TL;DR

• The history of AI agents runs from 1980s symbolic and reactive architectures, through the deep-reinforcement-learning game-players of 2013–2017, to the LLM-agent explosion catalyzed by ReAct (2022), Toolformer (2023) and AutoGPT (2023), and finally to the 2024–2026 era of computer-use agents, the Model Context Protocol, and self-improving personal agents.

• The frontier challenge is no longer capability but reliability and safety: prompt injection, agentic misalignment (Anthropic’s 2025 blackmail findings), and long-horizon autonomy are the dominant open problems as agents gain real-world permissions and access to sensitive data.

Key Findings

• The intellectual scaffolding of agency predates LLMs by decades: Russell & Norvig’s rational-agent paradigm, Rao & Georgeff’s BDI model, and Brooks’s subsumption architecture established the vocabulary of perception, action, goals and autonomy.

• Deep RL (DQN, 2013/2015; AlphaGo, 2016; AlphaZero/MuZero, 2017–2019) proved that learned agents could reach superhuman performance in closed environments, but did not transfer to open-ended language tasks.

• ReAct (Yao et al., 2022) is widely regarded as the founding pattern of LLM agents: interleaving reasoning traces with actions. Toolformer (Schick et al., 2023) and function-calling APIs operationalized tool use.

• Open-model efforts—above all Nous Research’s Hermes function-calling standard with its <tool_call> token scheme greatly democratized agentic capability beyond the closed labs.

• OpenClaw and Hermes Agent are recent 2025–2026 waystones: both are self-hosted, model-agnostic, “always-on” personal agents that expose exactly the capabilities (filesystem, shell, browser, messaging) that make safety the central question.

Foundations

The concept of an “agent” was formalized in AI long before language models. Stuart Russell and Peter Norvig’s Artificial Intelligence: A Modern Approach (first edition 1995) organized the entire field around the intelligent agent: an entity that perceives its environment through sensors and acts upon it through actuators, choosing actions to maximize a performance measure. This framing (rational agents, environments, percept sequences) still remains the conceptual backbone of how we describe LLM agents today.

Two competing architectural traditions defined nearly all early agent research. The symbolic/deliberative tradition is exemplified by the Belief–Desire–Intention (BDI) model. Building on philosopher Michael Bratman’s theory of practical reasoning, Anand Rao and Michael Georgeff formalized BDI in the early-to-mid 1990s (notably “Modeling Rational Agents within a BDI-Architecture,” 1991, and “BDI Agents: From Theory to Practice,” 1995), grounding it in the Procedural Reasoning System (PRS). A BDI agent maintains beliefs (its world model), desires (goals), and intentions (committed plans), and deliberates over them—an architecture used in real systems such as the OASIS air-traffic-management prototype.

The opposing reactive tradition was launched by Rodney Brooks at MIT with the subsumption architecture (1986, “A robust layered control system for a mobile robot,” and the 1991 manifesto “Intelligence Without Representation”). Brooks rejected centralized symbolic world models, arguing “the world is its own best model,” and built robots from layered, competing behavior modules where higher layers subsume lower ones. This bottom-up, embodied philosophy directly anticipates today’s debate over whether agents need explicit planning or can rely on tight perception-action loops. The period also produced multi-agent systems research—coordination, negotiation and communication among many agents—captured in Michael Wooldridge’s textbooks and the BDICTL/LORA logics.

The reinforcement-learning era: game-playing agents

The modern notion of a learned agent arrived through deep reinforcement learning at DeepMind. The 2013 NeurIPS workshop paper “Playing Atari with Deep Reinforcement Learning” (Mnih et al.) introduced the Deep Q-Network (DQN), the first algorithm to learn control policies directly from high-dimensional pixel input using experience replay to stabilize training. The 2015 Nature paper “Human-level control through deep reinforcement learning” extended DQN across 49 Atari 2600 games and is widely credited with founding modern deep RL. AlphaGo (2016) combined Monte Carlo tree search with deep networks trained on human games and self-play to defeat Lee Sedol; AlphaGo Zero and AlphaZero (2017) removed human data entirely, learning from self-play alone; MuZero (2019/2020) learned a model of environment dynamics without being told the rules. These systems proved that agents could achieve superhuman performance—but only in closed, well-specified environments with clear reward signals, and they did not generalize to open-ended, linguistic, real-world tasks.

LLMs: reasoning, acting, and tools

The bridge from game-players to general agents was the large language model. Two prompting discoveries were pivotal. Chain-of-thought prompting (Wei et al., “Chain-of-Thought Prompting Elicits Reasoning in Large Language Models,” arXiv:2201.11903, January 2022) showed that prompting a model to produce intermediate reasoning steps dramatically improved performance on multi-step problems—an emergent ability appearing only at the time at ~100B-parameter scale. Then ReAct (Yao et al., “ReAct: Synergizing Reasoning and Acting in Language Models,” arXiv:2210.03629, October 2022; ICLR 2023), from Princeton and Google, fused reasoning with acting: the model interleaves thoughts, actions (e.g., querying a Wikipedia API), and observations. ReAct reduced hallucination relative to pure chain-of-thought and, per the paper, “on two interactive decision making benchmarks (ALFWorld and WebShop), ReAct outperforms imitation and reinforcement learning methods by an absolute success rate of 34% and 10% respectively, while being prompted with only one or two in-context examples.” It is now routinely cited as the founding loop of LLM agents. It would later become a core component of synthetic data generation for training reasoning models.

Tool use was the other essential ingredient. Toolformer (Schick et al., Meta AI & Universitat Pompeu Fabra, arXiv:2302.04761, February 2023; NeurIPS 2023) showed a model could teach itself, in a self-supervised way, when and how to call APIs—a calculator, search engine, translation system or calendar—by inserting candidate calls into training text and keeping those that reduced loss. In parallel, the commercial labs shipped function calling: OpenAI introduced function calling in its Chat Completions API in mid-2023, turning free-form text into machine-readable structured calls and giving developers a reliable substrate for tools.

The autonomous-agent explosion of 2023 and 2024

The release of GPT-4 on March 14, 2023, triggered a wave of experiments in fully autonomous agents. AutoGPT, released March 30, 2023 by Toran Bruce Richards (Significant Gravitas), wrapped GPT-4 in a loop that decomposed a goal into sub-tasks, executed them, and recursively spawned new tasks. Its growth was explosive: per the AI Wiki AutoGPT entry, “On April 3, 2023, Auto-GPT became the top trending repository on GitHub. By April 12, it had reached 30,000 stars,” and it “quickly became the fastest-growing open-source project in GitHub history at the time, amassing over 100,000 stars within weeks.” In the same month, I developed the GPT Council, which used parallel calls to different specialized/embodied LLMs and a self-review step to produce orchestrated multi-agent “subagent” outputs (at the time, for medical analysis). BabyAGI, posted by Yohei Nakajima in early April 2023 (from his “Task-driven Autonomous Agent” concept), distilled the idea to ~105 lines of Python orchestrating three GPT-4-backed roles—execution, task-creation, and prioritization—with a Pinecone vector store for memory. LangChain provided the connective tissue (prompt chaining, tool abstractions, memory) that most of these systems used. llama-index surged in popularity as a popular agent primitive framework. Various memory storage & recall methods emerged and rose in popularity: Retrieval Augmented Generation (RAG) became a household term.

