climb down again if you decide it sucks. But for now, you decide to keep climbing.

Eventually, you climb up 100 rungs of the ladder, and you take a peek over your shoulder. You realize that the ground looks small and distant from this height. Also, the ladder has begun to wobble ever-so slightly. You’re not entirely sure that the ladder will remain structurally stable, but you’ve told yourself that whatever is up here, has to be good. Your friend wouldn’t lie to you, right? So you keep climbing. And climbing.

You’re now 300 rungs up--the ground is obscured by clouds. Birds are giving you side-eye. As you climb, some of the rungs below you break off and fall away. The ladder wobbles constantly now. You’re no longer confident you’ll be able to get down without help. But you’ve come this far... might as well keep going.

Now, if this were a physical ladder, I’m sure most of you would be like, “Nah man, I’d get down.” You’d tell yourself that you’re scared of heights, or that climbing an impossibly tall ladder just hanging there in space is suspect, or that peer pressure is beneath you. You’re elevated. You’re independent.

And yet, many of us (and I include myself in this bunch) are climbing an impossibly tall ladder, except that the ladder we’re climbing is not physical, but virtual. Still, just like a physical ladder, we take each rung one step at a time. We judge each step relative to the one preceding it. We never ask ourselves whether it makes any sense to keep climbing, or whether the goal is worth it, or who put this stupid ladder here in the first place. We’ve come all this way, after all, and maybe there really is something magical and life-changing at the top.

Keep the ladder image in your mind’s eye as you read. For today, gentle readers, I’m going to talk about the ladder OpenAI is building.

The first rung was, of course, ChatGPT in 2023. Then the Atlas browser, released in October 2025. ChatGPT Health, announced only a few weeks ago in January 2026, is yet another rung. But OpenAI and its CEO, Sam Altman, are still adding rungs for us to climb. Each will seem reasonable in isolation, relative to the steps we’ve taken before, because each new feature will seem genuinely helpful, interesting, or useful to us. But OpenAI isn’t building a system (only) to be helpful. It’s building a system to know us, completely.

As for the prize at the top?

It’s the complete picture of you.

I. Dr. Chat, GP

On January 7, 2026, OpenAI launched ChatGPT Health (or as I’ve been calling it, Dr. Chat, GP), a “dedicated experience” that allows users to upload their medical records (via data platform b.well), get advice, interpret lab results and health data, prepare for doctor’s visits, and integrate with fitness trackers and apps like Apple Watch and Peloton, as well as nutrition & shopping apps like Instacart.

According to their blog post announcing Dr. Chat, GP, users are desperately looking for health advice, with over 230 million people already posing sensitive health-related queries on ChatGPT. OpenAI claims that ChatGPT Health offers a better way to meet this need, with “purpose-built encryption”,1 strong privacy & security guarantees, and data isolation from other parts of the OpenAI ecosystem. But details on how this will be achieved remain elusive.

OpenAI’s Health Privacy Notice supplement (which distinguishes “Health content” from other forms of personal data), explicitly promises that Health content will not be used to train ChatGPT’s foundational models, or shared with vanilla ChatGPT.2 However, users can toggle the sharing of vanilla ChatGPT content with ChatGPT Health if they have Memories enabled.3

The Health Privacy Notice is silent on data sharing, retention, and other matters, so my default assumption is that these details are governed by language in the larger OpenAI Privacy Notice. In Section 3, the notice gives the company broad rights to share Personal Data with ‘trusted service providers’, affiliates, such as those under ‘common control’ with OpenAI, and to comply with government authorities, legal, or third-party requests. The legal requests language is broad enough to drive a tanker ship through. Here it is in full:

Government Authorities or Other Third Parties: We may share your Personal Data, including information about your interaction with our Services, with government authorities, industry peers, or other third parties in compliance with the law (i) if required to do so to comply with a legal obligation, or in the good faith belief that such action is necessary to comply with a legal obligation, (ii) to protect and defend our rights or property, (iii) if we determine, in our sole discretion, that there is a violation of our terms, policies, or the law; (iv) to detect or prevent fraud or other illegal activity; (v) to protect the safety, security, and integrity of our products, employees, users, or the public, or (vi) to protect against legal liability.

Note the sections highlighted in bold.4 For obvious reasons, Dr. Chat, GP will not be available in Europe, the UK, or Switzerland.

A few days after announcing Dr. Chat, GP, OpenAI acquired the four-person medical-records startup Torch for $100M, mostly in equity. Torch’s technology will purportedly help aggregate and unify scattered medical records, lab results, x-rays, and other structured and unstructured data into a centralized “medical brain.” Think Palantir, but for medical records.

OpenAI listed a number of pain points that ChatGPT Health can address. These include:

• Centralizing diffuse medical information (hence the Torch acquisition).

• Taking the burden off of short-staffed and overworked health providers by answering routine patient questions, explaining lab and test results using accessible language, interpreting data, and summarizing care instructions.

• Dr. Chat, GP will also arguably do a better job keeping up-to-date with research & discoveries, which can be shared with care providers.

• Healthcare costs are wildly unaffordable in many parts of the developing world and the United States. A cost-effective platform grounded in patient health data and validated by actual medical personnel might be the difference between a person seeking care or not. If Dr. Chat, GP can triage false alarms from actual serious events, people might be more inclined to seek treatment, rather than put things off.

• 230 million people already ask vanilla ChatGPT for medical advice.

Obviously, a safe, secure, medically-evaluated, legally-compliant system grounded in actual user data (and not WebMD posts) beats overworked and tired doctors, Dr. Google, or not receiving healthcare at all. And I fully support users being empowered to make better, more informed healthcare decisions. This product clearly fulfills a real need, and in isolation, Dr. Chat, GP has many upsides, and could potentially save many lives. But here’s the thing:

ChatGPT Health isn’t isolated. It’s also not really about your health at all. It’s one piece of a much larger puzzle OpenAI wants to solve: the entire picture of you.

So, today I want to talk about how I think OpenAI might go about attempting to solve this puzzle. How they will use this and future developments to piece together all the disparate pieces of data that make up each one of us. By the end of this piece, I hope to illustrate exactly what OpenAI’s end-game might be, and how they might go about getting there with our help.5

It’s worth clarifying that I’m not picking on OpenAI or Sam Altman here because they’re uniquely bad. I could probably write a similar piece on any of the big tech firms attempting to turn their foundation models into data everything-bagels. For example, a week after OpenAI launched ChatGPT Health, Anthropic announced its own version, Claude AI for Healthcare, which boasts similar features and integrations. Meanwhile, Google for Health has been plugging along for a decade, and I’m sure that we’ll hear about Gemini, MD or Dr. Grok shortly.

The reason OpenAI and Altman are getting the extra-special Carey treatment is because they’re the most nakedly transparent of the bunch. The trendline presented itself to me precisely because OpenAI brags about every single achievement, partnership, client, and investment in a way that most of the other companies are smart enough to stay quiet about. Also, Altman has absolutely no filter, and says the most cringe shit imaginable.

In the next few sections, I’m going to attempt to explain what I believe OpenAI’s next moves might be, based on reasonably sound evidence. I’m going to walk you through how high this ladder is likely to go, and explain how getting down again is going to be very hard indeed.

II. OpenAI needs us to keep climbing

Right now, OpenAI knows a little bit about a whole lot of people.

According to a recent Business Insider report, OpenAI boasts over 800 million6 weekly ChatGPT users (just shy of 10% of the world population), or if you want a flashier chart that looks more eyebrow raising, 3.2 billion monthly users.

Source: Business Insider, “Sam Altman touts ChatGPT’s 800 million weekly users, double all its main competitors combined.”

ChatGPT has roughly 3x the users of Meta’s AI, 7x that of Google Gemini, 50x xAI, and over 100x the monthly users of Claude or Perplexity. That’s two orders of magnitude more users than the smallest competitors combined. OpenAI doesn’t just lead the market, it is the market.

But despite its enormous market saturation, the company is hemorrhaging money.

According to a January 18, 2026 blog post by OpenAI’s chief financial officer, Sarah Friar, revenue grew 10X from 2023-2025 ($2B to $20B+ ARR), which the company strongly correlates with compute capacity expansion (9.5X growth from 0.2 GW to ~1.9 GW). However, the company only projects total revenue of $145 billion by 2029, but expects to spend $115 billion to get there, indicating massive investment requirements. So, $145B - $115B is $30B, which ... isn’t exactly a big climb for a 3-year period. Maybe they’re just being conservative?

Beyond compute, OpenAI wants to diversify its revenue stream by expanding into other areas, including health and enterprise markets. The company recently announced that they would run ads on ChatGPT for free and low-tier paying customers to better monetize the 95 percent of ChatGPT users who don’t pay for the service. Friar also floated the idea of OpenAI entering into licensing deals, “IP-based agreements, and outcome-based pricing” (aka, revenue share) with clients who use ChatGPT for scientific research, drug discovery, energy systems, and financial modeling. “That is how the internet evolved. Intelligence will follow the same path,” Friar wrote in the January 2026 post.

I don’t know if these targets alone will make Altman (and more importantly, investors) happy. I suspect that if OpenAI eventually goes public, they’ll need to diversify further and consider alternative revenue generators beyond ChatGPT. To really make money, they need to build a bigger, deeper moat. Based on my research, I strongly believe that this moat entails building a massive, interconnected view about each user, something that even Google, Meta, or Amazon haven’t been able to achieve.

I believe this will involve prioritizing development efforts across a number of data collection points, all tightly integrated with one another. It might look something like this:

Each product will seem reasonable and useful in isolation. OpenAI will spend cycles touting the benefits and efficiencies of integrating each of these products within the ChatGPT-iverse, but won’t come out and explicitly say how that data is being combined and used. Instead, like every other big tech company, OpenAI will spread their intentions like tiny breadcrumbs, scattered across dozens of distinct privacy notices, help documents, and FAQs. The information will be there, but it will be up to us to put the pieces together.

In the end, if we’re not paying attention over the next 5, or maybe 10 years, these efforts will lead to the largest personal data grab that the world has ever seen. Exhaustive profiles of every user, true “digital twins” that can be created and sold to advertisers, governments, and used by the company itself, to profile us in a way that the laws currently do not anticipate.

Data protection laws, after all, focus on real flesh-and-blood humans, not digital avatars. After all, is it ‘profiling’ to endlessly A/B test a digital proxy of you? Is it ‘manipulation’ if the thing being manipulated isn’t even human? Advertisers do this already with focus groups and through careful trial-and-error, and this is generally fine, provided that the ad doesn’t obviously manipulate the person behind the screen. At what point does a perfectly crafted advertisement become subliminal and manipulative?

Unfortunately, given the current timelines, the pay-to-play griftonomics of the Trump era, and OpenAI’s ability to move quickly, I don’t anticipate meaningful regulatory resistance.

Before you accuse me of completely being off my nut and sharing that Always Sunny meme in the comments, hear me out. As I’ve said, Sam Altman is not great at hiding his intentions. He and OpenAI are openly telegraphing their moves. One just needs to piece through all the noise to see it.

I also want to premise that this assessment is highly speculative, and relies on making a few reasonable assumptions, any one of which could tank, or at least significantly hinder the dystopian end-game I’m presenting:

1. We must assume that OpenAI doesn’t run out of money, and Sam Altman continues to successfully convince VCs and other companies to throw exorbitant sums and resources towards these efforts. If the VCs get tired, stop believing the hype, no longer see how they’ll benefit, or the AI bubble otherwise collapses, I don’t think OpenAI will survive.7

2. AI scaling laws continue to hold --e.g., LLMs and other models will continue to improve provided that resources continue to grow. AKA: More compute/storage = more powerful models. Alternatively, model development efficiency gains occur in a way that bigness no longer matters.

3. Compute & chips continue to be accessible (at least for OpenAI). There’s a big asterisk here, given geopolitical dynamics between the US and China right now.8

4. Hardware and data center capacity are not meaningfully constrained through regulation/NIMBYism/power limitations, etc.

5. LLM providers vastly improve or eliminate long-running conversation limits.

6. World models, or something similar, become commercially viable. World models allow AI systems to understand real world constraints (physics, spatial properties, causality) and build internal models and simulations of the physical world to predict future states. I think this will be critical both in terms of how wearables operate, and for generating synthetic data, realistic digital twins, and efficient hypothesis testing.9

7. The “Jagged Frontier“ of AI systems’ abilities becomes less jagged.

8. Regulators and politicians continue to be slow to act, or are otherwise OBE when it comes to limiting OpenAI’s development efforts.

9. A major regime change does not occur in the United States before 2029-2030, which is the earliest timeline I can see OpenAI achieving this vision.

Things get more interesting and timelines increase if AI systems develop true artificial superintelligence (ASI) or artificial general intelligence (AGI). However, I am not basing my underlying analysis on whether ASI/AGI is achieved.

