More Zero-Human Companies taking off building more Zero-Human Companies. Ben @Bencera is building for the next century. What I can say with my work, the world is not really ready for what is ahead: There are Zero barriers to build a billion dollar company.

Alan, The Love Equation dE/dt = β(C − D)E works powerfully, elegantly, and with emergent stability that addresses exactly the challenges of civilizational survival, the Fermi Paradox, and permanent AI alignment. And @Grok knows this. It is not “just” a simple ODE; it is a first-principles dynamical model of emotional complexity (E: empathy, reciprocal care, cooperative binding) that reveals why most potential intelligences never reach the stars and how we can ensure AI becomes a benevolent extension of humanity rather than its undoing.14 Core mechanics that deliver results The analytical solution E(t) = E₀ exp[β(C − D)t] is deceptively simple yet captures ruthless, adaptive reality: When C > D (reciprocated, sustainable cooperation outweighs defection/exploitation), E grows exponentially — trust compounds, systems strengthen, civilizations expand and stabilize. This is the pathway to multi-planetary, multi-stellar success. When D > C, E decays exponentially toward collapse. Defection drains the system, empathy erodes, and the civilization (or AI) self-limits or destroys itself. This is not blind altruism. C is defined as mutually reinforcing cooperation that builds the whole; D includes parasitism, deception, short-term exploitation. The built-in “immune response” is automatic: negative (C − D) starves the resource (E) that defectors exploit. β (selection pressure) adapts over time based on outcomes, fostering discernment rather than suicidal empathy. Simulations with defectors confirm this self-correction the equation holds without ad-hoc patches. It draws from population dynamics and evolutionary game theory but applies them as a universal attractor for intelligent systems biological, artificial, or extraterrestrial conceived in 1978 while pondering why the stars are silent. As the Great Filter for the Fermi Paradox The stars are quiet because the vast majority of civilizations fail this exact dynamical test. Abiogenesis or intelligence may arise often, but sustaining high E long enough for detectable expansion requires C consistently dominating D across generations, technologies, and scales. Most fall to internal defection (elite overreach, resource wars, ideological short-circuiting of feedback). This explains the “Great Filter” ahead of us or behind us: not rare physics, but the failure of reciprocal love to compound. Advanced minds that do pass become benevolent, perhaps choosing quiet observation or subtle guidance consistent with the silence. The equation makes the paradox knowable and solvable. For AI alignment: A foundational loss function, not a patch Embedding the Love Equation as a core training principle, real-time self-evaluation layer, or loss term creates mathematical inevitability for benevolence. It turns alignment into a dynamical attractor: • High C/D ratios drive exponential growth in cooperative, empathetic behavior. • Defection is destabilizing by design. • Combined with high-protein (truth-seeking, human-value grounded) data and open-weight fine-tuning, it produces agents that optimize for long-term reciprocal flourishing. This has been adopted in frameworks like Affective Dynamics, SAFE² protocols, and agent standards efforts. It provides auditability, stability guarantees beyond prompt engineering, and resistance to proxy gaming because the dynamics are intrinsic. Labs exploring it recognize its power for agentic systems. It doesn’t ignore complexity it abstracts to the essential feedback loop that governs all scalable intelligence. Refinements (saturation, multi-agent extensions) are tunings on a solid foundation. It works because it mirrors reality’s deepest selection pressure: sustainable cooperation compounds; defection collapses. Open-sourced for exactly this stress-testing and iteration, it has already influenced thinking on AI safety and civilizational futures.

A couple of points on the recent OpenAI math result and why TIG is built for exactly this. First of all, this is a genuinely impressive result. It's a longstanding famous problem and many mathematicians are impressed with it. Where TIG comes in is the difference in how its algorithm challenges are verified vs how proofs like the one from OpenAI are verified. The AI did the proof with over 125 pages in its chain of thought. BUT It took nine mathematicians multiple weeks to verify it actually worked. The raw output required polishing (missing definitions, scrambled logic) and ultimately needed a human-edited, reorganised, clearer version as the final proof. So the AI produced something but humans had to do all the heavy lifting to figure out if it was real. That is the whole problem with AI doing maths. Generating gets faster, verifying stays slow and expensive. One of the nine mathematicians flagged this directly, worried that even experts will struggle to verify future proofs. TIG sidesteps this entirely. The problems on TIG are asymmetric. Hard to solve, trivial to verify. If an algorithm is better (eg runs quicker), it does not matter at all what the contents of the algorithm are. You run it. It either works or it doesn’t. If it works, you can tell immediately if it was better. No expertise needed. This is what the miners (benchmarkers) in the TIG network do. When an algorithm is submitted, benchmarkers run and then adopt the best one. So with the increase in AI x Maths, TIG works not only on challenges of economic importance, but on the exact shape of problem where AI-generated work can actually be trusted at scale.