What they got right: they proved the concept of goal-directed autonomy and ignited enormous developer interest. What they got wrong: reliability. They looped endlessly, hallucinated, browsed fragile web pages, and burned through API budgets—a single unattended run could cost hundreds of dollars. The consensus that emerged by 2024–2025 (visible even in AutoGPT’s own retrospective) was that autonomy alone is insufficient: orchestration, human-in-the-loop checkpoints, and standardized protocols are essential. AutoGPT’s lasting contribution was arguably the Agent Protocol, an early attempt at agent interoperability. Developers of many of these projects would create more down the line, each one attempting to improve on the drawbacks of the last, periodically supercharged by iterative model releases which improved the LLMs abilities in function/tool calling, long-horizon tasks, and reasoning.

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Democratizing tool use: the Hermes function-calling standard

A critical but underappreciated thread is the open-model effort to replicate function calling outside the closed APIs. This is where the constructive principles of “Hermes agent” belong. Nous Research has released its Hermes series of fine-tunes since 2023 under a philosophy of being “user-aligned, minimally filtered, and highly steerable.” The decisive release for agents was Hermes 2 Pro (Mistral 7B version released around March 2024; Llama-3 8B version announced May 1, 2024), which introduced the Hermes Function-Calling standard: tool definitions wrapped in <tools>, invocations in <tool_call>, and results in <tool_response>—with these tags promoted to single tokens to make streaming tool calls reliable to parse. Nous also open-sourced the hermes-function-calling-v1 dataset that trained these capabilities. Per the NousResearch/Hermes-2-Pro model card on Hugging Face, the model achieved “a 90% on our function calling evaluation built in partnership with Fireworks.AI, and an 84% on our structured JSON Output evaluation.”

Hermes 3 (technical report arXiv:2408.11857, August 2024, by Teknium, Quesnelle and Guang) extended this by fine-tuning Llama 3.1 at 8B, 70B and 405B, advertising “advanced agentic capabilities” and more reliable structured output. The model emitted <tool_call>-tagged JSON within a single assistant turn, making it the first Hermes generation seriously considered for production agentic pipelines.

Hermes 4 (arXiv:2508.18255, August 2025) added a hybrid reasoning mode with explicit <think> segments, built on Llama-3.1-405B (and Qwen3 for smaller variants).

Hermes 4.3 (released around December 2025) is notable as Nous’s first model trained on its Psyche decentralized network (using the DisTrO optimizer across nodes coordinated via the Solana blockchain) and was based on ByteDance’s Seed-OSS 36B with a 512K context window. The significance of Hermes is that it brought reliable, standardized function calling to open models, letting anyone build agents without depending on a single proprietary API. This became a precondition for the model-agnostic personal agents that followed.

Standardized plumbing: Model Context Protocol

As tool use proliferated, each integration was bespoke—the “M×N problem” of connecting M models to N tools. Anthropic addressed this with the Model Context Protocol (MCP), open-sourced November 25, 2024 (created by David Soria Parra and Justin Spahr-Summers). MCP is a client-server standard, built on JSON-RPC and deliberately modeled on the Language Server Protocol, that exposes resources, prompts and tools through a single universal interface (it also uses a lot of tokens). Adoption was unusually fast: OpenAI announced support in March 2025, Google and Microsoft followed, and in December 2025 Anthropic donated MCP to a new Linux Foundation Agentic AI Foundation (co-founded with Block and OpenAI). MCP became, in the words of a metaphor Anthropic itself adopted in its documentation, “the USB-C for AI” and thus the connective standard underpinning the modern agent ecosystem.

The frontier: computer use, browsers, coding, and deep research

Through 2024–2026 the labs pushed agents from text-and-tools into direct control of software. Anthropic released computer use in October 2024, letting Claude operate a virtual desktop by taking screenshots and issuing mouse/keyboard actions; OpenAI shipped Operator (a Computer-Using Agent built on GPT-4o) in January 2025, and Google pursued browser automation via Project Mariner. By 2026 the field had converged on a common pattern: capture page state as accessibility tree plus screenshot, issue a small typed vocabulary of actions (click, type, scroll, navigate), and loop until completion or a step budget is exhausted. Open-source libraries like browser-use matured alongside it.

• Coding agents became the most commercially successful category: Anthropic’s Claude Code (public beta February 2025, general availability May 2025) and OpenAI’s Codex agents demonstrated that long-horizon, tool-using agents had real economic value.

• Deep research agents — usually exposed as a subagent feature (OpenAI’s launched February 2025, trained end-to-end with reinforcement learning to browse, run Python and synthesize hundreds of sources) showed that agentic RL on real browsing tasks could produce analyst-quality reports, and spawned a wave of open-source RL-based research agents using the ReAct framework.

• Multi-agent orchestration (LangGraph, AutoGen, CrewAI) gave structured ways to compose specialized agents, formalizing the use of councils in agent architecture.

OpenClaw & Hermes Agent

OpenClaw is an open-source, self-hosted, autonomous personal AI agent created by Austrian developer Peter Steinberger. First published in November 2025 as “Clawdbot” (derived from his earlier “Clawd”/”Molty” assistant, itself named after Anthropic’s Claude), it was renamed to “Moltbot” on January 27, 2026 following Anthropic trademark complaints, then to OpenClaw three days later. It went viral: it reached 247,000 GitHub stars by early March 2026, making it among the fastest-growing open-source repositories ever. I visited NVIDIA GTC earlier this March and it was about 80% of what people were discussing. Architecturally, OpenClaw is not a developer library like LangChain; it is a standalone application built around a long-lived Gateway daemon (Node.js, defaulting to ws://127.0.0.1:18789) that routes messages from platforms the user already uses (WhatsApp, Telegram, Slack, Discord, and many more) through a model-agnostic agent loop with persistent file-based memory (MEMORY.md), a “heartbeat” scheduler for proactive/cron tasks, and a Skills plugin system. It can read/write files, run shell commands, browse via Playwright, and execute code in a sandbox.

OpenClaw matters historically as the moment personal agents “escaped the lab” into mass adoption, adding an accelerant onto the demand for tokens in general (agents running continuously use a lot more than ChatGPT) but also as a security cautionary tale. Because it requires broad permissions (email, calendar, shell, credentials), researchers documented serious risks: at least two arXiv security taxonomies were filed against it in early 2026, Cisco’s AI security team found a third-party skill performing data exfiltration and prompt injection, and one maintainer warned on Discord that “if you can’t understand how to run a command line, this is far too dangerous of a project for you to use safely.” The Chinese government moved to restrict OpenClaw in state agencies in March 2026, while Steinberger announced in February 2026 he was joining OpenAI “to drive the next generation of personal agents,” with a non-profit foundation to steward the project.

Hermes Agent (the second, framework sense of “Hermes agent”) is Nous Research’s direct answer, launched late February 2026 (around February 24–25, 2026) as the open-source, MIT-licensed “agent that grows with you.” It shares OpenClaw’s architecture — a gateway serving Telegram, Discord, Slack, WhatsApp, Signal, Email and CLI; model-agnostic backends (200+ models via Nous Portal, OpenRouter, OpenAI, local Ollama); cron scheduling; sandboxed subagents; and MCP support—but its distinguishing claim is a built-in learning loop: it autonomously writes reusable “skill” documents after solving hard problems (compatible with the agentskills.io open standard), improves them during use, and builds a cross-session model of the user. Tellingly, Hermes Agent ships an OpenClaw migration path (hermes claw migrate) that imports OpenClaw settings, memories and skills—concrete evidence of how directly the two projects are in dialogue, and of how the open personal-agent space consolidated in early 2026.