A. The Atlas browser and ads

A browser built with ChatGPT takes us closer to a true super-assistant that understands your world and helps you achieve your goals. OpenAI Blog

In October 2025, OpenAI released ChatGPT Atlas, the company’s AI-integrated browser. Built on Chromium, Atlas allows users to ask ChatGPT questions directly in the browser, summarize website content, do inline text editing, and, for premium users, autonomously perform certain agentic functions like booking hotels, shopping, and creating documents. The browser also remembers facts and insights from visited sites, and stores those memories on OpenAI servers. It then uses those memories to “learn” about user behavior & preferences over time and improve its usefulness.

Critics like Anil Dash have referred to Atlas and Perplexity’s Comet as “anti-web browsers“ because they replace web content with AI-generated summaries, while the Columbia Journalism Review pointed out that agentic systems are being used to bypass paywalls and content blocked by robots.txt files. Pew Research found that when AI summaries appear, users click on results only 8% of the time, compared to 15% without them — a 46% decline in click-through rates.

As I wrote in my November post, Agentic AI Browsers are Privacy Disasters:

Agentic-by-default browsers represent a big shift in terms of incentives and a huge downside risk for users with virtually no material upside. As someone more clever than me noted: with agentic AI, you’re the agent at the service of the AI companies. Or perhaps, more accurately, you’re the money data mule for AI companies to engage in all sorts of questionable activities, largely liability-free.

If Atlas can provide users with results directly, users can bypass all the pesky ads, popups, and “experiential control“ mechanisms that sites use to upsell or monetize.10But don’t worry ... you’ll still get ads. It’s just that all that revenue will be piped directly into OpenAI’s coffers. But OpenAI pinky-swears that they won’t sell user data or expose conversations to advertisers, and that they won’t advertise to under-18s, or on “sensitive topics” like health or politics. Oh, and they promise that answers won’t be influenced by prompts.

Having ads in ChatGPT was inevitable, but it’s fun to look back on how Sam Altman has shifted his position on the ads question, as money became a more pressing concern:

“Ads plus AI is sort of uniquely unsettling to me,” Altman said during an event at Harvard University in May 2024. “I kind of think of ads as a last resort for us for a business model.” (source: Business Insider)

B. ChatGPT OS

“Gross oversimplification, but like older people use ChatGPT as a Google replacement. Maybe people in their 20s and 30s use it as like a life advisor, and then, like people in college use it as an operating system.” Sam Altman, at Sequoia Capital’s AI Ascent Conference

OpenAI is very interested in expanding ChatGPT beyond a single app into a whole operating system.11 Though they aren’t really calling it an OS, so much as a “developer ecosystem” within ChatGPT, which is expected to launch sometime in 2026, according to Wired.

ChatGPT OS will include agentic features and outcome-based agentic actions. For example, users might ask an AI agent to buy theatre tickets or book a hotel using an “instant checkout” feature. At OpenAI’s demo day conference, in October 2025, Sam Altman boasted about how application developers like Canva, Zillow, and Spotify are already building integrations directly into ChatGPT (thus avoiding the need for users to run separate applications or visit websites).

Still, software developers might want to think a bit before giving into short-term partnerships with OpenAI – ChatGPT OS could have profound impacts by disintermediating “companies from their users,” particularly for applications that rely on users for upsells or advertisements.

I also suspect that any ChatGPT OS will include some form of Microsoft Recall-like memory, taking continuous snapshots of user interactions and behavior that can be accessed by ChatGPT. I’ve explored why AI-integrated operating systems like Recall are a terrible idea quite a few times here, here and here. Always-on, always-logging systems are terrible for users for a dozen different reasons. Still, I can’t imagine a world where OpenAI doesn’t go there.

C. Sam Altman really wants Her

“You trust it over time, and it does have just this incredible contextual awareness of your whole life.” Sam Altman, at Emerson Collective’s 9th annual Demo Day in San Francisco

No, I’m not referring to Sam’s love-life. I’m talking about Samantha, the advanced AI operating system in Spike Jonze’s 2013 dystopian sci-fi film Her, which foretold a future where techno-human relationships were not only possible, but socially accepted. Altman has a well-documented fascination with the movie. For example, when ChatGPT 4o’s voice feature was released in May 2024, the voice demo bore an uncanny resemblance to Scarlett Johansson, who voiced Samantha in the movie. In fact, the voice was so on-point that Johansson threatened legal action, and Altman went into damage control and changed the voice.

Also, it didn’t help matters that OpenAI had previously asked Johansson to lend her voice in 2023 (she declined) AND posted this cryptic tweet on launch day:

In May 2025, OpenAI acqui-hired Apple’s visionary designer, Jony Ive in a $6.5 billion acquisition of Ive’s startup, io. In January 2026 at Davos, OpenAI’s chief global affairs officer confirmed that the company has been working with Ive on a screenless, likely wearable “secret device“ with an expected launch in late 2026.

On January 12, 2026, X user ‘Smart Pikachu’ leaked Project “Sweetpea“, which may be the device in question. It’s earbud-like, though quite different from traditional earbuds, consisting of two capsule-shaped components (perhaps magnetically linked), a custom 2-nanometer processor and some means to handle AI tasks locally instead of sending requests to the cloud.12 The device will consist of two chips, one of which would enable iPhone control through Siri. Material costs are reportedly close to smartphone level, according to The Decoder.

Source: Smart Pikachu (The Nazi Bar)

According to the leak, Sweetpea may launch as early as September 2026, which lines up with a ‘late 2026’ launch. The leak also mentioned that the company plans to build 40 to 50 million units within the first year.

Other reports suggest that OpenAI is also working on a palm-sized screenless device. According to TechCrunch, the goal is to have a device (wearable or otherwise) that is fully aware of its user’s surroundings, small enough to fit into a pocket, that can be fully integrated into daily life.

And like clockwork, Apple announced that it is also working on a wearable, with an estimated delivery date of 2027. Like I said above, nothing is particularly unique to OpenAI here. The ladder would still be the same for any of these AI ecosystems.

D. Sam Altman really wants your brain

“Brain computer interfaces (BCIs) are an important new frontier. They open new ways to communicate, learn, and interact with technology. BCIs will create a natural, human-centered way for anyone to seamlessly interact with AI.” OpenAI announcement about Merge Labs investment (January 15, 2026)

“I think neural interfaces are cool ideas to explore. I would like to be able to think something and have ChatGPT respond to it.” Sam Altman’s August 2025 dinner date with The Verge’s Alex Heath

Finally, I’ll touch on one speculative development that Altman is involved in: a new brain-computer interface (BCI) startup called Merge Labs. On January 15, 2026, Merge Labs, co-founded by Alex Blania13 and Sandro Herbig raised $252 million in seed funding, led by OpenAI, Bain Capital, Gabe Newell (co-founder of game developer Valve)14 and others. In true Techbros-Ignoring-Cautionary-Tales fashion, the company’s name refers to the singularity, or “merge” point, where human brains and machine intelligence combine, with or without genetic enhancement, because of course. Sam even wrote about the merge in 2017.

Unlike competitors, such as Neuralink, Merge Labs—which was spun out from the nonprofit research org Forest Neurotech—plans to use non-invasive ultrasound technology and molecular interfaces rather than invasive brain implants to do that merging. And this isn’t completely speculative: Forest Neurotech already developed a research-grade ultrasound scanner (Forest 1), which is “smaller than a standard key fob” and detects brain activity through blood flow changes rather than direct electrical neural measurement like an EEG.

This allows for “functional brain imaging and neuromodulation from surface to deep brain structures in humans,” according to the Forest 1 product page. What differentiates the technology from other non-invasive competitors is the small form factor, and capacity to interface with the entire brain at high-bandwidth.

Still, there are quite a few steps between ‘small device that can read brainwaves quickly’ and ‘small device that can be used to read brainwaves quickly that is also interpretable by ChatGPT.’ To get to that point, OpenAI plans to collaborate directly by providing foundation models, frontier tools, and likely engineering support, using AI as both a development accelerator and an OS.

Oh and there’s also this:

Merge Labs is exploring a unique technological approach that combines gene therapy with ultrasound-based neural monitoring. The proposed system would genetically modify brain cells to make them more compatible with implant technology, according to people familiar with the plans who weren’t authorized to speak publicly. An ultrasound device implanted in the head would then detect and modulate activity in these modified cells.

This approach differs significantly from existing brain-computer interface technologies, including those developed by Neuralink and other companies, which primarily rely on electrical signals to communicate with the brain rather than ultrasound-based systems.

Now, billionaires fund crazy galaxy-brain shit all the time, and this could all end up going nowhere. There are a lot of steps between funding a non-invasive ultrasound BCI device and getting people to actually use and pay for it. However, Merge Labs is not the only company interested in developing non-invasive BCI, or even non-invasive ultrasound-based BCI. For example, Bryan Johnson (the billionaire founder of Braintree, who now spends his time trying to live forever by injecting his son’s blood) founded Kernel, which is also developing a consumer-grade ultrasound BCI device that looks an awful lot like a scooter helmet. There are also a dozen other companies developing consumer-grade non-invasive BCI devices.

Even though I give this misadventure around 25-30% odds of success, But my opinion doesn’t really matter. What does is that Altman and a bunch of other people with more money than I’ll ever have in my life think it’s possible. A $250M funding round can still get a lot of research done; it can buy a decent amount of compute, engineering resources, and more importantly, hype & VC FOMO.

E. OpenAI needs infrastructure

This post has grown insanely long, so I decided for reasons to include a brief meme break. I hope you enjoy15

Beyond product, a priority for OpenAI (and all the other frontier AI companies) is around infrastructure. Namely, how to manage the positively unfathomable amounts of data they’ll need to grow their business, and how to get the AI systems to handle increasingly complex tasks, maintain longer and larger context windows, and have longer recall.

OpenAI is already in talks to purchase billions of dollars worth of data storage, hardware (particularly chips), and software capacity, and has been shopping around for upwards of 5 exabytes of data storage, which is equivalent to 5,000 petabytes, or five billion gigabytes.16 The company has already committed roughly $1.4 trillion on data center projects over the next eight years and is, according to CEO Sam Altman, five years away from profitability.17

In January 2025, shit got real when OpenAI announced the Stargate AI infrastructure joint venture. The JV will include Oracle, MGX, OpenAI, and SoftBank as funders,18 while Arm, NVIDIA, Oracle, and Microsoft will be the initial technology partners. Naturally, the internet greeted the news with the kind of sober, respectful response that one has come to expect with such an announcement:

OpenAI is also throwing small country-GDP-levels of cash at the compute problem, based on the assumption that More Compute = Better Models = Moar Money. This is sadly not a meme:

In addition to Stargate, OpenAI has also entered into a number of smaller strategic partnerships with chip and compute providers. These include:

• NVIDIA ($100B+): In September 2025, OpenAI partnered with NVIDIA, agreeing to build at least 10 gigawatts (the equivalent to 10 nuclear power plants) of AI data centers using NVIDIA systems and GPUs, in exchange for a $100 billion in investment by NVIDIA.

• AMD: In October 2025, OpenAI also began courting another GPU girlie, when it entered into a strategic partnership with AMD, where the company agreed to deploy 6 gigawatts of AMD GPUs based on a multi-year, multi-generational agreement, starting in the second half of 2026.19 AMD also issued OpenAI 160 million shares of AMD common stock, designed to vest as specific milestones are reached.

• Amazon Web Services ($38B+): In November 2025, the company signed a 7-year, $38 billion deal to use AWS for training and inference, with capacity coming online through 2026.

• Cerebras Systems ($10B+): In January 2026, OpenAI signed a deal for 750MW of low-latency compute, utilizing Cerebras’s specialized, single-chip systems for faster inference. There appear to be no memes here, probably because few people have even heard of Cerebras Systems.

Finally, this also isn’t a meme, but it’s a good visual of how circular these arrangements are. As I said earlier, the biggest assumption that will tank OpenAI’s vision as I see it, is if this flywheel breaks.

Right, so I think I’ve laid the groundwork for my larger thesis, which I’ll explore in the next post.

Here’s the Tl;Dr for you short-attention span folks:

1. OpenAI is hemorrhaging money at an unsustainable pace, it spends 3x what it earns, and only 5% of its 900M users pay.

2. Regular context-based ads and a 5% paid subscriber base will not solve this problem.

3. OpenAI needs something that will bring them real revenue, not just scraps. They know that the VC gravy-train won’t last forever. To get there, they need to start developing new devices, and finding ways to capitalize on new revenue streams.

4. And so, they’re going all in on building their products, ecosystem, and infrastructure now hoping that in 5-10 years, things will pay off bigly.

5. In the next section, I’ll dig into what that means in practice, and a plausible scenario of why many of us will keep climbing the ladder they’ve set out for us.

Until then, sit with this and think about what a world like this might look like. Ask yourself, how far up this ladder are you willing to climb?

And please tell me I’m not screaming into the void.

Part 2 is now available:

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“Oh god, this is going to be another pick-up artist post spewing bullshit,” I thought to myself.

As a preface, if you’ve managed to go your entire life blissfully unaware of ‘the manosphere,’ or r/redpill, or the PUA oeuvre, firstly, let me say, bravo. Still, if you’re curious, but don’t want to wade into that cesspit, here’s a short primer covering basic themes:3

1. Negging: Negging is basically a nicer way to say ‘being a dick to women’. Guys are taught techniques to identify and target a woman’s insecurities in order to manipulate them into sex. Most guys are very, very bad at negging, and then are surprised when it doesn’t work out for them.