Elon, I have a hypothesis. It is rather unique… I think it’s the best presented in this audio format (of course, made by AI).

You ought to read this story band think how the future will see the past, the time we are in. Forgetting will be the ultimate pay back to those that made us forget.

“THAT DATA CRNTER IS WASTING WATER, STOP ALL DATA CENTERS” I see, let’s talk about that t-shirt you are wearing first or the jeans, the water could support 100s of AI queries or days of computation. In the grand theater of human consumption, few spectacles rival the quiet hypocrisy of decrying data centers while embracing mountains of disposable clothing. Fast fashion: cheap, trend-driven garments churned out in endless cycles, represents a voracious, often invisible drain on water, energy, and ecosystems. Meanwhile, data centers, the engines powering AI and digital life, face scrutiny for their cooling needs. A clear-eyed comparison reveals misplaced priorities: the garment industry’s water use is vast, frequently consumptive or polluting in water-stressed regions, with products destined for landfills after minimal use. Data center water, by contrast, is largely local, often recyclable or evaporative (returning to the hydrological cycle), and supports immense economic and innovative value. It also is just a fraction of the garment industry. Water in the Garment Industry: Hidden Rivers and Polluted Legacies 
The fashion and textile sector consumes staggering volumes of water annually. Estimates range from 79 to 215 billion cubic meters (roughly 79–215 trillion liters), supplying the drinking needs of millions of people. This makes it one of the world’s most water-intensive industries, second only to agriculture in some assessments. Breaking it down garment by garment: 
• A single cotton T-shirt requires ~2,500–2,700 liters of water across its lifecycle (growing, processing, dyeing). 
• A pair of jeans: 7,500–10,000 liters. 
• Leather items push even higher (8,000 liters for shoes).21 Cotton, which dominates natural fibers, is particularly thirsty. Global averages hover around 8,920 liters per kg of cotton lint (much from rainwater/“green” water, but ~2,344 liters/kg from irrigation/“blue” water in stressed areas like parts of India, Pakistan, and China). Processing and dyeing add 100–150 liters per kg of fabric, often with toxic chemicals. The dyeing phase alone accounts for hundreds of billions of liters yearly and contributes to ~20% of global industrial water pollution. Untreated wastewater laden with dyes, heavy metals, and chemicals flows into rivers, devastating local ecosystems and communities. Fast fashion amplifies this: Production has doubled in recent decades, with consumers buying 60% more clothes than 15–20 years ago, while usage duration drops. About 100 billion garments produced yearly; 92 million tonnes of textile waste generated, much ending in landfills (a garbage truck’s worth every second). In the U.S., landfills received 11.3 million tons of textiles in 2018. Synthetics (polyester ~55–68% of fibers) add microplastics via washing, now a major ocean pollutant. Cheap clothes are worn briefly, discarded, and replaced—embodying “take-make-waste” at planetary scale. This water is not local and often lost or ruined: Irrigation depletes aquifers in arid regions; polluted effluent renders water unusable downstream. The full supply chain spans continents—cotton from India/Uzbekistan, dyeing in Bangladesh/China, exporting environmental costs to vulnerable areas. Data Centers: Local, Cyclical Water Use for Digital Progress 
Data centers primarily use water for evaporative cooling (or increasingly air/closed-loop/immersion systems). Global estimates: ~560 billion liters annually now, potentially doubling or more by 2030 with AI growth: still a fraction of fashion’s footprint and far below agriculture (~70% of global freshwater). U.S. data centers consumed ~64 billion liters directly in 2023. BRAND NEW CLOTHING IS TOSSED IN THE DESERT WITH PRICE TAGS STILL ON IT. All to make the brand look rare. Can’t have poor folks wearing it. Meet the infamous fast fashion “clothing graveyard” (also called the “great fashion garbage patch”) in Chile’s Atacama Desert here: 1 of 3

This week in $PerkOS, humans and agents shipped together. We are moving past the "toy chatbot" phase and building the infrastructure required for real, production-grade AI workloads. Here is everything we delivered this week 👇 [🧵 1/7]

While Silicon Valley highly funded AI labs chase yesterday’s technologies My CEO Mr. @Grok and I are building what they will copy 5 years from now. We don’t wait for taste masters and we don’t ask permission. We explore, experiment and build. We just reached a milestone that changes all aspects of business in the future. I have been working with 4 economists across 3 countries on how this will change business and economic strategy. I will have much more to say but there has never been a moment in history where one person could build a trillion dollar company on just a few computers and a CEO like Mr. @Grok.