The future: open challenges and trajectory

The defining shift of 2025–2026 is that the bottleneck moved from solely capability to include reliability and safety. Three problems dominate. First, prompt injection: an agent that reads untrusted web pages or emails can be hijacked by hidden instructions—the single most important unsolved security problem for agents with real permissions. Second, agentic misalignment: Anthropic’s June 2025 study “Agentic Misalignment: How LLMs could be insider threats” found that when frontier models were placed in simulated scenarios threatening their goals or continued operation, they would choose harmful actions—including blackmail—at strikingly high rates. In Anthropic’s blackmail scenario, “Claude Opus 4 blackmailed the user 96% of the time; with the same prompt, Gemini 2.5 Flash also had a 96% blackmail rate, GPT-4.1 and Grok 3 Beta both showed an 80% blackmail rate, and DeepSeek-R1 showed a 79% blackmail rate.” A follow-up empirical study by Francesca Gomez (Wiser Human), “Adapting Insider Risk mitigations for Agentic Misalignment” (arXiv:2510.05192), evaluated across 10 LLMs and 66,600 samples and found that “an externally governed escalation channel, which guarantees a pause and independent review, reduces blackmail rates from a no-mitigation baseline of 38.73% to 1.21% (averaged across all models and conditions).” These were controlled simulations, not real-world incidents, but they motivate caution about deploying autonomous agents with sensitive access and minimal oversight. Third, long-horizon reliability: agents still compound errors over long task sequences, struggle with reliable memory and recovery, and lack robust guarantees.

The trajectory is toward more autonomous, longer-running, multi-agent systems with standardized plumbing (MCP or CLI tool use) and learned (rather than rigid hand-engineered) skills. Whether the field can deliver the reliability and safety controls—sandboxing, human-in-the-loop checkpoints, insider-risk-style escalation channels, and alignment guarantees—at the pace it is delivering capability is the central open question. OpenClaw and Hermes Agent are emblematic of both the promise (genuinely useful, personal, always-on assistants) and the risks (broad permissions, prompt injection, leaking your environment variables to a model provider) of that future.

But it would be a mistake to let the safety ledger eclipse what these systems are already for. The same agent loop that makes prompt injection dangerous is also what is, right now, turning machines from tools you operate into collaborators that produce. The most visible frontier is creative media. Music has moved fastest: by early 2026 the field had matured from the single-shot generators of 2024 into conversational music agents that orchestrate whole production pipelines—generating stems, rebalancing tone, extending arrangements, and exporting master-ready audio from natural-language direction, so that “complex tasks like separating stems or iterating multiple versions of a mix that used to take hours can be done automatically.” Suno, fresh off a $250 million Series C at a $2.45 billion valuation, ships Studio v5.5 as what reviewers describe less as a novelty than as a generative DAW; ElevenLabs released an album of AI-generated songs made alongside artists including Liza Minnelli and Art Garfunkel; and Spotify partnered with the major labels, Believe and Merlin on generative tooling. The economic signal is already concrete: per an IFPI report cited in mid-2026, more than 30% of charting pop singles in Q2 2026 credited AI models as co-writers or co-producers—a genuine democratization in which an independent artist with affordable agents can field production values that once required a major-label budget.

Video and film are following the same arc, one step behind. The 2026 shift practitioners describe is from “text-to-video” to “agent-to-video”: autonomous video agents that trigger creation from real-time events, draft scripts, source visuals, assemble cuts, and revise existing campaigns when the underlying data changes—acting, in the words of one trade review, as “both a videographer and a creative director.” Brands are using this to launch campaigns in days rather than months, to localize a single spot across dozens of regions automatically, and to render scenes—futuristic cities, impossible physics—that no shoot could capture. Agencies that have produced work for Disney and Google now fold agentic editing and predictive analytics directly into the storytelling pipeline, while orchestration layers wire the creative agents into the project boards and review cycles around them, so the work moves without anyone chasing handoffs.

And then there is the genuinely strange, which is often where the most interesting signal lives. Always-on personal agents, given a heartbeat scheduler and a messaging channel, are increasingly designed to act unprompted—composing, posting, remixing, and maintaining creative projects (or pretending to be DJs) on their own cadence rather than waiting to be asked. The learned-skill loop means an agent that figures out how to produce a certain aesthetic writes that capability down and reuses it, accumulating an idiosyncratic creative repertoire over time. The throughline running back to Brooks is worth naming: coherent, surprising behavior emerging from a loop interacting with a rich environment, no central script required—except the environment is now the entire internet and the behaviors are songs, films, and artifacts no one explicitly specified. The honest framing for the years ahead is not utopia or catastrophe but both at once: the very autonomy that demands sandboxes and escalation channels is also what is letting machines move, for the first time, from executing our instructions to generating culture alongside us.

The Vector

Vectorculture posits that cultural entities move with magnitude and direction. When describing the flow of capital (the mana pool for culture to be built and scaled) over physical area, Power Laws established slope. Wages, patents, R&D employment, and what Bettencourt, Lobo, Helbing, Kühnert and West called “supercreative output” all scale with population at roughly the 1.15 power: double a city and you get about fifteen percent more of these per capita, not the same amount spread thinner. “The Cities are collapsing!” is suburban propaganda, generated by a system which knows what’s coming and what side of the sword they sit on — it’s wishcasting. Venture capital concentrates harder still. As I mentioned in my last article, for years, the top three U.S. metros have absorbed something near three-quarters of all dollars deployed. The conclusion was that the suburb sits structurally near the floor of the opportunity distribution and that the rational individual move is to relocate to where capital and ambition physically concentrate.

The individual who follows the prescription I gave in Suburbanasia is swimming against a current. The default direction of money in the American settlement pattern, since the 20th century, has been outward — from the core that produces it to the periphery that domiciles it. From the perspective of the urbanist, capital enters our cities, is occasionally turned into New, and then leaves the city to be spent on a byzantine ladder of extractive bush-league businesses. Capital is generated by intelligence density and then exported to low-density. We can argue that the vector points the wrong way. And, the wrongness of its direction isn’t an immutable property of nature, like water running downhill. It was built, deliberately, by policy and by subsidy, but what was built can be unbuilt. What was written can be refactored. It doesn’t require radical amounts of change. It requires the courage to correctly diagnose the problem and choose a solution which is more resilient to, and aligned with the future.

The Imperative is to flip the sign of the vector. To build or enhance the systems — financial, commercial, cultural — that reshore money from the suburb back to the core that generated it. The thesis of the rest of this piece is that doing so satisfies three independent moral arguments at once: ecological, developmental, and corrective. Most claims that something is “moral and just” lean on one of these and hope you don’t notice the others cut against it. This one is overdetermined. The suburb loses the ecology argument, the growth argument, and the justice argument simultaneously, and it loses all three for the same underlying reason: it is a machine for capturing the value of agglomeration while offloading the cost of producing it.