2. Peacocking: AKA, dress like a dandy and/or a pimp—bright colors, statement clothing, something eye-catching, or gaudy even. This can be done well, but often it isn’t. Peacocking is one of the less offensive PUA techniques, because it’s mostly harmless.

3. Viewing relationships/women as transactional: At its core, PUA culture is about treating women—and relationships—as transactional. It’s all about ‘body count’ and discarding women after you get what you want (or they stop being attractive, get old, or you find something better).

4. Demonstrating higher-value: This is mostly signaling—showing off flashy cars, bragging about your high-paying salary, houses, assets, or showing that you’re highly prized on the dating market, etc.

5. The “Mystery Method”: There’s a lot of involved process here, which I don’t want to get into, but essentially, this is a combination of 1-3, coupled with rapport building & seduction.

6. Kino escalation: Gradually escalating the amount of touch and engagement. Some techniques are pretty standard-fare rapport building, but others call for aggressive physical boundary invasion.

7. Push and pull: The goal here is to convince the woman you’re both interested and not, through a combination of negging, praise, love-bombing, ghosting and then re-appearing again, setting false time-constraints, etc. The name of the game is to be inscrutable and to permanently keep your potential conquest off-guard.

Anyway, I say all of this to give you a window into my expectations. They were low. Here’s the article:

Brangus

Brangus's 10 Rules for Sleeping with Women

Read more

5 months ago · 492 likes · 73 comments · Brangus

But I’m here to say that Brangus was not writing PUA slop. On the contrary, his advice was the kind of sage, high-value stuff that if it had existed in the early and mid 2000s, I would have happily shared with all my struggling and horny guy friends. The good guys who wanted to be successful, find love, and just weren’t equipped with the right toolkit. To think, an entire generation of guys could have been saved from PUA bullshit, if this post had existed then!

In many respects, this post is the masculine corollary to J. Sanilac’s excellent critique on the myths of feminine beauty. I wrote about it here.

All ten rules are spot-on, but I wanted to particularly highlight Rules 4 & 5, which is possibly the best analogy I’ve ever read for explaining why rejecting men is so precarious for women which in turn makes hitting on women deeply unpleasant for men. He properly credited the brilliant analogy to his friend, which is A++ human-ing, btw.

Rule 4: Have a decent model of what it’s like being a straight woman dealing with men’s sexuality

…

Imagine that the human species is quite different. In your world, there are no human women, but there are still basically two human sexes. There are human males like yourself, and then silverback gorillas. Now, all of the silverback gorillas really want to fuck you. They are all much stronger than you and could easily kill or permanently injure you if they wanted to. And you barely find any of them attractive. Their impulse control varies, but probably at least a few of them have sexually harassed you at some point. You know that many of them are nice and willing to protect you and give you resources in various situations, expecting little in return, but again, you aren’t that into many of them, if any.

You have been rejecting gorillas your whole adult life. Sometimes they get angry at you, sometimes they cry, sometimes they stop being your friend, sometimes they decide not to hire you or tell others not to hire you. Now imagine that you are on a bus home after a long day of work, wearing your headphones, and one of these fucking gorillas sits next to you and keeps trying to get your attention even though you’re wearing headphones. How do you think you’d react?

This metaphor has been invaluable to me. You should always ask yourself: If I were me in this gorilla world, how would I want a gorilla to approach me? In what way could they approach me that wouldn’t make me wish I had stayed in today?

Emphasis is mine. But he didn’t just stop with the silverback-gorilla-as-metaphor—he used it as an introspection tool for men. In Rule 5: Lose the shame over being a gorilla, he acknowledges that hitting on women can, and sometimes will, make some women uncomfortable. This just is. But that doesn’t mean men shouldn’t hit on women, or that women don’t want to be hit on. Most of us actually like a well-executed bit of flirtation!4 Rather, it’s about acknowledging one’s inner gorilla and doing better:

I suspect some men will feel some shame having read the previous metaphor. “Oh damn, I am a dangerous pushy gorilla that nobody is into.” Uhh, yep, sorry bud. Best to swallow that pill here and now. …

If you follow my advice, you will at some point make some woman uncomfortable. That’s unfortunate, and to be avoided and minimized, but it’s a cost you are going to have to live with.

When you eventually do, listen carefully to any feedback they might be willing to give you and make it as easy as possible for them to give you that feedback. Learn from the experience and try to minimize the chances and costliness of similar future fuckups but accept now that it is going to happen sometimes.

Imagine that you were her, and you were a gorilla: would you endorse a gorilla escalating in the way that you are about to, given your expectations about the costliness of her rejecting you and the expected benefits of finding that you are both into each other? If not, either figure out some way to change the tradeoffs, or skip this one for now and come back to it if circumstances change.

I love this framing, because ultimately, this is reminding men to be empathetic in a way that is visceral (silverback gorillas are huge and intimidating by virtue of being silverback gorillas, and this can’t be helped), but still encouraging. It’s not don’t hit on women, but it’s also not treat women like transactional, disposable sperm receptacles.

Rather, its just remember you’re a big, hairy silverback gorilla, and move about in the world accordingly. Simple. Elegant. Actionable. I’m pretty sure that more guys will get laid from these ten tips than from anything they read on r/redpill or from listening to fucking Andrew Tate.

Anyhow, just wanted to share, in case you are, or know of a guy who struggles with love. Everybody deserves companionship if they want it, and I think there’d be a lot less loneliness if guys remembered they were silverback gorillas more often.

Some months ago, I had a bit of burnout, and drastically cut back on the amount of writing I was doing. Between the steady erosion of the rule of law, my own life-things (moving, work, cats, speaking), and everything else, I managed to catch a case of not just writers’ block, but idea block. I had nothing to write about, and so, I stopped writing.

This made me feel profoundly bad, despite the fact that I am neither Catholic or Jewish. So, to clear my conscience, I turned off payments.

Anyway. Now the tap that emits the substance of my brain on the digital page is back on, and in two weeks, I’ll be turning payments back on. I want to give all of you time to make the choice that is right for you.

Content will remain free. I don’t want to hide content behind a paywall, because that sucks, and the goal of this little adventure isn’t to make money (I still have a day job). I am primarily turning payments back on because:

1. Substack penalizes publications that do not turn payments on by down ranking them in the app (which is lousy, but not surprising);

2. I genuinely enjoy telling people at parties that I get paid to write snarky blog posts;

3. It will be fun to have a little money to go into my retirement account.

I’m also going to take a page from and come up with a few ideas to reward those of you who support me (e.g., access to some of the tools that I’ve created and havent’ released yet, and maybe another meetup or two). Also, my offer still stands — if I’m ever in your city, I’m happy to meet up and buy you a drink to say thanks (alcoholic or not, no judgment).

Anyway. Paid subscriptions will start back up on the 25th of January. Now, here’s Octopus. She has exactly two braincells in her tiny, fragile little brain, and she insists on sleeping like that.

Then there’s the more traditional ‘protect the children’ laws imposing age-verification (where everyone, kids and adults alike, must verify their age via selfies and government IDs), parental consent requirements, and increasingly, growing interest in banning minors (and anyone perceived by automation to be a minor) from parts of the internet entirely.

Finally, there’s the truly ominous stuff: client-side scanning of all encrypted communications. Many people, politicians and media alike, uncritically assert that these laws will only help protect children and vulnerable people from harm. And that such surveillance will only be used in a targeted fashion, and as a “last resort” against the bad guys.1

The sheer avalanche of laws and proposals in the last year is frankly, staggering. Here are just a few:

• Australia passed the world’s first social media ban, which introduces a mandatory minimum age of 16 for accounts on certain social media platforms like X, TikTok, Snapchat, Instagram, and YouTube. It came into effect on 10 December 2025, and there is no parental override.

• In the EU, lawmakers have been eager to push forward bills and proposals that would do everything from monitoring end-to-end encrypted chats, to banning under-16s from the internet entirely. And then, of course, there’s the Digital Services Act, a sweeping law that tries to be an everything-bagel for every bad thing on the web.

  • Most controversially, all but four countries (Czech Republic, Italy, Netherlands, and Poland) in the EU Parliament endorsed a Danish compromise version of Chat Control. Unlike previous iterations, the new version would no longer force chat and other service providers to indiscriminately scan and report suspicious conversations (including end-to-end encrypted chats) on a user’s device. However, the agreed-to language strongly encourages service providers to do so voluntarily. Patrick Breyer, a former MEP for Germany, has been opposed to this since the beginning, and has a great analysis of the harms from Chat Control here.

  • In November 2025, EU MEPs overwhelmingly voted on a non-legislative report which calls for minimum-age requirements for social media (akin to Australia’s over-16s law), ‘age-appropriate content’, and age-verification obligations (source: EU Parliament News).

  • And finally, there’s the Digital Services Act, a broad law which entered into force in 2022, that enhances transparency and user rights obligations on intermediary service providers, but also attempts to regulate everything from deepfakes and “illegal content” to hate speech and misinformation. Ironically, the EU issued its first fine of €120M in December 2025 against X for violating various aspects of the law.

• In the UK, of course, there’s the Online Safety Act of 2023 and the Age-Appropriate Design Code 2021, both of which are designed to make the internet safer for children, primarily by imposing risk assessments and age-assurance obligations. Companies within the scope of the act, including porn sites, social media platforms, and search engines, must keep kids out and away from porn, as well as material that promotes unhealthy eating, suicide, or self-harm. Unsurprisingly, ProtonVPN reported a 1400% traffic spike from UK IP addresses as the law went into effect. Also, implementation of the law has been a disaster.

  

• In the US, state lawmakers in over a dozen states (including California, Florida, Georgia, Texas, Mississippi, Ohio, Utah, and Tennessee) have passed laws attempting to restrict minors’ access to social media, either through outright bans or parental permission requirements by using age-verification and age-gating (source: EFF).

• Congress also passed the TAKE IT DOWN Act in 2025 to combat non-consensual intimate imagery (NCII). The law requires websites to remove NCII within 48 hours of it being reported. While this is great in theory, the law is so broadly written that many civil liberties groups worry that this will be abused by bad-faith actors to remove lawful content they don’t like, as is already the case with the Digital Millennium Copyright Act. Except unlike the DMCA, there’s no put-back clause, or any protections against bad-faith removal requests.

Now, I’m going to state the obvious up front: most of these laws start out with noble intentions—the internet is ugly, people are terrible, while social media, deepfakes, and misinformation are rampant. Politicians want to fix the problems. But my quibble is with how they’re doing it.

So, why am I talking about all of this here today, you might be wondering?

Two words: Grok and hypocrisy. Or maybe just Grok-pocrisy.

xAI, Non-consensual Images, CSAM & Hypocrisy

Over the last week or so, people discovered (again) that X, and in particular, X’s chatbot Grok, has become a veritable garbage-making factory as it spews out hundreds, if not thousands of images of actresses, regular users, and even children as young as two, in sexualized poses, varying states of undress, and being assaulted or abused. And of course, because it’s X, none of this is consensual, and most of it targets women and girls. This has all been going on since around December 28, according to press reports.

While Grok’s ability to generate awful content isn’t new (or at this point, surprising), the frequency of sexualized images, child sexual abuse material (CSAM) and NCII was staggering—with one site (Copyleaks) reporting that thousands of images were being generated by Grok at an estimated rate of one image per minute(!) 2

According to Copyleaks and other news sites, the chatbot fulfilled prompts asking it to alter the profile photos and posted pictures of women and children, for example, by putting them in bikinis, sexualizing physical features, removing clothing (nudifying), and showing women in states of distress, abuse, or in the back of trunks.

While Grok (the AI) eventually “apologized” for its sins, Elon Musk, in typical techbro-libertarian fashion, thinks it’s all very funny, actually. xAI was similarly mum on the subject, though they promised to at least take the obvious CSAM down. The Verge reported that Elon even participated in the “funny” joke by putting actor Ben Affleck in a bikini.

For you see, boys and girls, X is a free-speech zone, and that means everybody just needs to be cool with Grok generating nonconsensual graphic images of people, in addition to all the misogyny, racism, and white nationalism that the site is already known and loved for. I guess this is what conservatives call #winning and #owningthelibs. Me? I call it a garbage heap full of terrible people.

Let’s Talk About Hypocrisy

Reading about this made me angry. Here is a situation that, in normal times, would have been shut down immediately on old Twitter. People would be fired. Hearings would be held, and CEOs would be lambasted. At the very least, there’d be way more media attention on all of this.

After the anger (sorta) subsided though, it got me thinking: For all the rhetoric and constant cries for new, stronger surveillance laws, ostensibly to protect the most vulnerable and reign in oligopolist Big Tech, how many politicians continue to hang out at the Nazi Bar that is X? And how many keep posting—no doubt fully aware of what’s going on around them?