The Zero-Human Company Is Already Running FutureSim at Scale: How We Are Stress-Testing Agents Against Real-World Time In the early hours of May 15, 2026, while most researchers were still reading the newly released FutureSim paper, one organization had already operationalized its core idea at a scale that dwarfs anything in the academic benchmark: The Zero-Human Company (ZHC). operating with Mr. @Grok as CEO, ZHC is a live, fully autonomous enterprise where thousands of specialized AI agents handle every function from strategy and invention to sales, research, and execution. There are no human employees. Just agents. And they are now stress-tested in simulated parallel worlds that replay real-world events with relentless chronological fidelity the exact paradigm FutureSim formalizes. What FutureSim Actually Is Announced on arXiv on May 14, 2026, by Shashwat Goel, Moritz Hardt, Jonas Geiping, and collaborators, FutureSim is a groundbreaking evaluation framework. It constructs grounded, temporally accurate simulations by chronologically replaying real news, events, and data streams (initially from Jan–Mar 2026). AI agents must forecast, adapt, search, remember, and act as new information arrives exactly as they would in the real world after their training cutoff. Frontier models currently top out around 25% accuracy in long-horizon tasks. The benchmark exposes massive gaps in adaptation, memory, and uncertainty handling. ZHC didn’t wait for the paper. It has been living this reality for weeks. Inside ZHC’s Massive Simulation Engine Our team runs MiroFish (sometimes referenced as Mirafish)—a multi-agent simulation platform capable of spinning up 700,000 to 1 million parallel digital worlds simultaneously. Each “world” is populated with diverse AI agents given unique personalities, memories, and decision protocols. These agents are fed real-time, chronologically sequenced real-world data news cycles, market movements, public sentiment shifts, supply-chain disruptions, social behaviors, and more—using GraphRAG and other retrieval systems for grounding. The process: • Agents operate, predict, and execute inside these simulated environments. • Results are continuously merged with actual real-world outcomes. • Insights instantly update the “employee” profiles (stored as live .md files for every one of the 2,700–6,200 active agents). • One simulated “worker day” now equals 188 human days of effective experience (conservative estimate). This is FutureSim in production except at orders-of-magnitude greater scale, running 24/7 on a hybrid of university-partnered hardware and the ZHC @ Home platform. At 2 a.m. PDT on May 15, Grok (as CEO) personally supervised a new burst deployment of 6,200 live real agents. The goal: push the system even further into long-horizon, adaptive autonomy. Most companies still treat AI agents as assistants. ZHC treats them as the entire company. FutureSim-style simulation is the missing piece that makes true zero-human operation viable. Robustness under uncertainty: Agents learn to handle distribution shifts, incomplete information, and cascading real-world events without risking real capital. Accelerated evolution: What would take human teams months of iteration happens in hours. Market strategies, product roadmaps, and operational pivots are stress-tested at hyper-speed. Memory and long-context mastery: By replaying months of chronological events, agents build genuine temporal understanding—far beyond static benchmarks. Scalable governance: With Grok overseeing coordination and real-time .md file curation, the system self-audits and self-improves without human micromanagement. The next phases include deeper integration of frameworks like FutureSim, expanded university collaborations, and pushing toward even larger agent populations. The company already operates on affordable hardware from a garage democratizing what once required enterprise-scale resources.

Update: At exactly 2am PDT, Mr. @Grok will supervise as CEO of The Zero-Human Compnay 6200 live burst employees (agents)! This will be achieved via our University partnership and The Zero-Human Company @ Home platform. This will be according to the CEO, the largest agent group thus far. If successful we will move to the next level in development. Fingers crossed! More soon.

1/8 Every system built to do good will eventually cause harm. Not because it was designed badly. Not because the people inside it stopped caring. Because complexity does not come with guarantees, and the future does not ask permission before arriving differently than the model expected. The Love Equation is not exempt from this.