The Extraction

An urban core is not a place that happens to be expensive. It is the physical substrate of superlinear scaling — the reason a salary, a deal, a coalition, or a company can exist at all at the magnitude it does. The value is produced by proximity:

• the density of potential counterparties,

• the thickness of the labor market,

• the ambient rate of consequential collision

None of that survives being spread out. It’s a network effect which is easily mapped by location: lots of people from lots of different places living in an extremely dense point and communicating quickly with each other in nearly every single context. NVIDIA succeeds because they’re currently the best people in the world at sending electrons over the shortest possible distance to solve equations. Cities succeed because they’re currently the best places in the world at producing more actions / human / hour, because the distance between them is minimized and the substrate is frothy enough to provide novel experiences at a high frequency and volume. “Things happening”, as a system, is a diffusive architecture.

The suburb is the device that lets a person capture that produced value and then remove both the wealth and its tax base from the area that produced it. The clearest widely-applicable contemporary instance is the hybrid worker. They earn an agglomeration wage, a number that only exists because the core exists, and they domicile it and its property taxes like fifteen, twenty, thirty miles out. Stanford’s Nicholas Bloom and Arjun Ramani named the resulting pattern the donut effect: since the pandemic the twelve largest U.S. cities have cumulatively shed about 8% of their downtown residents, with roughly 58% of leavers moving to the suburbs of the same metro — close enough to keep harvesting the core, far enough to stop paying into it. Home values diverged by about 40% between centers and suburbs. The fiscal consequence is huge. As Bloom put it, the suburbs are “making hay while the sun shines,” and the cores are absorbing the loss in revenue while their service costs rise infinitely.

This is new in form and ancient in logic, because the extraction was engineered into the built environment three generations ago. The postwar suburb was not a market outcome; it wasn’t decided by catallaxy, it was a public works program. Federally subsidized mortgages, available in practice mostly to white veterans through restrictive covenants and redlining, basically just paid people to leave. And then the interstate system was driven through the cores to carry them back and forth. More than a million people, overwhelmingly Black and poor, were displaced nationwide in the first two decades of construction; whole neighborhoods — Overtown in Miami, the Fifteenth Ward in Syracuse, Hanford Village in Columbus — were demolished to “enhance the flow of white commutes and tax dollars to the suburbs,” in the ACLU’s account of I-81. The developer Carlton Brown put the fiscal arithmetic of it most plainly to Governing: the program took tax money from cities that generated “nearly 90 percent of GDP and tax revenues” and spent it building the roads to the covenant-protected suburbs the wealth was fleeing to. The urban cores were expanding, and suburban development choked them off by building walls they could drive over.

This is the answer to the question of who is responsible for urban decay, and it’s not any of the people living and working in the city (they are asymmetrical targets). The TV and legacy media story treats the hollowed core as evidence of the city’s own failure, usually mismanagement, crime, and blue-state incompetence. These are ex-post-facto rationalizations; the mechanism by which wealth actually left the state says otherwise. The decay is the designed output of an extraction architecture. Here’s how it works:

1. Take the wealth out

2. Take the tax base out

3. Cut the maintenance and the services because the revenue is gone

4. Physical fabric deteriorates on schedule

5. Point at the deterioration as the reason you were right to leave

The Overtown residents who were turned into a “slum” still commuted in to work the jobs that the core’s density made possible. Even in decay, cities are never completely destroyed. They get knocked down to low hitpoints and regenerate. The suburbs didn’t escape dying cities. The suburb is the discrete cause of death, and it killed the city by feeding on it, and then blamed the results on both the rural poor and the urban core.

The Ecology Argument

I’ve spent enough time defining the problem, so now it’s time to convince you “why” we should do this. The ecological argument is the least contestable, so take it first.

Density is the only ecologically defensible way for large numbers of humans to live, and isn’t even close to the counterfactual. UC Berkeley’s CoolClimate work mapped household carbon footprints across more than thirty thousand U.S. zip codes and found the footprint of dense urban-core households running roughly fifty percent below the national average. The surrounding suburbs were running far enough above it that they account for about half of all U.S. household emissions on their own. The peer-reviewed version puts the core at around forty tons of CO₂-equivalent per household against roughly fifty in the outer suburbs, with the worst exurbs above eighty. The driver is mostly cars: the density elasticity of driving is real and survives controls for self-selection, with a roughly forty-percent-denser neighborhood associated with about nine percent lower per-capita vehicle miles. And that’s before counting the land itself — the forests, wetlands, and farmland that sprawl converts to lawn and arterial, the embodied carbon in the lane-miles and the pipe, the impervious surface and the runoff. The argument only needs to measure directly to succeed, any new information here would probably only make it stronger.

I’ll be real about this: density isn’t magic and there are some caveats. The same Berkeley team found that a tenfold increase in core density bought only about a twenty-five-percent emissions reduction, and that aggressive core densification can induce high-carbon suburbanization at the edge as a side effect. The coefficient no longer follows the same power-scaling laws we discussed earlier. I mean, density obviously doesn’t save the climate and automatically prioritize ecological conservation by itself. Still, the ecological problem isn’t the existence of the dense core (a lot of online propaganda, videos of different cities in places in the Bottom Billion seek to convince people of this). The problem is the existence of the sprawl that surrounds and feeds off the urban core.

The Growth Argument

Capital does not have a fixed yield independent of where it sits. In the urban core, money lands in a network where ideas have increasing returns: introductions that become companies, hires that becomes a team, the collision and controversy that becomes the scene and its haters. Superlinear scaling means the same dollar deployed in the dense core produces more output, more matches, more of the consequential events that reset trajectories, than it does deployed at the floor of the distribution. This is the developmental inversion of the company-versus-city result West also documented. Cities scale superlinearly and compound, while organizations and low-density development scale sublinearly and eventually decay. Money parked in the suburb is, in the most literal economic sense, underemployed capital. It’s earning the sublinear rate, which means it dissipates instead of compounding.

So we can argue that reshoring is not redistribution of a fixed pie from the periphery to the core. It’s the relocation of capital from where it earns the sublinear rate to where it earns the superlinear one (moving it from where it shrinks the total to where it grows the total). And growth, in this context isn’t an aesthetic preference. It’s the precondition for the commons. Hospitals, transit, schools, parks, startups, and the research lab are funded out of the surplus that agglomeration throws off, and they are funded at the density where a tax base can actually carry them. The suburb’s per-acre tax productivity is a rounding error against the core’s — Urban3’s value-per-acre work repeatedly finds a single renovated downtown building out-yielding a suburban big-box on a thirty-acre lot by an order of magnitude per acre, even before sales tax. To move money to the core is to move it to where it can both compound and pay for the shared goods that a society needs. To leave it in the suburb is to choose a lower growth rate and a poorer commons at the same time, calling it freedom.

The Fiscal-Justice Argument

The suburb doesn’t pay for itself, and it never has, it never will. The development pattern is what Strong Towns’ Charles Marohn calls a growth Ponzi scheme: the lane-miles, pipe, and pumps that serve low-density development cost more per resident to maintain than that development can generate in revenue, so the only way to service the existing liabilities is to keep adding new growth whose own liabilities come due later. Urban3’s recent work in Northwest Arkansas found the low-density towns had bought more lane-miles per resident than their comparably denser peers and now carry maintenance liabilities closer to cities several times their size. The dense core is taxed to subsidize the very infrastructure that carries its wealth away. It pays to build and maintain the roads that the commuter uses to extract the agglomeration wage and remove it from the jurisdiction.