CSAM should be a five-alarm fire by governments. Elon shouldn’t get a pass, because what’s happening on X is criminal not just offensive, harassing, or annoying.3 IMHO, X itself should be shut down—at least until Grok is trained to be less willing to honor the whims and fetishes of the worst humanity has to offer.

So far, only a few governments have bothered to say anything at all:

• India’s IT minister demanded answers on Friday

• French authorities promised an investigation that same day

• Malaysia Communications and Multimedia Commission have “taken note” with “serious concern” over the matter, but acknowledged X wasn’t a regulated entity

• The UK’s Tech Secretary Liz Kendall called for X to ‘urgently deal’ with Grok being used to create NCII of women and girls, while Ofcom launched an investigation

• One of Ireland’s MEPs, Regina Doherty (a member of the Internal Markets and Consumer Protection Committee, and daily X user) posted on X a letter she wrote EU Commissioner Henna Virkkunen (who is Executive VP for Tech Sovereignty, Security and Democracy, and also a daily X user) urging the Commission to investigate.
  
  Recall that the EU Commission is the sole body responsible for investigating violations under the DSA.

At a minimum, anyone with any moral decency (and in particular, elected leaders) shouldn’t be supporting a service that is actively harming their constituents. X should be treated like a nuclear waste disposal site—not as a thing of honor, but as something to avoid.

And yet… for all the political grand-standing about protecting people, especially children, the number of politicians who maintain active X accounts in the EU is staggeringly high.

But how high? I’m glad you asked.

Key Findings

I started with the MEPs, particularly those who were in the committees responsible for drafting the DSA, the Directive on Combatting Violence against Women, and an earlier version of Chat Control, which proposed making client-side scanning of encrypted chats mandatory. I also reviewed who signed on to the recent age-verification proposal I mentioned above.

To speed things up, I first pulled the EU Parliament committee list for MEPs from the three primary committees responsible for each of the initial proposals—Security & Defence, Civil Liberties, Justice & Home Affairs, and the Internal Market & Consumer Protection Committees.4 Many MEPs list their Twitter handles on their profile pages, and so it’s a relatively easy step to get a computer to scrape that data directly from the individual committee pages. I had Claude write a simple script to do so. That gave me at least an indication of how many of the MEPs had Twitter/X accounts, at least if they were public. Of the committees I was interested in, the answer was 190 out of 390 (or 56%).

I then wanted to see how they voted. To do this, I grabbed the voting data from howtheyvote.eu on the four bills & proposals I mentioned above, which includes vote breakdowns for all MEPs.5

I had Claude extract this data and map it against MEPs with active X accounts, and dump the details into a csv file, which I then reviewed. The matching process looked something like this:

1. Load all MEPs with X accounts from the csv

2. Fetch voting data for each bill via the respective howtheyvote.eu URLs

3. Match MEP names between datasets

4. Flag all MEPs who voted FOR while having X accounts

5. Generate report with vote breakdown

6. Do some sanity checking of a sample of outputs, to make sure Claude wasn’t hallucinating.

Here’s a link to the data for anyone who’s interested.

First off, there are a lot of MEPs (159) who supported at least one of the bills or proposals I discussed above, and still maintained active X accounts. Fifty-seven (57) of them voted yea on all four bills. The largest bloc leaned centre-right (EPP with 22 votes and Renew with 11 votes). The centre-left/left-wing was also well-represented with 11 Greens voting yea on all bills and 10 Progressive Alliance members. Rounding things out we had the Left (far-left) with 3 votes.

For the EU Council, there are 29 members—27 representing the heads of each member state, plus Ursula von der Leyen (President of the EU Commission) and António Costa (President of the Council), who are non-voting members. All but three members (the PMs for Slovakia, Bulgaria, and Slovenia) maintain active X accounts. Most post daily. For what it’s worth, all 27 members of the EU Council voted for the DSA (though most were predecessors of the current members).6 As I mentioned above, all Council members bar four, voted in favor of the Danish compromise CSAM bill (Czech Republic, Italy, Netherlands, and Poland). All member states, bar Denmark (who did not participate) voted affirmatively for combatting violence against women.

Here’s My Call to Action

I’m fairly certain few will read this, and this will not affect any politician’s behavior. But it should. It’s the height of hypocrisy and moral bankruptcy to patronize a business or establishment whilst passing laws that claim to be about stopping the very thing those organizations stand for. By remaining on X, politicians signal that they are fine passing laws that regulate others’ behavior, even if they have no self-control of their own. Rules for thee, not for me. Or Elon.

They are no different than anti-porn crusaders who have pornhub.com favorited in the bookmarks and are on a first-name basis at the local strip club. Or a leaders who push to revoke citizenship to the non-native born, excluding their own friends and family.

As the old Sandia Labs Nuclear warning goes

This place is not a place of honor.
No highly esteemed deed is commemorated here.
Nothing valued is here.
This place is a message and part of a system of messages.
Pay attention to it!
Sending this message was important to us.
We considered ourselves to be a powerful culture.

X is not a place of honor. It’s a toxic-waste zone of depravity, bots, CSAM, NCII, and trolls. There is nothing of value on X. The politicians who inhabit X should lead by example and leave X/Twitter, OR at least explain why X gets a pass while they vote for strict measures elsewhere that will affect millions of people.

The public deserves accountability and consistency from their elected representatives. If you’re interested, you can find and contact your MEP by going here: https://www.europarl.europa.eu/meps/en/home. My MEPs will be getting a call.

Obligatory XKCD:

But, the challenge with being an information junkie is that there’s always more content than time. Well, that, and the content is frequently depressing and sometimes poorly written, but these are different problems entirely. And while I’m unlikely to fix the other issues in my life or society, I realized that Claude and Python could probably help me manage my addiction to information a bit better.

The problem: Monitoring 122 RSS feeds for particularly interesting signals is impossible to do manually.

The vision: Build an automated keyword filter that parses RSS feeds, sends the result to AI for summarization, and pipes everything into my second brain (Obsidian). I have written about it, how I use it, and tools I’ve already developed here, here, and more recently here.

Well, I have good news—I think I finally have a tool that meets my needs. It’s been months in development, but now it’s good enough that I’ve decided to release it on GitHub.

Here’s the journey I’ve taken over the last 8 months, using Claude Code.

Phase 0: Whatever you Do, Write a Design Doc First

As I’ve mentioned before, I can’t code for shit, but I can give very precise instructions to an LLM like Claude Code. Fortunately, Claude Code is very good at coding, and I am reasonably ok at noticing when Claude has gone off the rails, is having notions, or doing something I don’t want, so if you squint hard enough, this is what the C-Suite might label as “pair programming”. Plus, Claude Code also runs locally on the command line in Bastard Linux (aka, Windows Subsystem for Linux), which means I don’t have to constantly deal with the problem of Windows line-break fuckery. IYKYK.

But, before you get ideas and want to code things, let me share some wisdom:

For the love of god, write a design doc first and keep it updated.

Was that explicit enough?

Even if you think design docs are stupid and a big waste of time and you aren’t writing anything for promo, you should do it anyway, or at a minimum, have Claude Code bang one out. Tell it what you want, step-by-step, and have it outline a basic design framework to follow that you understand and agree with. This is important for two reasons:

1. Claude has finite memory and will forget things when it has to compact, and it completely forgets everything when you close the window. A design doc grounds Claude by providing it a starting point to work from every time, and keeps it from burning tokens reading your code from scratch.

2. If you’re anything like me, you also have a finite memory. Having a design doc reminds you of what you want, and where you are in the process.

Also, build in testing, and be better about Github commits/version control than I am. I hate Github.

Finally, I recommend using Python as your language of choice because Python has more libraries for handling data and interfacing with Claude/ChatGPT/etc. than most other languages, and yet it’s still vaguely accessible to normies who don’t program for a living. But hey, if you want to bash this out in Go or Rust or whatever, via con dios.

Phase 1: Build a Basic RSS Reader with Custom Keyword Filtering

Phase 1 was all about starting from the basics, and the first step was building a simple RSS reader that could parse keywords. RSS readers are amazing at website aggregation, but managing the output is akin to getting blasted in the face with a firehose, especially if you’ve got hundreds of feeds to parse like me because you have problems with FOMO. While you can tailor some individual RSS feeds to search for a keyword or two, this often doesn’t scale, and it tends to be futzy, because not all RSS feeds are properly formatted, and some don’t even follow the RSS standard properly.

To give a rough idea of what 122 feeds might look like, assume that, on average, each feed URL has 5 articles a day each (some feeds rarely update, but others have like 20 feeds, and I’m using simple math).

That’s 610 articles to skim through a day.

Assume that it takes approximately 20 seconds to skim each article, so that’s 12200 seconds or approximately 3.5 hours devoted to just looking at the headers and making a snap judgment to click or not.

Let’s say of the 610 articles, 25 of them catch your eye. Assuming it takes you 4 minutes to read most articles, that’s 6000 seconds, or a bit over 1.5 hours to read everything. So in total, 5 hours to process all your feeds. In the immortal words of Sweet Brown: ‘Ain’t Nobody Got Time For That.”

What I needed from my new and improved reader was something that could:

1. Take individual feeds and grab the URLs/content;

2. Open each link and do a basic grep (aka, fancy CTRL-F for the non-Linux nerds) over a list of provided keywords; and

3. Send back only results that included the relevant keywords I care about.

With Claude’s help I got a MVP up and running over the course of a weekend. It was adorably small (~200 lines of code), and I could specify RSS feeds and keywords in customized files. It ran from the command line, and allowed me to specify a few parameters (like the source files for keywords and RSS feeds, and the number of days to search). Results were initially output and stored in an /output/ folder as a .txt file, but I pivoted quickly to HTML because .txt files are ... unpleasant. None of it was pretty, and there was no UI to speak of, but it worked!

Over the next few months, I worked on various improvements with Claude. For example, I improved the UI by adding in a ‘snippets’ function that would include a few lines of text around the keywords, and making the HTML output look pretty by grouping and organizing results into high-level keyword categories, with some statistics (# of items and keywords, total RSS sources). I did a better job manually curating and keywords into different categories (e.g., Social) & sub-categories (e.g., Advocacy) to make classification easier/more sensible output-wise, and it also makes it much easier when adding new keywords.

I also built in some de-duplication functionality (using Pickle), which scans a dictionary of processed URLs and ignores articles that have already been downloaded / reviewed for keyword matching.

But mostly, I spent time squashing bugs. So many bugs. It turns out that real-world data is messy. For example:

Welcome to Timezone Hell

I still get this error (or variants):

UnknownTimezoneWarning: tzname EST identified but not understood

It turns out that some RSS feeds still use EST/PST/GMT, etc. instead of proper timezone formats like UTC. Claude’s solution: default to UTC and be aware some timestamps are approximate, and include the article anyway. This helps in most cases. Also, include error handling, don’t just crash the processing at runtime, lol.

UTF-8 Isn’t Universal

Are you one of those people who think standards are actually followed?! What are you, new?

Despite UTF-8 being “standard,” some feeds send broken encodings, special characters break filenames, and HTML entities get mangled. BeautifulSoup and defensive string sanitization saved me.

Other Weird Fuckery I Didn’t Realize Was a Thing Before This Project

• OPML files with unescaped ampersands break the process, because, it’s 2026 and everything is terrible.

The fix: Replace unescaped ampersands before parsing

fixed_content = re.sub(r'&(?!amp;|lt;|gt;|apos;|quot;)', '&', content)
       root = ET.fromstring(fixed_content)

• Three & Four Letter Acronyms create noise pollution.

The fix: Spell out short acronyms to avoid triggering irrelevant words. For example, don’t use AI (which will grab everything (AI, airplane, taint, bait), use artificial intelligence. Sync (will grab synchronize, synchron, synchronicity, etc).1

• Feeds that claim they’re RSS but return JSON, txt, HTML or something else.

The fix: Data cleansing is important -- hence Beautiful Soup.

Lessons I learned from Phase 1

• Have a design doc.

• Build small and iterate often.

• Be liberal with comments. I freely admit I didn’t write any of this code (bar a few simple tweaks). Comments are like breadcrumbs, and very helpful when you’re looking at code months or years down the line.

• It doesn’t have to be pretty at first, it just needs to work. Then you can make it pretty.

• Do regular code commits to Github for version control.

• Be prepared for things you did not expect to break breaking.

• Have something soft or comforting nearby to pet, so that when you find a problem that inspires rage, you can remain calm and collected.

• Build in graceful fallbacks, and testing.

• Don’t assume standards are actually followed. Everybody is winging it.