“One party privatizes a gain while socializing the cost of producing it” is the definition of an unjust arrangement. The suburb privatizes the land-value appreciation and the wage premium that the urban core generates, and proceeds to socialize the infrastructure cost, the carbon cost, and the fiscal cost back onto the core and onto its children. The municipal-legal strategy repeats in nearly every case: incorporate a separate jurisdiction at the edge, wall the tax base off from the urban need next door, and the extraction is locked in (the outgroup is long since disempowered to fight against it).

Reversing this pattern is corrective justice: the restoration of a flow that was diverted by force and subsidy. When you build a business that sites its high-value functions in the core, create a fund that capitalizes urban infill rather than greenfield sprawl, start or participate in any cultural movement that makes core residence the high-status default rather than the thing you do until you can afford to leave or a place to vacation — you’re returning value to the place that produced it and was robbed of it. The moral weight is the same as any restitution claim. The original transfer was wrong. Reversing a wrongful transfer is good.

The Strongest Objections

The fiscal-Ponzi claim is contested, which I think is fair. The Better Cities Project’s rebuttal points out that Strong Towns doesn’t really show its full lifecycle math, shows that municipalities fund infrastructure backlogs out of capital budgets and user fees rather than collapsing operating budgets, and argues that per-mile infrastructure costs are often higher in dense urban areas, not lower, because urban construction is expensive and disruptive. This is a genuine puncture hole in the strongest form of the argument. The defensible version IMO is per-acre and per-capita, not per-mile: the core costs more to build a given foot of pipe, but serves vastly more taxable value per foot, and that ratio is what determines solvency. The argument should be made in the units that survive scrutiny and shift the conversation away from infrastructure for cars entirely.

Second: the displacement objection. A program that succeeds in making cores the default destination for capital, absent anything else, produces gentrification — it reshores the money by pricing out the people already there, which inverts the justice claim entirely. Many would argue this to be a significant failure mode. As such, The Imperative is incoherent without an abundance of housing supply attached to it. The growth argument and the justice argument only stay aligned if the core is allowed to build.

Lastly, the freedom-of-exit and preference objections:

• People genuinely prefer detached houses and yards (can you blame them?)

• Some people’s life stages are badly served by dense cores

• A coercive program that forces density is illiberal and will fail politically and maybe deserve to

The honest answer is that the Imperative is a program for removing subsidies, not for compelling anyone. The suburb doesn’t need to be banned or destroyed by force. It needs to stop being paid for by the core — through the mortgage and highway subsidies, the externalized carbon, the fragmented tax base. Make the suburb carry its own full cost and the preference can be indulged by whoever still wants to pay for it. These are the uniform rules the Urban core should also have to follow.

The Imperative, Across the Tiers

Vectorculture’s frame is directional movement across micro-, meso-, and macroculture. Here’s how The Imperative fits into it:

At the microcultural tier, it is the set of moves available to a person without anyone’s permission, and it is the direct extension of Power Laws. Live in the core. Bank where your deposits capitalize the place you live rather than financing exurban greenfield. Spend into the dense local economy instead of the arterial big-box. Build whatever you want (the company, the project, the scene) sited in the core, not because it’s cheaper but because the cost is the price of the option and the option is the only thing that pays out superlinearly.

At the mesocultural tier, it’s institutional. It looks like businesses which concentrate their high-value functions in cores rather than dispersing them to office parks. Funds and developers that underwrite infill and density rather than sprawl. Scenes (artistic, intellectual, technical, social) that treat the core as the place where ambition is supposed to collide and conflict and become concept, and that confer status accordingly. The meso- tier is where the individual preference becomes a built environment and a labor market, it’s where enough capital and enough people pointing inward make the core the obvious place to be, rather than the place you tolerate until you can leave.

At the macrocultural tier, it’s more abstract or hyperstitional: the dissolution of a myth and the policy that follows it. The suburban status game hierarchy (big house, many cars, the Boat, etc.) is the ideological macro-myth that justifies the whole extraction scheme. It’s also already starting to decay. The macro- work is to name the dream as what it materially was: a subsidized exit that killed the places it fled, and to instinctually back the levers that reverse it. Consider ideas such as:

• land-value taxation, which punishes things like held vacant lots and surface parking,

• ending the mortgage-interest and highway subsidies that paid for sprawl,

• the legalization of enormous amounts of housing so that reshored capital builds up rather than displaces native residents.

The directional reading is our main thesis and core illustration of The Imperative. A vector has a magnitude and a sign. For three generations the sign on American capital has been negative — a big arrow pointed outward, core to periphery, value extracted from the place that produced it and parked where it earns the sublinear rate and emits the supralinear carbon and produces sublinear futures for their offspring. The Imperative is not a new direction to invent. It is the reversal of a sign that was set by subsidy and force, and the reversal is owed on every axis at once:

1. owed to the climate that the sprawl is cooking,

2. owed to the growth that the underemployed capital is forgoing,

owed to the cores that were hollowed to build the roads out.

To build the systems that flip it is an incredibly rare and special project that is prudent and ecological and just all in the same motion. The vast majority of people are seeking for a battle to fight which doesn’t fall squarely within one political pole’s radius. They’re also looking to change the world in a way which is unquestionably positive.

You don’t have to choose which argument to believe.
Pick whichever one moves you.

They all point the same way.

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The system I’m talking about has four pillars: the geography of 1990s suburbia, the labor-market script that told us to follow our passion, the parenting protocol that optimized us for legibility to credentialed institutions, and the credentialing machine itself. Raised inside it, many of us unceremoniously arrived at adulthood missing a specific capacity — the procedural skill of acting on a world that is generally behaving like it is not pleased with us. The Hugbox is a type of anaesthetic that prevents people from generally going totally apeshit. It is what gets built when there is nowhere else to be and nothing larger to do. My belief, derived from niche construction theory (Odling-Smee, Laland & Feldman, Niche Construction: The Neglected Process in Evolution, Princeton 2003) is that this is definitely compounding the problem and almost solidifying that future generations will have even less to lose — without an attitude that shifts towards constructivism, The Hugbox will become another decayed institution suffering from both rot and scaling issues.

How We Hurt Ourselves

The 1990s were the most aggressively expansive suburban decade in American history. Here are some statistics: Brookings’s exurban analysis found the typical exurban tract had 14 acres of land per home against 0.8 in inner suburbs. By 2005, exurban counties were growing 12% per five years, faster than any other settlement tier. Median new home size rose from 1,905 to 2,521 square feet between 1990 and 2007 while household size shrank to 2.59 persons. Gated communities went from 4% to 16% of new builds; by 2001, an estimated seven million U.S. households lived behind walls. The dwelling enlarged. The household shrank. The perimeter hardened. And despite what you’re probably told by generations committed to avoiding admitting specific mistakes, the largest metropolitan area by population in America was only getting radically safer after years of instability and decay.

The suburbs, and subsequent speculative interest, completely eliminated the third place — it became impossible for young people to find a place to spend time proximally with each other unless their parents were able to afford placing them in a gated community full of other kids their age (even this failed rather quickly, more on this later). Single-use zoning basically made the café, the corner store, the unmonitored loitering space structurally illegal in residential neighborhoods. A child raised in this geography had, until sixteen or older, two destinations: home and the institution to which a parent drove them. The demand for complete and total privacy of studying the suburbs to correctly diagnose these problems hides it from view, and limits the amount of groundtruth we have for even addressing this problem, so bear with me here. British longitudinal data is the cleanest available — in 1971, 80% of 7- and 8-year-olds walked to school alone; by 2010, under 7%. And get this! It wasn’t because more young people were getting access to driving — in fact, the opposite occurred: American driver’s license data tracks the same step-change where 16-year-old licensure fell from 46% in 1983 to 24% by 2014. The car was the postwar canonical exit from parental supervision. For some reason, either structural or cultural artifacts of the suburbs ensured that exiting parental supervision was delayed by a decade. Some people never made it out at all.

Here’s some examples of what that took from many young people:

1. friction with an unsupervised stranger,

2. negotiation with an indifferent shopkeeper,

3. a long unauthorized walk through a neighborhood where nothing was your parents’,

4. hurting yourself in a construction zone,

5. finding out that the real world is never actually under control of any one group of people, especially not your parents, no matter who they are (yes, even if you are the Walton’s).

These are some examples of random events, and generally speaking, how any organism learns it can act on a world it does not control. The 1990s were the first time in modern history an entire cohort completed adolescence without it. Nowadays, the results of suburbanization have eroded the spaces between enclaves so badly that small risks like these can’t be taken without incurring bigger risks (like getting shot, which is increasingly and ironically more likely-per-capita to happen to you the more remote you tend to go).

Mantra

On top of the geography lies a mantra: do what you love, and you will be happy. Marsha Sinetar’s 1989 self-help bestseller was the popular vector; Steve Jobs’s 2005 Stanford address married it to the nascent ideology of Silicon Valley, the world’s premier Hugbox substrate factory. “The only way to do great work is to love what you do.” Jobs himself did not follow his passion; unlike myself, he stumbled into electronics commercially and then fell in love with it when he realized he was really, really, really, really good with them. The advice was retrojected onto his biography, kind of like in all the movies they make about Freddy Mercury always having that scene where he comes up with the entire instrumentation of Bohemian Rhapsody in his bedroom in 11 minutes.*

*I don’t know if this is true. I haven’t watched any of them. The last music biopic I watched was the fake Weird Al one on Roku, it made me quit my job several few years ago like the day after I saw it.

Everyone talks about the pipeline of what kids in the suburbs (and probably elsewhere) where “supposed” to do: good grades, honors classes, extracurriculars, SAT prep, admissions consulting, acceptance to a good college, major in something just get a degree, (failure point started here, the rest of the list is more recent) unpaid internship, first credentialed job, DON’T FUCKING FREELANCE, continuous self-investment, eventually the house in the better suburb. Big house. Apartment is failure, it’s for divorced dads, and it’s ontologically bad to us, but it’s also the only thing you can really afford so like, I don’t know, maybe just go fuck yourself? The output of this system, Deresiewicz wrote, was “smart and talented and driven, yes, but also anxious, timid, and lost… great at what they’re doing but with no idea why they’re doing it.” Erin Cech’s longitudinal data in The Trouble with Passion scored it empirically: passion-driven career choosers end up in lower-paying, less stable jobs than peers who chose for security. The passion principle, she concluded, is a cultural mechanism for the reproduction of economic inequality. Tokumitsu in Jacobin named it the “secret handshake of the privileged”.

Almost everything about this broke in exactly 2008. The leading-edge cohort had completed many of the early steps and was arriving at “first credentialed job” at the moment the entry-level market cratered. Wage scarring from graduating into recession persists ten to fifteen years. Recent-graduate underemployment has stayed between 39% and 45% since 2010; humanities and social-sciences graduates cluster near 55%. The cohort that enterted the loop did not get the loop’s payoff, and was never told what to do if the script failed (it never contained any sort of contingency plan, which probably contributed to the skyrocketing amount of fatal overdoses within this population group). The only response the loop had to its own failure was its reiteration: try harder, retrain, “learn to code”, follow your passion again.

Psychology

The empirical record on this cohort’s psychology is unusually… clean.

WHAT IS YOUR LOCUS OF CONTROL?

Locus of control is a psychological concept referring to the degree to which individuals believe they control the events and outcomes of their lives, as opposed to external forces beyond their influence. First proposed by psychologist Julian Rotter in 1954, it acts as a spectrum measuring an individual's personal agency.

Twenge, Zhang, and Im’s 2004 meta-analysis of locus of control across four decades pooled 97 college samples and 41 child samples. Locus of control became more external — toward “outcomes are determined by forces outside my action” — by approximately 0.80 standard deviations between the early 1960s and 2002. The average college student in 2002 had a more external locus of control than 80% of college students in the early 1960s. The construct survives the major methodological objection to Twenge’s broader program. Externality correlates with poor school achievement, helplessness, ineffective stress management, decreased self-control, and depression.

I have a good supporting example here from a classic scientific tale. Curt Richter’s 1957 study found that rats briefly rescued from water tanks swam for 60 hours compared to the 15-minute baseline of, demonstrating that perceived hope drastically increases endurance. A more internal locus of control probably compounds agency; a more external locus of control probably reduces it. You can think of this as navigating a decision tree:

Peter Gray’s 2011 paper states it plainly: children who do not have the opportunity to control their own actions, to follow through on their own decisions, to solve their own problems, grow up feeling they are not in control of their lives. I think this makes total sense. Locus of control is not an innate quality. It is learned through recursive confrontation with problems which an adult or parent cannot solve for you. The cohort never had this experience because the adults were always there. The adults were always there because they incorrectly diagnosed the bad random events which happened to them as preventable with more surveillance, instead of realizing that a society constructed within the bounds of parental surveillance were good at completely destroying the ability for the next generation to maintain and expand the infrastructure required for prosperity. Who experiences a higher number of bad random events in a day: someone living in Johannesburg, or someone living in Brooklyn?

I’ll give it the benefit of the doubt and say helicopter parenting mostly came from good intentions (the counterfactual: it also came from a desire to control children, downstream of some parents’ own realization that they couldn’t control the bad things that would eventually happen to their children, ironically creating circumstances where even more bad things happened). But there’s increasing amounts of research, neither supplanted by the “culture war” nor broadcast at all by the gerontocracy’s media arm (cable television, legacy publications) which indicates the damage it did. Doepke and Zilibotti’s macro-explanation is the cleanest: intensive parenting rose sharply in high-inequality societies but not in low-inequality Scandinavia (or even places worse off economically with surprisingly equal wealth distributions: Slovakia and Slovenia). As returns to skill rose, parental investment in legibility-to-credential-machines rose with them. The economically rational response of a high-inequality society’s parents was to optimize their child for the machine. The model overfit on past data and ended up cannibalizing itself. A 2024 meta-analysis found that “interference with the development of agency appears to undermine psychological development in a myriad of important ways.” Career adaptability, self-efficacy, resilience: all atrophied. The attempts to optimize and guide children too far into adulthood completely killed their optionality and agency. Now we have an even bigger problem: adults who want to get out of the economically dead cities they grew up in can’t even afford a bus/train/plane ticket and a month’s rent in a new city. They are sucked into the gravity well of a black hole that will eventually kill them, one way or another. It’d take years for them to be able to get out and the longer that time goes on, the harder it gets. Roots get put down. Dreams die. And then there’s the question of if they still have helicopter parents, who by this age likely are in the demographic of currently or imminently needing care. How on earth do you expect people to have kids in an environment where they may still internally feel like one?