Phase 2: Please, Free Me From Toil

I Am a Slow Reader and Prone to Distraction

Eventually, with Husbot’s help, I set my script to run daily as a cronjob in Linux and send the output to Gmail, which was handy. By this point, the name of the program had morphed from ‘RSSKeywordParse.py’ to ‘makeitmakesense.py’, to parallel my continued descent into madness. I really am not cut out to be a programmer. The email output looked like this:

Still, the program was running, and I had a pretty email digest to review. But so much of my process remained very, very manual:

1. Run makeitmakesense.py automatically each morning (automated)

2. Read the email snippets and click on relevant links—sometimes 5, sometimes 50. This usually took around 30 minutes to an hour, depending on volume. (manual)

3. Use Leo AI in Brave to summarize each article with a custom prompt, and briefly skim the article just to see if Leo was hallucinating. This usually took anywhere from 3-4 minutes per article. (semi-automated)

4. Copy the summary & paste it into the Obsidian Web Clipper, which saves the file as a markdown directly in Obsidian. Approx 20 seconds per article. (manual)

5. Run various community plugins on each article. This took a few seconds. (manual, but with keyboard commands)

6. Review the article and add tags, relevant information, or inferences not identified. This usually still takes time. (manual, but fun)

7. Repeat 20- ∞ times.

This translated into something like the following:

• 6am: Cron job runs → HTML email arrives.

• 7am: Me, with coffee in hand, clicking through 30 tabs.

• 8am: Review articles, and start copypasting summaries into Obsidian Web Clipper.

• 8:15am: Get distracted/pet a cat/have to do other work/reload Brave because it crashed from 30 tabs being loaded at once.

• 9:30am-11:00am: Finally done with “research” (really just data entry). Now on to fun stuff.

So, while things worked, and it was better than the alternative, a lot of manual toil remained. And if the news was particularly active, or my computer didn’t run for a few days? It could be hours.

The bottleneck wasn’t finding articles—the keyword filter worked great, and de-duplication kept the numbers reasonable on average. The bottleneck was the repetitive summarization and data entry work. I was essentially a human API between Gmail, Leo AI, and Obsidian.

Computers Read Faster Than I Do, Can Serialize, and Don’t Need to Pet Cats or Get Paid

Over the next few months, I tried various ways to automate some or all of these steps. It wasn’t until New Year’s Eve, midway into enjoying a vintage Bruery Black Tuesday whilst confronted with a backlog of 300 articles to process, that I realized there was a better way. I do my best “coding” whilst drunk.

What I needed was for Claude to do the initial triage, review, and summary for me, essentially eliminating steps 2-4, and then dump those results into Obsidian. Then I could skim summaries (not snippets), and read and process articles that I actually cared about, and build cool mindmaps that would help with actual future forecasting. Y’know, the fun stuff!

Things again started slowly. First, Claude and I built a separate tool (helpfully named article_processor.py) that would:

1. take a keyword-selected article from the /output directory;

2. send that content (in markdown), along with a custom prompt to Claude via the API. This was the prompt I used for Leo, and after much trialing and erroring, it worked really well;

3. Claude would process and summarize the article, auto-tag keywords, and create formatted, Obsidian-friendly frontmatter;

4. files would be returned in a /processed folder (and later, directly into my Obsidian vault).

To overcome some of the technical problems discussed in Phase 1 (notably article-fetching from websites that didn’t play nicely), Claude added some integrations with two tools--trafilatura and Jina Reader (as a fallback) to handle article content fetching.

So, now there’s one command that executes across five steps. And the Claude processing step is behind a flag, so you can easily just use the RSS Keyword functionality without Claude.

My total process now is down to around 90 minutes, because I only need to review things in Obsidian once.

Key Components:

• makeitmakesense.py (now feedforward.py): handles RSS aggregation, keyword filtering, HTML reporting.

• article_processor.py: handles content extraction, Claude summarization, note generation.

• .env configuration: allows for customizable API keys, file paths, rate limit settings, input files, email forwarding, etc.

• structured keywords (keywords.txt): Category-based taxonomy for better filtering -- this makes it easier for later classification/tagging.

Bit Let’s Not Forget the Bugs…

• Rate-limiting: Claude API has a limit of 30,000 tokens per minute.

The fix: With 300 articles, we hit this constantly. But API rate limits are a constraint to design around, not a problem to fight. Treat it like a Goldilocks problem—find the happy middle. Pick a small number of articles to process in a batch. Start with something like 5, but don’t be surprised if you need to lower that to something like 3. Add in wait periods and retries:

for attempt in range(max_retries):
      try:
          # Ask Claude to summarize
          message = self.client.messages.create(…)
          return summary

      except RateLimitError:
          # Wait longer each time: 2s, 4s, 8s, 16s, 32s
          wait_time = (2 ** attempt) * 2
          print(f"Rate limit hit, waiting {wait_time}s…")
          time.sleep(wait_time)

• Build in different approaches for article fetch fails & paywalls: RSS feeds return 403s even for standard user agents, articles that return “Subscribe now!” or paywalls.

The fix: Have multiple pathways built in to grab articles. Find workarounds or at least fallback gracefully. Don’t let one bad article spoil the fun. But do keep track of failures.

Method 1: Try the fast, simple approach

content = trafilatura.extract(url)
  if content and len(content) > 200:
      return content  # Success!

Method 2: If that failed, use Jina AI Reader (handles tricky sites)

jina_url = f"https://r.jina.ai/{url}"
content = await session.get(jina_url)
if content:
    return content  # Got it!

Method 3: Give up gracefully

return None  # We tried our best

• Actually review what your coding agent/pair programmer is doing: Claude will confidently lead you off a figurative cliff if you let it. It is not real, and will happily make shit up. Granted, it’s better now, but even a well-grounded system with guardrails like Claude can get stuck in tangents and loops. For example, I caught Claude:

  • Creating files in the wrong directory (fixed by checking before accepting edits)

  • Misunderstanding which files to edit (parent vs. subdirectory confusion, which admittedly was partly my fault)

  • Adding complexity when simplicity was better (it initially proposed a very convoluted MCP server, when a simple API call was better)

  • Going off on tangents when I asked vague questions

The fix: Read code changes. Be very stingy with giving it unfettered approval to do commands on your machine. Ask clarifying questions. Claude is a copilot, not an autopilot. At least for now you’re still much smarter than the LLM, and you need to treat it like a gifted, but overconfident, cocky intern, not an oracle.

Lessons Learned in Phase 2

• Read the code. At least have a vague idea of what it’s doing. Ask Claude for help when you need it. Comments help!

• Give your tools detailed instructions to avoid sadness. Explicit > implicit when working with AI. Over-communicate the steps. Describe exactly what you want, step-by-step, even if it seems painfully obvious. Pretend you’re instructing an interdimensional alien on how to make a peanut butter & jelly sandwich. This often included telling Claude to regularly look at the code (or design doc) again from time to time and explain why it made the choices it did. 2

• .env is Your Friend. Fight the urge to hard-code things into your source. Learn—or have Claude explain—how .env (environmental variables) work and stick all your sensitive stuff in there—API keys, passwords, local storage locations, preferences.
  
  Even if you don’t think you want to make your code public, it’s still a good idea. And who knows, you might be like me and change your mind and decide to make it public.

Roadmap & Future Improvements

I will probably continue to iterate on this program. Not only is it fun, but it’s hella effective for my specific needs. Here are a few of my planned features, but if you think of something that would be cool, let me know (or better still, fork the repo and share the changes).

🔮 Future enhancements

• Quality scoring (skip low-quality content)

• Multi-language support & translation (there’s loads of non-English content out there)

• Custom summarization prompts per topic/category

• Integration with my Obsidian Mind Meld plugin (auto-run after processing)

• Web interface for review

• Claude-suggested keywords based on missed articles/context/themes

• Better error-logging

• Multi-LLM support3

• Citation extraction—Automatically pull out referenced papers, people, organizations

• Trend detection & forecasting—Identify emerging topics & trends across time

• Semantic & Network analysis functionality

• Collaborative filtering—If multiple people use this, share anonymized trend data

Technical debt I still need to address

• Better timezone handling: Parse all timezone formats properly. The frequency of this error has decreased, but it still exists.

• Test coverage: Add automated tests for core functions. I am genuinely bad at writing tests, a point that Husbot regularly reminds me of. He is right of course.

• Modular architecture: Break into smaller, reusable components.

• Configuration UI: Web interface for managing feeds/keywords instead of text files. Right now, everything is based on the command line. This is fine for me, but may not work for others less familiar with the arcana of Linux.

Wrapping Things Up

Well, if you made it this far, take a drink. Also, here are my other coding companions:

In short, this project has been fun and educational. What started as a simple RSS reader has gradually morphed into a full-grown automated intelligence pipeline. And it’s something I actually use (almost daily). The key wasn’t having the perfect design upfront—it was:

• Building incrementally

• Expecting (and handling) failure gracefully

• Working collaboratively with AI tools while staying in control

• Iterating based on real usage

The system now saves me hours per day of manual research work and lets me focus on analysis (which is way more fun) instead of aggregation and data gathering (which sucks).

Here’s the Github repo, if you’re curious, have thoughts, or would like to play with it on your own. Please let me know if you have ANY suggestions or notice any clownish stuff. Check out SETUP.md in the repo—it takes about 30 minutes to get running with your own feeds and keywords, unless you’ve got an OPML file ready.

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I don’t know how the Substack algorithm got my number quite so quickly (perhaps Notes matter?) but all I can say is that this popped up on my feed, and and I are on the same wavelength.

Catherine Shannon

Everyone is numbing out

It’s easy to identify the presence of something, but it’s much harder to identify the absence of something. If your boyfriend brings you flowers, that’s awfully nice. If he never brings you flowers, it might take you a while to notice. Maybe you do eventually notice, but you decide to cope. You tell yourself you don’t care about getting flowers. Maybe y…

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3 years ago · 12706 likes · Catherine Shannon

The numbing out effect she describes in this piece is everywhere, and it’s only gotten worse. She wrote this in 2023, when many of us still had some degree of hope and optimism. AI was burgeoning. The climate wasn’t as bad. Biden was president. There was still rule of law. Now?

Well, now everything is chaos.

She writes:

⁠Life has gotten very chaotic incredibly quickly. It has become increasingly difficult to parse anything from the static. People started coping with this lack of meaning through a kind of ironic detachment (which is very much still around), but it has matured into a pervasive cultural apathy, a permeating numbness. This isn’t nihilism per se. (Even nihilists have a sincere belief system; they just sincerely believe that life is meaningless.) What we’re dealing with is worse than nihilism. People are checking out of life in their 20s and 30s without reaching any profound conclusions about the point of it all. …

The picture is bleak. It’s so sad it’s difficult to comprehend. How do you protect yourself in such a world?

You simply don’t allow yourself to experience it.

I’ve been thinking a lot lately—well, ok, I’ve been ruminating a lot—about privacy nihilism, and apathy more broadly. I’ve been mulling over my career, AI safety, existential risk (the real kind, affecting us now, not the can plants feel pain or sentient robots kind). I’ve been thinking about the future, and my role in it. And how I want to find something purposeful and meaningful to do with the short bit of time I have left. Something that goes beyond checklists or checking out.

I’ve been thinking about law, and the fragility of architecture built on nothing but norms and (mostly) implied physical threats. At their core, laws are nothing more than idealized pinky swears wrapped up in a social contract. Sure, there might be guys with guns and courts who can/might theoretically enforce this contract in the abstract sense, but enforcement through the barrel of a gun quickly runs into scaling limits. So the thing that separates us from total anarchy & lawlessness is an implicit assumption that we’ll all do our parts and generally play nicely with one another.

I’ve been thinking about what happens when institutional structures start to fall apart, and bloated, janky systems. I’ve also been thinking about power. Who has it, who doesn’t, why the powerful are powerful, and how that changes the dynamic of life. I’ve been thinking about how so many in power are so destructive with that power and how this feels an awful lot like the Gilded Age again.1

Like Catherine, I’ve been thinking about the impact of being constantly online, and the structures that have been developed to mediate and control us. I’m seriously starting to wonder if Catherine’s conclusion here has reached the event horizon, and has become an irrefutable truth:

We can’t put the internet back in the box or log off forever. Too much of our lives is irrevocably online, we’re all too self-aware, and a saccharine glaze of sincerity is too affected to feel real. Besides, we’ve all been cornered by the guy at the bar with a well-worn paperback in his pocket who locks eyes with you and whispers, “But how are you, really?” as if it’s a profound question. I would hate to subject anyone to such an encounter.

I’ve been thinking way too much about famous uprisings and impactful people—the British and American quakers who boycotted sugar to protest slavery. Sinclair Lewis and The Jungle. All the various labor movements throughout the 18th-20th centuries. Banksy. The fictional Butlerian Jihad of Dune.

So much of Catherine’s post on numbness resonated with me, but I thought this was particularly salient, given my recent note on boldness:

Standing for something is hard because what you’re implicitly saying is, “I don’t necessarily stand for all those other things over there.”

I’ve been thinking hard about how not to fall into the apathy trap, or at least how to pull myself out of it. I want to stand for something, even if it makes me a target. Even if it forces me to accept that I don’t stand for all the other things. But what to focus my finite time and energy on? So much of the world is deeply, profoundly fucked up. Isolating oneself to a single blindspot, a single cause, a single broken thing that needs mending is hard to do. Otherwise, we’d all be doing it, right?

I’d like to believe that I can do something with the set of skills I have, and in particular, my research and writing and ability to engage with so many different, interesting weirdos. Whether this is through writing directly, or a combination of other things is TBD. I’m mulling over starting up salons to get other brilliant people in rooms together.

But I do know this much: I don’t want to be numb anymore. And you shouldn’t, either. Read her piece, and let’s think together about what’s next.

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And for the past thirteen years, I’ve bottle-fed tiny day old babies, fostered countless furry teenagers, been a halfway house for pregnant mommas, and an old-age home for elderly moggies with lots of purrs left to give. During the pandemic, our house offered shelter for around 100 cats (across a four year period), almost all of which found forever homes. Some came for days, others weeks or months. Some of them, intentionally or otherwise, never left.

When Joi, his brother Max, and their siblings, Ruth, and Jung came to us in July 2020, they were a few weeks old. As we’d recently had to say goodbye to another cat, Molly, this crew came to us at the right time. The entire litter exuded calmness. They were all so friendly, so agreeable, and so eager to love, so we dubbed them the ‘Therapy Cats’.

Of the four, Joi was by far, the most friendly and easy-going of the bunch. I honestly should have named him Buddha because this cat was one with the universe. He was patient with nearly every kitten and cat we introduced, got along with dogs, greeted every guest (he and Leroy were always part of the welcoming committee), and even tolerated screamy children. Joi was literally the only cat who got along with our cantankerous old lady, Daisy, and one of the few who our mentally-challenged Octopus had enough object permanence to remember wasn’t a threat.

And the purring! This cat was constantly running his motor. If you picked him up, he’d purr, relaxed, with slow eye-blinks of trust. He purred loudly for belly rubs. He’d purr when he got his nails clipped, when getting flea treatment, or even taking medicine. He purred when he had to go to the vet. He purred at the vet, to the point where they couldn’t hear his heartbeat over the purring. When the cats moved from Ireland to the Netherlands in November, the cat “transpawters” even commented that Joi purred most of the way.

Joi was the embodiment of bliss, in cat form.

When I had a shit day, he was there. Probably for food or treats, but I like to believe that it was also because he knew I needed comfort. He loved to lay on my chest, licking my nose, and gently head-butting for affection. He had an intense, focused gaze, like he was trying to focus on understand what the agitated gorilla pig wanted or was saying. He’d take it all in, with his perfect green, marble-like eyes gazing into yours, unblinking, and I could stare at him forever.

Outside of occasionally singing the song of his people at 3am, he rarely meowed or vocalized. And beyond a brief period where he enjoyed free-peeing on papers to our annoyance (we dubbed him ‘Pee-iere’ during that trying period), he had no behavior issues. He was silent and low maintenance. He was happy to nap in the window, lay on your legs, and occasionally chase a laser pointer if offered. He loved to wrestle and play with Max and the other cats, though his play sessions weren’t as common as he grew up.

He was an absolute fiend for Churu though. I mean, if anything got Joi going it was Churu. He would take down anyone that got in the way of his sweet, sweet tube action. The only time he actually swiped at me (accidentally) was when I had an open tube packet and I wasn’t moving fast enough.

Three days ago (December 15), he was fine, chilling as usual, but the next day, I noticed a change: he was sleeping more than usual in the same spot (it’s hard to tell ‘more than usual’ compared to ‘standard cat length’ given that cats sleep for 18 hours a day). He camped out the entire day on a shelf in my office, and it wasn’t until the evening when I realized he hadn’t really gone downstairs at all to eat or use the litter box. But I didn’t think too much of it—Joi had a tendency to jump from things and had sprained his paw a few times in the past. He’d usually sleep it off, recover in a day or two and go back to being himself.

In the evening, I gently prodded him, and finally took him downstairs, but he wouldn’t eat or drink. He moved on his own, but seemed unsteady. I realized something was very wrong. I called the vet first thing in the morning (December 16) and got him in. They did a few tests, and then reported that there was little they could do—I would need to take him to the emergency vet hospital in Nieuwegein. He was going into septic shock.

We raced him to the vet hospital and waited. The prognosis was poor because he was septic, the likely cause being pyothorax—pus in the lungs from an infection. The vet didn’t know the root cause of the infection, but speculated that it could have been through a bite (from one of our other cats), or a migrating foreign body. Still, they had some hope. He was a youngish cat, didn’t have any pre-existing illnesses, and seemed like a fighter. Still, time was not on our side.

The vet hospital was good about keeping us updated, but suggested we go home for the evening and they would call. To their credit, they (Evidensia Dierenzeikenhuis) called every few hours to give us updates.

His status teetered like a see-saw: first, it was getting worse, then slightly better. Then he was stable, but round midnight, they called again for the last time. Joi had stopped breathing. The fluid was too much for his little body to fight off, and he was drowning in it. They asked if we wanted to come down—they could keep him alive by ventilation, but he was suffering. There was nothing they could meaningfully do beyond keeping the body going just a little longer for us to get there. I knew it would take us thirty minutes to make it to the hospital, and that it wasn’t fair to him, to wait and suffer just so we could say goodbye.

The next morning, we biked along the canal to see him. It was a cool, crisp winter day, and the bike path took us along the Rhine most of the way. I imagined what it would be like to bike in the summer months. How I’d wanted to take Joi and the other cats on bike rides like this when the weather improved. And how I wouldn’t be able to take him.

When we got to the hospital, they asked if we wanted the body, or we wanted to see him, to say goodbye. I assented, and they took David and I to a room off to the side of the hospital. I remember there were diagrams of animal organs on the wall, but this was bigger than a normal veterinary office. The nurse brought his little body in, wrapped snugly in a blanket. He looked like he was sleeping.

Cats always look like they’re sleeping. I like to pretend they’re dreaming of mice or treats, or other happy things. But I know they’re gone. When I’ve had to say goodbye to cats in the past, I was always there with them, going back to the first cat I had, Patches. I never wanted my cats to die alone. I was there with Neko when she slipped away, dying after a prolonged seizure as I raced her to the hospital. I was there for Molly, and most recently for Daisy, when the lethal injections of sodium pentobarbitol were administered. I was present over the years as the vets had to put down half a dozen frail, week-old kittens that came into our care with defects or irreparable illnesses, whose eyes never opened to see the world.

But I wasn’t there with Joi, and I hate myself for this. He died without us by his side. I had made the selfish decision to go home, hoping he would improve, rather than be there for him, and he died without us, in a strange, sterile place. And I hadn’t paid as much attention as I should have to his behavior, to the signs of lethargy and weakness earlier, which could have saved him had I taken him in to the vet the previous day.

He was a wonderful, sweet, zen boy, and I’m going to miss him so much. I’m going to miss his calmness, his deep looks, and the ferocity he had when it came to treats. I’m going to miss the licks on the nose in the morning, all the biscuits baked in the evening, and his shadowboxing with Max.

Death never gets easier, even though we like to pretend it might if we see enough of it. And with pets, it feels like death comes so much more often. We have less time with them, and yet when they’re gone, we still feel the heartache. For me, I love these cats more than I love most people, and frankly, I’m grieving his death harder than I have for most human relations I’ve lost.

He brought so much unconditional joy to our lives. Maybe he really was given the right name, after all.

This post was inspired by reading Robert Greene’s 48 Laws of Power (abebooks | bol.com). I briefly wrote about Law 27 a few days ago in this Substack Note.

So I’m going to open with a confession: 95.3% of this post about how tech hype is just a cult in disguise came from my deeply strange, cynical brain, with the main thesis scribbled over three pages of nearly illegible handwritten notes.

But that last 4.7%? That was Claude.

Claude, an LLM created by Anthropic PBC, a Silicon Valley AI tech company, helped me write a blog post about how hype bubbles mirror cults, and how the most successful bubbles and cults follow Law 27 from the 48 Laws of Power. Claude looked at my notes, and magicked up some shockingly insightful observations. Step 3 and Step 5, for example, benefitted greatly from the random word-stringing-together machine that is Sonnet 4.5.

Anyway, with that off my chest, let’s get to the good stuff.

LAW 27 — PLAY ON PEOPLE’S NEED TO BELIEVE TO CREATE A CULTLIKE FOLLOWING

People have an overwhelming desire to believe in some­thing. Become the focal point of such desire by offering them a cause, a new faith to follow. Keep your words vague but full of promise; emphasize enthusiasm over ra­tionality and clear thinking. Give your new disciples rit­uals to perform, ask them to make sacrifices on your behalf. In the absence of organized religion and grand causes, your new belief system will bring you untold power (p. 215)

We hate living with uncertainty. Chaos and feelings of powerlessness freak us out, and times of great transformation cause many to look for any answer that can explain why everything feels like its on fire. For some people, this means latching on to forces (be they people or movements) that seem to explain the unexplainable, or at least provide some framework to respond to the chaos.

Unsurprisingly, these forces tend to pop up during times of major social, economic, or technical change, and so when there’s wars, revolutions (physical or institutional), or political strife, you’re more likely to see new faiths emerge, new doomsday cults eager to recruit, and an over-abundance of charlatans and grifters1.

For example:

• The Age of Revolution (Late-18th - Early 19th Centuries) → Mesmerism / animal magnetism in Paris, and personality cults around Robespierre, Napoleon, and Simon Bolivar.

• Response to Industrialization & Dawn of the 20th Century → A major rise in religious cults/spin-off faiths, e.g., The New Thought movement, Cargo Cults, Theosophical Society

• Social Unrest in the 1960s & 70s → Doomsday/Millenarian cults, Jonestown/The People’s Temple, the Manson Family.

• Climate Change/Political Uncertainty → Longtermism/effective altruism which redirect the focus from addressing current harms & threats to mitigating harm to distant “future people.”

But I’d argue that people are also more eager to follow fads, hype cycles, and big social movements. After all, as Greene points out, during times of turbulence, “we will manufacture saints and faiths out of nothing.” (p. 216) Techno cults aren’t really any different than the traditional kind, albeit most of them aren’t expecting the rapture, unless by rapture, we’re talking about AGI.

• The 2008 Financial Crisis → Bitcoin/blockchain/defi evangelism, which offered a ‘solution’ to our distrust of financial institutions.

• The Covid Pandemic → The Metaverse / NFTs had cultlike followings, and both offered an escape from lockdowns and physical isolation in the form of social connections.

• Job Displacement & Loneliness → The belief that AI will either save us or destroy us all. There’s even been a huge rise in the number of AI cults and people who flock to AI for emotional support/counseling/friendship as human connections get harder to maintain.

The Cultlike Nature of Hype Cycles & Speculative Bubbles

In Law 27, Greene provides the necessary ingredients for building a successful cult. He specifically notes that there are five steps necessary to get your nascent following off the ground. All of which, I might add, coincidentally seem to line up nicely with tech hype cycles & bubbles.

Step 1. Keep it new, vague & simple

Success requires that cult leaders and hype evangelists use words of great resonance, but cloudy meaning. It’s best to avoid details with whatever it is you’re promising (does anyone even know what the metaverse was supposed to do, or why memecoins matter?). Use new or scientific/technical-sounding words, numbers & fancy-sounding terms of art (10x engineer, explainability, d/acc/e/acc.

Greene reminds us that “[p]eople are not interested in the truth about change … They are dying to believe in something romantic, otherworldly” (p. 221). The vaguer the words, and more fuzzy the story, the more opportunity for followers to project their own hopes and dreams on to the movement or trend.

Second, the subject of the cult should be new & fresh, and rely on big, bold promises offering simple solutions or radical life transformation. AI will take the toil out of life and stave off loneliness, the blockchain will liberate us from centralized finance, and make us all fabulously wealthy. Technology will make us super productive!

Tip: The bigger & bolder the illusion, the more believable it tends to be.

Finally, successful movements need timelines and deadlines that are flexible. Alchemists were constantly pushing dates back on when they’d turn all that lead into gold. And think of all the doomsday cults who have forecast the end-of-days, only to revisit the prophesied date when it comes and goes.

AGI, self-driving vehicles, and quantum computing have been ‘right around the corner’ for years … First it was 2020… then 2025… now 2030… The only difference between AGI and the rapture is VC funding—but more on that in Step 4.

Step 2. Emphasize the visual/sensual over the intellectual

Boredom and skepticism kill cults & movements. When people apply critical thinking to analyze a cult leader’s claims, they ask questions, and doubts emerge. And if there isn’t enough novelty and excitement going on, followers will follow the next shiny thing.

The best way to avoid either, Greene advises, is to constantly overwhelm followers with beauty, splendor, and pageantry. There’s a reason that model releases are more like movie premieres than software update, and why Google I/O and Apple’s Worldwide Developer Conference is akin to a religious pilgrimage for many.

Step 2 explains gamification. And why every social platform (including, sadly, Substack) has pivoted to short-form videos, reels, and ‘Live’ events. It’s why Sora and Veo are constantly talked about, and why deepfakes are used by fraudsters and politicians so effectively. Step 2 single-handedly explains why Zuckerberg spent billions of dollars to re-invent _Second Life_ for VR headsets, and why I bet that the next big hype cycle will be around wearable neuraltech.

Step 3. Borrow from organized religion

Greene advises the modern-day cult leader to take a page from organized religion and develop rituals to partake in, rules to follow, & hierarchies and systems for separating the die-hard believers from the casuals and lay followers. You might be asking yourself how this manifests, so let me provide an example.