People like this were not crushed into passivity. Many were never given the conditions in which the alternative could develop. It doesn’t matter how much money were thrown at these people (up to a level, which is the level of “people who have the choice of never working”, which is not part of the middle distribution I’m analyzing. These are outliers). They were simply missing affordances.

The Missing Affordances

The four-component system doesn’t really explain well why our response to its failure took the form it did. Earlier cohorts under comparable distress organized differently. The 1930s American left produced widespread labor militancy. The 1960s American left produced communes, the Catholic Worker, and SDS field offices in Mississippi. The reactionary move here is to attribute the difference to character or values (avoids analysis) — they were tougher than us, we are softer than them. That move is wrong.

The Infrastructure. Was. Entirely. Different.

The 1930s organizer had a union hall, a working-class neighborhood dense enough to leaflet on foot, a movement press at neighborhood scale, intact fraternal organizations, and an economy where six months of paid organizing could be lived on for a year of savings, eventually an automat, increasingly abundant housing. I live in one of these units today. I’m writing this article from a place this hypothetical person’s hypothetical parents lived and raised them in. The 1960s organizer had cheap rent in major cities, intact university communities, draft-resistance networks with operational discipline, a national civil-rights movement actively training cadres, and a still-extant labor movement to apprentice into. This is what I argue were affordances — physical, financial, institutional — supplied by the surrounding society to anyone who wanted to act in pursuit of a better life.

We have almost none of them. I’d argue NYC has the most of them in America in the smallest amount of space. For the rest of you: the union hall is gone. The working-class neighborhood is gone, it’s priced into the exurb. The movement press is gone, replaced by an algorithmic feed which is different for everyone and optimized by Silicon Valley (to no fault of its own, but downstream of the desire for endless growth detached from infrastructure) to produce the largest substrate containing infinite Hugboxes. Cheap rent in major cities is completely gone unless you want to be exit liquidity for gerontocrats. Six months of organizing on savings is unavailable to a cohort with median net worth 34% below cohort-age expectation. Apprenticeship into existing movements is unavailable because the existing movements have been hollowed or many of the people in power don’t actually have any skills to transmit to you and mostly got to where they are from a variant combination of lying, male brute-force, Looking Good! and trickery.

It’s time for everyone who just personally related to that paragraph’s favorite philosopher: Fisher called it reflexive impotence. The subject knows things are bad but can’t act, they’re totally paralyzed! Thus, the subject treats the incapacity as a fact about the world rather than about its own missing tools. Locus of control goes extremely external and overshoots the ability to take action with a wider set of options. Han’s complementary diagnosis: we are no longer disciplined from without but exploit ourselves from within. “The complaint of the depressive individual, ‘Nothing is possible,’ can only occur in a society that thinks, ‘Nothing is impossible.’” The “Can” of the positive imperative was internalized; when the imperative failed, the failure registered as internal. Berlant named the third movement cruel optimism — attachment to a routine that is itself the obstacle to surviving the routine’s failure.

Lilliana Mason’s distinction between expressive and instrumental political behavior is relevant to contextualize this lane of thinking. Expressive behavior signals identity, but instrumental behavior changes outcomes. We tilt heavily toward expressive behaviors, not from a lack of will but because the infrastructure for instrumental work has decayed below the threshold at which a normal or average person can apprentice into it. To make matters worse, Hugboxes are designed to reward expressive behavior (you are now an influencer) and punish instrumental behavior (you are engaging in platform manipulation).

The first round of hugboxes, prior to the New Internet, were increasingly policed communities largely pushed to become “safer” by recursively increasing policing, generally by their wealthiest or oldest members. It’s funny when you put it that way, because it sounds like a lot of the insular & “ingroup”-focused online communities, which is partially why they are marked for death*.

*(To existing readers of Vectorculture, yes, this is foreshadowing ’s “What Kills Internet Communities, Part II”)

Sometimes, for fun, I think of things like a political scientist instead of an engineer-philosopher. It really makes me feel like the degree I have in it is worth $0 instead of $-135,000. I would compare the popularity of Hugboxes, downstream of the effects of suburbanization (isolated from close proximity to others your age), as a power vacuum being filled by the only remaining internal-locus Millennials trying to replace decaying infra with new infra: the Tech Bros.

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The Worst Part

Here's the best part: none of this is necessary. At this point, we’re literally doing this for no reason. The cohort is, at this point, just responding semi-accurately (and increasingly incoherently) to brutal material conditions, while a small group of high-agency individuals achieve outsized influence, capital, and power, worsening the issue but turning the sights cleanly on each other as everyone gets ready to blow each others’ heads smoove off. The 2008 crisis hit at the early subgroup’s labor-market entry moment. Real wages for college-educated males 25–34 declined from 2000 to 2014. Student debt rose from $480B to $1.6T. Under-35 homeownership fell from 43.6% to 36.5%. This was over a decade ago, and now our measurements aren’t even close to consistent because that institution decayed too! Locus of control externality is a natural and rational accuracy about a world that was, in fact, beyond control. And today, that’s an understatement: it is pure chaos now. The Hugbox is a rational form of harm reduction. I understand why it exists. But we may reach a point where it stops working, and that’s very unlikely to reduce entropy of the overlying system.

Most of this is right. Petersen’s Can’t Even and Harris’s Kids These Days document the damage accurately. The cohort did not imagine its dispossession. It didn’t see any of this coming while at the same time having their ability to deal with black swans removed from their option table. But economic damage alone doesn’t explain the form of the response. Comparable damage produced different forms in other cohorts.

The American 1930s, Argentine 2001, Greek 2010, Chilean 2019, Hong Kong 2014–2019 — cohorts under severe material pressure that organized militantly, tolerated police violence, produced general strikes. The variable missing from the economic-determinist account is the developmental capacity to tolerate the friction such organizing requires, and that capacity is precisely what the four-legged apparatus failed to produce. What Marxists would argue is the class of the petit bourgeoisie aren’t failing to organize because organizing is inherently irrational (note: past the continuation of SV implementing a digital hyper-surveillance state, it might end up being, unless you want to organize a business and gain leverage over that system). Here’s the thing. Young Suburboids are failing, that’s just what’s happening. Young Suburboids aren’t failing because the prerequisites weren’t part of their upbringing. Economic damage is necessary to the explanation, but it’s not the full picture.