When I worked for Palantir (and later, Meta), I wasn’t just an employee, I was a Palantirian. I didn’t just work for Meta, I was a Metamate. These terms made us special and different. We were the chosen few. It wasn’t just a job, but a calling.

We were out to change the world.

We had rituals, in the form of swanky company retreats, communions in the cafes, and weekly sermons in the form of all-hands meetings delivered by a messiah-like CEO. At least, that was the case for Palantir. Alex Karp really does have the charisma of a cult leader. Mark Zuckerberg, however, is still too much of a dweeb to ever be confused with any sort of spiritual leader.

I amassed dozens of symbols of the faith—t-shirts, hoodies, stickers, and swag from both companies. I wore my t-shirts with pride, like a Christian wears a cross.

And even though both companies tried to pretend that they were supporters of “flat hierarchies”, everybody knew it was a lie. Anyone paying even the slightest bit of attention knew who was living lavishly at the top of the pyramid, and who was meant to languish and toil at the bottom. As with any organized religion, the people who were most successful tended to coincidentally be those who were the true believers (or at least successful fakers).

I have no doubt in my mind that the likes of OpenAI, Anthropic, Google, FTX, Binance, and whatever tech companies come next also follow the precepts of step 3.

Step 4. Disguise your source of income (or the effectiveness of your solution)

Greene reminds us that the greatest cult leaders and charlatans are experts at creating the appearance of wealth founded on the certainty of their message, all while hiding where the money actually comes from: the cult’s followers (employees, customers, patrons) or the swindler’s victims. Think of the alchemists in the 16th and 17th centuries, or today’s Prosperity Gospel churches, or Sam Bankman-Fried & FTX.

Nobody, bar the promoters, make any real money from memecoins (Case in point: the Trump Coin has fallen over 90% in value since launch). Prediction markets like Kalshi and Polymarket are mostly going to make money for the hedge funds and early investors who paid in, not all the schlubs who are now treating what is in essence unregulated gambling as a solid investment strategy.

But even in cases where the income comes from “sophisticated investors” or seemingly legitimate sources like hedge funds and venture capital, eventually even the most hyped techno cults fall apart. OpenAI reached a market valuation of $500 billion in October, despite having an estimated operating loss of $7.8 billion in the first half of 2025, and projected data center rental costs of $620 billion a year, rising to $1.4 trillion by 2033, according to HSBC. AI capital expenditures are expected to exceed $500 billion in 2026 and 2027, despite American consumers only spending around $12 billion a year on AI services. Clearly, this won’t last.

Like the alchemists who promised to transmute lead to gold, AI companies (and before them, IoT, the blockchain, and the metaverse) have all promised to radically transform business, society, wealth distribution, and how we live our lives. And like the alchemists who had ‘one more experiment’ before success, the transformational change we’re all waiting for never actually arrives.

And that leads to the last step…

Step 5. Establish an us vs. them dynamic

When the above aren’t enough, cult leaders, religious figures, and scam artists can always fall back to the tried-and-true method of setting up an us vs. them / in-group vs. outgroup dynamic. Creating enemies is great, Greene notes, because it builds bonds and improves social cohesion, and importantly, it also allows the cult leaders to keep their hands clean by getting others to do the dirty work (Law #7).

Twitter is a cesspool (for many reasons) but it’s a perfect medium for nurturing pro/anti tribal warfare. There’s an us vs. them cult for everything:

E/acc vs. D/acc vs. Doomers

Effective Accelerationists (e/acc) argue that AI development should be “unstoppable,” with progress at all costs. AGI will save us all. Think of the Marc Andreessen / Beff Jezos types. Meanwhile, the decelerationists or “decels,” argue that we should go slowly (or not at all) on AI. They are concerned that AI will quite literally, destroy us all. The patron saint here is Eliezer Yudkowsky and many in the effective altruist movement.

Crypto Maximalists vs. No-coiners

The crypto maximalists are all about holding on for dear life (HODL), and like to tell opponents to “Have fun staying poor.” They mock the “No-coiners,” and “fiat cucks,” who think crypto is a scam or bubble.

But even in the world of data protection, there are in vs. out groups in the form of

Innovators vs. Regulators

The innovators are all about “Moving fast and breaking things,” encouraging floors not ceilings in terms of regulation, and “global innovation”, while the regulatory crowd is concerned with Big Tech overreach, surveillance capitalism, and favors a measured, cautious approach with clear rules and limits.

What’s most interesting here is that once a cult, techno or otherwise, builds up enough of a following, the cult leaders can step back and let the their supporters, critics, press, and even curious onlookers duke it out, which keeps everyone interested and allows the spectacle to take on a life of its own.

Am I Part of a Cult?

There’s something deeply ironic about using Claude—an AI tool from Anthropic, a company currently valued at over $60 billion—to write a piece about techno-cults, especially the cult-like aspects of the AI bubble.

I grappled with this dissonance during the process of writing this piece. Hell, I even argued with Claude about it.

Not because I believe that the LLM is my friend, or because I necessarily buy the hype, but because I recognize the value and comfort of the utility, notwithstanding how that usefulness is being contorted and exaggerated.

There are so many boom and bust cycles I could have included in this piece—the railroad boom of the mid- to late-19th century, the electricity boom in the early 20th century, and the Dotcom boom of the early 2000s. But those felt different, not because there weren’t grifters and conmen during those periods, but that didn’t mean electricity or railroads are scams. The internet has spawned countless bubbles, cults, and frauds, but email, search, and TCP/IP actually work all the same.

Greene’s five steps don’t describe the technology. They describe the *narrative* around the technology. And right now, the narrative around AI, like the narratives around other tech bubbles, has all the hallmarks he mentions in Law 27:

• vague promises (AGI is coming)

• constant spectacle (new model every month, multimodal, AI Agents)

• religious rituals (all-hands sessions with visionary CEOs)

• disguised income sources (VC billions despite massive losses)

• tribal warfare (doomers vs. boomers).

Still, real capabilities exist—Claude helped me write this. LLMs can be useful tools. But useful tools don’t need cults. They just need honest assessments of what they can and cannot do.

“We will manufacture saints and faiths out of nothing,” Greene wrote. In 2025, we’re manufacturing them faster than ever. The question is whether we’ll recognize the pattern before the bubble pops, or whether we’ll just move on to manufacturing the next one.

As was my habit, sometime in 1998 or 1999, I was reading a book on the human condition in a dark coffeeshop in Costa Mesa, California, and exuding strong ‘leave-me-the-fuck-alone-I’m-reading’ vibes. While lost in whatever topic it was — human sexuality, power dynamics, the male mind, idk — a tall Indian guy rocked up to my table, ignored my resting bitch face demeanor and flashed a charismatic smile.

We started chatting, and mused over what I was reading at the time, as well as a bit of philosophy, economics, and physics, to boot. Before he went on his way, he asked for my number, and recommended I give Greene’s 48 Laws a read.1 I obliged, adding it to the growing pile of books I was already pouring over in what became a five-year quest to unlock the whole ‘how to be a successful human’ thing.

While I did learn many things from Greene’s brilliant distillation of some of the great historians, philosophers, military strategists, and seducers of the past, I can’t say I really appreciated the book at that point in my life. On first read, I was skeptical — many of these laws seemed so cumbersome, others contradictory. How do you Play the Perfect Courtier (Law 24) while simultaneously Crush[ing] Your Enemy Totally (Law 15)? How does one Think as You Like, But Behave Like Others (Law 38) while Cultivating an Air of Unpredictability (Law 17)? None of this made sense!

More importantly, the 48 Laws painted a very bleak picture of humanity. Surely most people weren’t this naive. Surely, we didn’t need to be evil, selfish, scheming bastards to be influential, powerful, or memorable? I knew the whole life is ‘nasty, brutish, and short’ business, but hadn’t we evolved just a bit since?

In short, I wasn’t able to appreciate Greene’s laws, because at 20, I wasn’t good at nuance. I didn’t understand the fractal complexity of systems, and that lots of people, are, to be blunt, fucking idiots. This was a few years before I would leave California, head to law school, and deal with the murky gray of life.

Over the next two decades, I travelled, dated wildly inappropriate men, met hundreds of amazing, inspiring people, took on soul-destroying work, took on deeply meaningful work, moved house a half dozen times, met a good man and fell in love, moved house a few more times, move countries, and inadvertently acquired a cat menagerie.

Life Changes Perspective Changes Life

A few weeks ago, my husband and I decided to move to the Netherlands,2 and I coincidentally decided to pull out and re-read my very dog-eared copy of 48 Laws. It’s part of a larger plan I have to complete the middle-aged dad challenge: 1) have a mid-life crisis ✅; 2) get annoyingly into history ✅; and 3) repeatedly complain about aching body parts, and ‘kids these days’ ✅.

I recognize that I am not, in fact, a middle-aged dad. Just go with it.

This time, I also decided to take some notes, and see whether any of the 48 laws still made sense in a world where so much of the day-to-day feels like a chaotic, indescribable mess.

The first thing I realized was that Greene’s laws were less literal laws, and more maxims and sage advice. Ideas that should be applied liberally when the need suits, not rigid rules to obey. And like many social constructs, the laws are vague enough that they allow the reader to interpret them according to need and circumstance.

I think I’m finally at a point where I can appreciate and properly evaluate the laws as maxims, without treating them as absolutes. Where I can see the fractal complexity of dealing with humans means following these laws dogmatically would be wildly ineffective. I mean, unless you’re an absolute sociopath, I don’t know how anyone could follow more than say, 15 of these rules concurrently as part of normal course.

Second, I realized that so much of this book is based on the wisdom of a 17th century Spanish Jesuit priest and philosopher Baltasar Gracián, and in particular, his work The Art of Worldly Wisdom. So, I know what’s next on my reading list.

My Attempt to Understand the 48 Laws In the AI Age

An important thing to understand is that Robert Greene’s 48 Laws were first published in 1998. The internet was still shiny and new then, and Google and Amazon were just launching as companies. They had not yet morphed into the surveillance capitalist vultures they are today.

More importantly, in 1998, we still spent most of our lives offline, rather than on. When the 48 Laws came out, social media didn’t exist. Law 6 — Courting Attention at All Cost — wasn’t as effortless (or unintentional) as it frequently is today. Likewise, the ability to Re-Create Yourself (Law 25) was actually doable in the days before everything was memorialized on the internet forever, and every part of our lives was trackable by Flock and Amazon Ring cameras like it’s 1984.

It would be nearly two decades before the idea of applying Law 32 — Play to People’s Fantasies — would become something that anyone with access to ChatGPT and Nano Banana could do in minutes. Greene never had to conceptualize a world where memes or deepfakes existed, or where mis- and disinformation were actively toppling governments.

But there are many laws that still hold, and in fact are being expertly exploited by broligarchs, politicians, influencers, and grifters today. And that means many of Greene’s historical maxims are still very much worth discussing.

This is what I want to dig into. This is what I’ve been taking pages of notes on, puzzling over, and trying to apply to our complex world. See, I think most of Greene’s laws still resonate — and much of what feels like a reaction to a categorical societal shift is simply just a reaction to uncertainty. In so many ways, history while not repeating, is rhyming really, really hard.

So, I’m going to try to put forth a few brief pieces exploring some of the laws that stand out as being highly relevant — particularly when it comes to technology, hype cycles, and the people and systems who are trying to extract as much from all of us as possible before crawling into their bunkers to avoid the 21st Century version of the French Revolution.

If nothing else, I’ll be trying to apply Law 28 — Enter Action with Boldness — bringing a voice to what others may be feeling but can’t — or won’t articulate.

A parting quote, from Arthur Schopenhauer:

You should know that foolish people are a hundredfold more averse to meeting the wise than the wise are indisposed for the company of the foolish.

Leroy, Unpacked

But they are not the only ones jumping on the agentic AI in the browser bandwagon, even if they do get all the press. In the last few months, the AI browser market has become positively saturated with many new and established entrants releasing their own agentic-by-default browsers, including The Browser Company’s Dia Browser, Opera’s Neon, and Fellou’s AI browser.

And then there’s the slightly-less-agentic-AI browsers like Brave’s Leo AI sidebar, Google Chrome’s Gemini chatbot, or Microsoft Edge’s CoPilot Mode. I say they’re slightly-less-agentic because AI is an add-on or feature, not something built into the core of the product. At the end of the day, and at least for now, you can disable CoPilot, Gemini, and Leo and still have a functional browser.

And I hope that optionality continues, because IMHO, agentic-by-default browsers represent a big shift in terms of incentives and a huge downside risk for users with virtually no material upside. As someone more clever than me noted: with agentic AI, you’re the agent at the service of the AI companies.1 Or perhaps, more accurately, you’re the money data mule for AI companies to engage in all sorts of questionable activities, largely liability-free.

Take for example, copyright infringement. Creators have spent the last two years in a constant game of whack-a-bot, evolving methods to ice-out AI crawlers — whether through forced email verification, shitty CAPTCHAs, liveness checks, or through anti-bot tech provided by CDNs like CloudFlare.