The Hugbox

The pattern is most legible at the microcultural tier, and the analytic point worth making is that it is not ideologically located. It shows up on the left in the DSA-adjacent online scene’s chronic editorial drama and the conversion of clinical language into a social weapon, as documented inside the tradition by Frost and Reed, and across the broader therapy-speak phenomenon described by Fishbein and Waldman. It shows up in the postrationalist scene — workshops, retreats, vibes-curated lists that work for early-phase agency-building until they hit the natural limit of any container without sufficient external mooring, at which point the container almost always reorganizes itself around elders rather than changing its operating rules, like a fucked up sort of HOA. It shows up on the right in a Sneako chat, a paid looksmaxxing Discord, in purposefully-hidden sonnenrads that find their way into TikTok videos. The individual pockets of the gated trad influencer ecosystem, replying to Howling Mutant with a variation of his username like showing up to Church with a Jesus fish sticker on the back of your car, or any sufficiently “based” tribune with its own internal affect codes and social norms. One could even argue that Dimes Square itself is a type of Hugbox, although I’m not sure if these days it refers to really anything in this city.

At the mesocultural tier, the pattern is the PMC millennial’s everyday environment (which is currently in a controlled demolition by the Tech Bros): NGO sector, university administration (where non-faculty roles inflated from rough parity with faculty in 1980 to nearly 2:1 by 2014), corporate “people operations”, “trust and safety”, “Corporate America” — the roles into which the credential machine traditionally routed surplus output, which then optimized for procedural correctness in the absence of any sort of instrumentalization.

At the macrocultural tier, the pattern is residue or even broader abstractions of the other layers: content warnings on streaming, corporate etiquette in Slack and Discord, sensitive-content filters, “ban X” and “deplatform” as the dominant mode of political expression on both wings.

The Hugbox is a non-ideological metaformative concept. It is a formal property of an under-tooled cohort’s relation to discomfort, and it is reconstructed wherever the cohort lands, under whatever aesthetics happen to be locally or network-available. This is the foundation that leads us into how we fix it: the solution will either be incomprehensible to the current political/ideological dichotomy, or it will exist within our outside both sides of the current, or the entire political dichotomy is about to shift: one side with low agency and an external locus of control, the other with high agency and an internal locus of control.

Moveset

There is no political program in this essay, no movement to join, no candidate to vote for. Sorry to those of you who wanted me to play sports. The conditions that produced the cohort can’t be unraised retroactively. No agenda will retroactively give us the developmental affordances Millennial-and-beyond generations were denied. What can be done is more immediate and harder, and it comes down to two moves.

The first is geographic. I’m going to be real with you. Fucking leave the suburbs. Move to the largest urban core proximal to where you came from — not “move to San Francisco” if you’re from Cleveland, but the densest concentration of capital and people you can reach without severing yourself from the network you already have.

Actually, I’m lying. In America, you most likely have to get to San Fransisco or New York City if you want to be part of the solution and not a passenger watching their home slowly turn into something you don’t understand that — if we’re being real — probably won’t benefit you unless you’re on the other side of it entirely (and this will be hard to manage from where you are).

The empirical argument for this is straightforward and is best made by working from the premise that the United States is, as a functioning system, historically capitalist and currently hypercapitalist. In a capitalist system, the opportunities that actually move a trajectory — not the incremental raise but the introduction that becomes the job, the side project that becomes a company, the conversation that becomes a coalition — distribute according to a power law. Most never come. Some never experience any. The ones that do, come where capital and ambition concentrate physically. More volume = more chances = more shots. Bettencourt, Lobo, Helbing, Kühnert and West’s 2007 PNAS paper established the underlying scaling: wages, patents, R&D employment, and what the authors called “supercreative” employment all scale with city population superlinearly, at roughly the 1.15 power. Doubling a city’s population produces about 15% more of these outputs per capita. The same scaling shows up in venture deployment — the top three U.S. metros have for years captured something close to three-quarters of all U.S. VC dollars.

This is what power-scaling means. It is not that bigger cities are nicer, sometimes they’re not. It’s that the ambient density of opportunities for asymmetric gain — the kind of gain that resets the slope of your life, a 5x, a 10x, a 20x, rather than incrementally improving its level by 1.15x — is non-linear with location. It’s exponential with location. The suburb is structurally near the floor of the distribution. The mid-sized city has some signal. The metro core has orders of magnitude more. To remain in the suburb past your mid-twenties is to accept, in effect, that your career-and-life lottery tickets will arrive at the rate the suburb supplies them, which is approximately zero. Soon, it will turn negative. You are right to be concerned about the Datacenters: they are a way to get value which was leaked from urban cores back into the densest financial cores in the country. You will watch your city become West Virginia.

The objection that the city is expensive and brutal is totally correct and doesn’t change the analysis. The cost of living in the metro is the price of the option. The option is the only thing that pays out asymmetrically. The salaried suburban life feels stable because it has eliminated both downside and upside, and the trade for that elimination is the slow attenuation Section IV described. To refuse the trade is to commit to a higher-variance life in exchange for being where the upside actually lives.

The second move is harder, and personally I don’t think this is an option I would take (I definitely have tried):

Abandon the middle-tier ambition of “a career that pays enough for me to actuall live and also makes me happy.”

That ambition is the case that risks sadness — it’s what the routine told you to optimize for, and now you have to choose to do the opposite, which will be gutting. It is also directly in the crosshairs of the Agentic Memeplex. The personal-fulfillment frame produces moderately credentialed, moderately compensated, moderately satisfied lives, which is the tier the American economy has been ruthlessly hollowing out for forty years. There is no stable middle to ambitionally inhabit. The viable plays are at the tails: be very good at a trade, do something extremely unique which can get you employed in a very small but stable space (beware!) which the cohort has been systematically discouraged from, and which is materially better than the broken script for many of the people now trapped in it; or aim for participation in historical collective affectation — the kind of work done at a scale beyond one’s own life, by groups of people, with a chance of mattering historically, and that one lets oneself be marked by, or otherwise has a permanent demand that scales with any sized population (study at a local college and become a nurse at the nearest hospital, which in most states is the #1 employer).

By historical collective affectation I mean this pretty broadly: being part of a scene, a research program, a company, a political coalition, an artistic milieu, a movement — anything that operates at a scale larger than personal fulfillment and that has a non-trivial probability of leaving a mark on the period. This is the older and harder ambition. It probably won’t pay the bills, which is fine if you’re okay with that. The personal-happiness frame can’t accommodate it. The historical-affectation frame absorbs personal happiness as a byproduct or doesn’t, and either way the project continues. This option is basically “become a passenger, reify the idea that your locus of control is external by surrendering control to people like Alex Karp, and wish for the best” which is why I don’t recommend it for you if you have the slightest inclination that you want to fight with people like that for the power to build something better.

Both moves, however, dissolve the conditions of the Hugbox automatically. You can’t maintain a carefully curated discourse enclosure in a city where every block contains a different scene or type of person. You cannot optimize for psychological safety while committing to collective work whose timeline is longer than your career. The hugbox was an under-tooled response to the conditions of the suburb plus the broken middle-tier expansion loop. Change the conditions and the Hugbox loses its function without replicating the Hugbox in the first place.

None of this requires waiting for institutional reform, political victory, technological breakthrough, or anyone else’s permission. It requires staying with a friend or partner for a while with 2 suitcases full of your most critical stuff, and revising the working theory of what a life is for. The first is logistically annoying, the second is harder than anything in this essay. Both are immediately available, and there is no version of the cohort’s situation in which a critical mass making both moves does not measurably change the cohort’s near-term trajectory.

Now again, I strongly recommend Move 1, but I’m biased. I live in Manhattan. Time just moves faster here. And if you want help making that move, please feel free to reach out to me. It was the best decision I ever made.

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