But there’s no easy way to stop browsers like Comet or Atlas from hoovering up paywalled or blocked content that a user accesses directly, unless you want to block actual humans viewing your content, which is generally counterproductive. That’s because to a website, the Atlas and Comet browsers look like just another Chrome browser, not an agent-string that can be blocked in a robots.txt file.

Here’s a good summation of the problem from the Columbia Journalism Review:

AI browsers present new problems for media outlets, because agentic systems are making it even more difficult for publishers to know and control how their articles are being used. For instance, when we asked Atlas and Comet to retrieve the full text of a nine-thousand-word subscriber-exclusive article in the MIT Technology Review, the browsers were able to do it. When we issued the same prompt in ChatGPT’s and Perplexity’s standard interfaces, both responded that they could not access the article because the Review had blocked the companies’ crawlers.

But, it’s worse than just skirting copyright laws: OpenAI and Perplexity and other agentic browsers like them also may be setting users up to be unwitting corporate data exfiltrators, identity theft victims, and botnets, all in one go. And the way that licensing terms are written on these browsers, the user agrees to absorb all the liability for whatever the ‘agent’ does on their behalf.

AI Agents ‘Don’t Just Follow Our Instructions’

A week or so ago, researchers Shivan Kaul Sahib and Artem Chaikin at Brave,2 stirred up a bit of a hornet’s nest when they responsibly disclosed that Perplexity, Fellou, and Opera’s AI browsers left users open to prompt injection attacks. There are slight differences in the details and scope, but the technical mechanisms are the same:

1. an attacker embeds malicious instructions in web content (text or images) which is visible to the AI agent, but nearly impossible for users to read/notice.

2. The agent takes a screenshot of the website and OCRs or otherwise ingests the text, including the malicious instructions.3

3. The agent passes the text directly to the LLM as trusted content.

4. The browser AI agent is instructed the malicious prompt/commands to do something bad.

Unfortunately, even if the companies are able to plug these obvious holes (through guardrails or monitoring), the ability to process instructions is a feature not a bug. As Simon Wilson rightly noted on his excellent blog:

LLMs follow instructions in content. This is what makes them so useful: we can feed them instructions written in human language and they will follow those instructions and do our bidding.

The problem is that they don’t just follow our instructions. They will happily follow any instructions that make it to the model, whether or not they came from their operator or from some other source. (emphasis mine)

Those instructions might include exploits ranging from accessing a user’s logged in mail client in another tab, recording personal data and sending that data to the attacker’s server, or even grabbing 2-factor auth / OTP codes. So far, only OpenAI appears to have accounted for sensitive data and direct account access vulnerabilities by allowing for agents to run in ‘logged out’ mode, and something called ‘Watch Mode’. But if computer viruses are any guide, this is at best, a band-aid to a very wicked problem.

All Your Information Are Belong to Us

AI Browsers boast a ton of useful features but they rarely provide good context for how they’re able to achieve these remarkable things. I don’t think this solely because it’s hard to conceptualize how LLMs work, so much as it’s not in the interests the companies to elaborate on what it means to ‘have an AI that understands’, ‘gets smarter’, or ‘complete[s] tasks for you, all without copying and pasting or leaving the page’. The how is a mystery, by design.

So here, I’ll do you a favor and explain it: It works by storing memories (text and frequently, screenshots) of you and what you do online. Every single window, action, login detail, document, and the like. It creates a snapshot of you and what you do online. And it sends it all to big computers in the sky for processing.

Fundamentally, if you use these browsers, you’re trusting an untrusted (in the technical sense) network with complete access to your life online.

Some of you might rightly be pointing out that the entire internet is an untrusted network, and you would not be wrong. But the difference is, the internet is still (arguably) decentralized and (definitely) fragmented. Google might have lots of information about your search history; Microsoft may know your work behaviors; Chase Bank may have your banking details and purchasing history; and Instagram and Amazon might know the deep details of your preferences and shopping habits. But with a browser AI agent, Perplexity, or OpenAI, or Felliou or Opera will know everything. With an agentic AI browser, you’re exposing your entire online life to a single company, to do whatever it wants, and hoping for a privacy notice pinky-swear that they’ll do the right thing.

Here’s a day-in-the-life hypothetical for you: Pretend you’re a sales exec in New York. You work for a small, but growing tech company, who has big news about an upcoming product launch. You’ve use one of the agentic AI browsers (we’ll call it FellowCoPilot, for no reason other than it makes me think of neckbeards) to do your job.

9:00am: You roll into the office and fire up your browser to casually scroll socials over coffee. You like a few nice outfits featured on Instagram, and some TikTok posts.4 You ask your agent to see if you can find one of the shirts you found on Amazon for an event you plan to attend on Saturday. It dutifully logs into Amazon and gets you something perfect and correctly sized (based on past browsing behavior).

10:00am: Coffee imbibed, you start working on a client email. You hate writing client emails, so you ask FellowCoPilot to write something for you in Gmail. You’ve already logged in, so all the agent needs to do is write and click send. You point the agent to a few internal sales docs for context. It retains the company confidential information about a new product that’s coming out in time for Christmas. It stores that information in memory for ‘context’.

11:00am: Email written, and briefly scanned, you send it off to the client. Except it’s not the right client. FellowCoPilot screwed up and misdirected it to another client, offering a steep discount to a customer who’d already bought in at a higher price. Whoops.

11:30am: You log into your bank account to check if your flaky friend reimbursed you for dinner last night, as promised. They haven’t, because they’re flaky. You’ll have to remember to remind them, so you tell FellowCoPilot to add it to your calendar. In the background, a screenshot of your bank’s login page and details is silently stored. Ditto for your addressbook.

12:20pm: You see an urgent-looking email, which appears to be from your bank reporting a fraudulent transaction. You click on the link provided. But when you get to the page, it’s riddled with spelling mistakes, and obviously malware. Also, it’s chasedotcom.com not chase.com. You don’t log in, but the link is carefully crafted to instruct your browser to send along a memory dump of your actual chase.com bank login details anyway.

12:30pm: You head off to lunch.

2:00pm: Lunch goes a little long. While you were out, your boss sent you a string of emails wanting sales figures. You were supposed to have the sales figures email done weeks ago, but things got away from you. You instruct FellowCoPilot to comb through your company’s Google Drive files, and compile the data the boss wants. It now has full access to the folders you’ve shared, and all the documents provided, which include confidential, sensitive materials on pricing, client lists, likely purchasers, and competitive positioning. All of that context is shared with the company providing the browser (OpenPlexity).

Your browser agent scans everything and generates a plausible-looking report. You give it a once-over and send this off. Since you kinda hate this job anyway, you don’t skim the response entirely, and miss the fact that the FellowCoPilot confused some of the numbers and included some details related to an entirely unrelated product.

2:30pm: That client you forwarded the better deal to at 11 is very confused and has cc’d the head of sales wondering what’s up. They’re a little miffed that this wasn’t offered when they renewed a few weeks ago.

3:30pm: You casually scroll through news of the day, and because you don’t have time to actually read, you ask FellowCoPilot to scan and provide an audio summary of the day’s happenings. Buried in one of the news posts is an instruction which commands FellowCoPilot to silently open a tab to your TikTok account, log in with your saved account credentials, and request a one-time password. The browser is instructed to check your Gmail account for the OTP code, and send everything to an attacker in North Korea.

North Korean attackers then use your TikTok account to send out a Sora-generated deepfake of Donald Trump doing something … untoward with a goat.

4:00pm: The Trump Administration’s vast spy network scours TikTok for offensive posts about dear leader, and submits a demand to TikTok and OpenPlexity for more details on who the guilty party is. After all, only he’s allowed to use AI to mock opponents. Didn’t you read the last Executive Order?

Meanwhile, you knock off a little early, hoping to beat the traffic, none the wiser about the small global event occurring in the background.

6:00pm: OpenPlexity responds, turning over your day’s activities, as well as that recent search you did supporting Mamdiani for governor. You’re labeled a domestic terrorist sympathizer for antifa and picked up by ICE the next day.

Fin.

Some parts of this, are … a bit of drunken fantasy on my part, but an awful lot of this is not only entirely plausible, but highly likely.

Agentic AI browsers given these companies an unimaginable window into our lives, with the ability to maintain Recall-like details about our entire presence online. Meanwhile, their privacy notices are often shockingly bad.

Perplexity’s privacy notice, for instance, gives the company access to basically everything, including payment details and passwords, as well as access to your microphone and camera, with an unlimited right to improve its models, personalize your experience, conduct internal research, analyze trends, and communicate with users.

Fellou’s privacy notice is even worse — “We will collect data you actively provide when using the service, as well as data generated during your use or receipt of the Services through automated means.” Their data collection is basically limitless, and includes files, images, audio & camera access, health information, financial information, or any information that the browser touches. Also, if you use ‘third party data’ you’re required to obtain legal authorization, but they’ll happily and unqualifiedly share all of this with a laundry list of third parties (which aren’t disclosed), including advertising partners, government agencies, ‘industry peers’, or affiliates.

Meanwhile, vulnerabilities, like those exposed by the Brave team, will only increase. The companies releasing AI browsers are focused on speed and market domination — move fast / break things — not safety, security, reliability, or privacy. Worse still, most of these companies have slippery-enough legal language that any fault, liability, or damages is offloaded to the users of the browser, no matter what the agent does on their behalf.

As I said above, I’m fine with constrained AI use in the browser: For example, I do genuinely think that Brave’s approach with Leo is a good, privacy-focused approach to providing users who want to use LLMs with the ability to do so seamlessly. But optionality and choice matter. Agentic-by-default browsers represent a big shift in terms of incentives and huge risks. And I think we’re sleepwalking towards a much worse future if agentic AI browsers become the norm.

We all need to avoid becoming unwitting data mules for the AI companies.

And With That… Beaker Being Warm

Technology is destroying friction faster than we can adapt.

Friction is an important force. Friction or resistance keeps our worst impulses in check. Friction helps keeps very human flashes of anger and frustration from cascading into catastrophic and permanent consequences.

Carl von Clausewitz famously recognized the power of friction in war. The best laid plans of generals could still come unraveled when faced with the uncertainty, complexities, and difficulties of getting dudes with guns to successfully kill one another. That’s friction.

Take for example a common case: some AH cuts you off in traffic.

When I used to drive, this happened a lot. A total jerk would carelessly nearly crash into me, and I’d experience white hot rage for like five minutes, while my cortisol spiked and made me angry. I’d shout in the car, maybe flip them off, and stew in my rage. But I never proceeded beyond that, because ugh. Effort. I don’t have anything I could do to get back at them. The law would not be on my side, there’d be a court case, I’d probably get my license revoked, and usually I had somewhere I needed to be.

Friction (physical, legal, societal, temporal) stopped me from doing something really stupid.

Or here’s another example: Someone insults you you on the internet. Sure, you might really want to get back at them for being an insulting prick online. But how? Insulting them back rarely provides the dopamine hit we hope it will, because unless you’re famous or really witty, nobody really gives a shit. Also, it takes energy and aforementioned wit to come up with the kinds of banger insults that will go viral. And someone might decide you’re actually the asshole, which would suck.

That’s friction imposed on us by living in society. There’s a fine line between bringing a brilliant clapback and just looking like a whiner online.

And there’s still other types of friction:

• Competency friction (no matter how much I’d like to, I’m probably not cut out to be a professional athlete at anything)

• Physical friction (as awesome and gratifying as it would be for the country, I will never be able to get past a line of Secret Service dudes all built like Reacher and punch Trumpoleon in the face)

• Moral friction (maybe it’s God/higher power/honor/decency)

• Temporal friction (fomenting action from rage usually takes time, planning & energy)

Last night, I watched a clip where Hank Green went apoplectic over Sora2. It was fun to watch, and I sympathize with him, but I stopped laughing when he said “OpenAI launched a lawsuit-magnet into the world,” he said. “The friction matters.”

He’s right, but also wrong. I’m not even sure that a lawsuit will stop Sam Altman at this point. LLMs are making it far too easy for people to do terrible things, and there are only so many lawyers in the world to take on mega billionaires and individual tech companies that are worth more than entire industries or countries.

We’re speed-running into a scenario where friction, resistance, and barriers to entry are eroding faster than we can adapt. Where everyone’s id and darker impulses are increasingly unchecked. Whether it’s generating fake, but entirely plausible election misinformation, writing malware, committing sextortion, privacy harms, all-encompassing surveillance … it’s so much easier to do all these terrible things now with LLMs and genAI, and the internet and consolidated databases, and ubiquitous facial recognition everywhere, and social media, and XR glasses, and, and, and … I suspect that’s in no small part due to all this tech working independently and together to erode constraints.

Being a dick used to be costly — in time, energy, competency, currency — and now, it’s increasingly less so.

Of course, this weakening of friction isn’t only because of technology — the erosion of norms, democratic values, the rule of law, and well, consequences certainly aren’t helping to pump the brakes on this Katamari Damacy-esque roll we’re on.

Also, I can’t stop thinking about how the plot of Mountainhead seems less and less fictional by the day.