The only reason you aren’t dead right now is that you are wrapped in a cocoon of knowledge. We aren’t “balancing” nature—we are conquering it with explanations that make multiple things work at once in spite of a larger number of problems that could kill us. Pure nature is a sequence of variables indifferent to human life: Natural Water: When nature provides water, it also provides dysentery, cholera, and parasites. In a state of pure nature, every drink is a gamble where the prize is shitting your internal organs into the dirt. Natural Water: Natural Climate: Without a grid, “seasonal beauty” is a countdown to hypothermia. Before we mastered thermodynamics, a cold snap meant burying your children because they froze to death in their sleep. Natural Climate: Natural Healthcare: Nature’s version of medicine is an infected scratch that turns into gangrene. Without the “artificial” miracle of antibiotics, a splinter or tooth abscess is a slow, agonizing death sentence. Natural Healthcare: Natural Biology: Before we “interfered” with the natural order, childbirth was a leading cause of death. “Natural” means your lineage ending in a bloody room because your biology was slightly less efficient than the microbes trying to eat you. Natural Biology: We don’t live on a “habitable planet.” We made the planet habitable. If you don’t understand that your safety is an act of defiance against reality, you’re an NPC. And so instead of being a crybaby when things don’t work out your way, figure out how to conquer. The Hypocrisy of the “Natural” Hating on AI—or any tool that extends human reach—is pathetic, self-inflicted loserdom. If you are expending food, sleep, and finite biological energy to produce less output than people using leverage, you aren’t being “authentic.” You are being an inefficient, immoral drain on the world’s potential. You are choosing to be a bottleneck in the progress of the species because you have a fetish for “the way things used to be.” It is peak delusion to watch people sit in climate-controlled rooms, wearing synthetic fabrics, using satellite-linked glass rectangles to complain about things being “unnatural.” You use technology to brush your teeth, groom your body, travel across continents, and shield your soft skin from the sun—and then you draw an arbitrary line at GMO foods, vaccines, or large language models. If “natural” is your moral North Star, throw away your toothpaste. Smash your windows. Turn off the fan. If the modern world is so “immoral” because it’s artificial, your only honest option is to move to a cave and wait for a predator to find you. But you won’t do that. You’ll stay here and LARP as a traditionalist while enjoying the 21st-century safety net that “artificial” knowledge built for you. Solving problems requires engaging with the variables of reality, not running away from them. The goal isn’t to be “natural.” The goal is to be effective. To create the knowledge that turns “impossible” variables into solved ones. Everything else is just surrogate posturing for timid people. What Multivariate Reality Actually Looks Like You’re negotiating a system of constraints where the constraints keep changing. Some variables you can see: Some variables you can see: Technical feasibility Economic viability Time to market Team capability Regulatory landscape Technical feasibility Economic viability Time to market Team capability Regulatory landscape Some variables are in tension: Some variables are in tension: Speed vs. quality Cost vs. performance Simplicity vs. functionality Short-term survival vs. long-term vision What users say they want vs. what they’ll actually pay for Speed vs. quality Cost vs. performance Simplicity vs. functionality Short-term survival vs. long-term vision What users say they want vs. what they’ll actually pay for Some variables you can’t see yet: Some variables you can’t see yet: Technologies that don’t exist Regulations that haven’t been written Competitors you don’t know about Second-order effects of your own success Black swans that invalidate your entire model Technologies that don’t exist Regulations that haven’t been written Competitors you don’t know about Second-order effects of your own success Black swans that invalidate your entire model And some variables change based on other variables: And some variables change based on other variables: Hiring more people slows you down (Brooks’s Law: adding people to a late project makes it later) Raising more money increases coordination costs and political complexity Reaching more users changes which features matter (what works at 100 users breaks at 100,000) Success in one market creates enemies in adjacent markets Your solution to problem A creates problem B, which you didn’t know existed Hiring more people slows you down (Brooks’s Law: adding people to a late project makes it later) Raising more money increases coordination costs and political complexity Reaching more users changes which features matter (what works at 100 users breaks at 100,000) Success in one market creates enemies in adjacent markets Your solution to problem A creates problem B, which you didn’t know existed You have to navigate all of this. Simultaneously. Not sequentially. Not one at a time. And you have to get all the important variables right. Not perfect. Not almost everything. Just the right variables that affect the outcome. important variables While running out of money. While your competitors are shipping. While half your team wants to quit. Impossible vs. Difficult (And Why You Can’t Tell the Difference) Impossible is not difficult. Impossible is not difficult. If you’re trying to build a machine that creates energy out of thin air, stop. You are fighting the Second Law of Thermodynamics. It is impossible. Unless you have a better explanation that supersedes thermodynamics and allows your dream to come true, trying to “innovate” such machine is foolish. But if the physics don’t forbid it, your failure to achieve it is merely a knowledge gap. Therefore you must either: 1. Uncover the knowledge to pull it off 1. Uncover the knowledge to pull it off This is the default path. Most problems are solvable if you learn enough. The Wright brothers didn’t know how propellers worked, so they built a wind tunnel and tested 200 wing shapes. Knowledge gap closed. 2. Uncover the law of physics that’s against it 2. Uncover the law of physics that’s against it Some things genuinely can’t be done. If you’re hitting a thermodynamic limit, a speed-of-light limit, or a conservation law, stop. Redirect your energy to a different problem. But document it for us all. If you’ve truly hit a hard limit of the universe, that knowledge is more valuable than your failed startup. 3. Uncover the resource constraint that makes it unfeasible—but keep your eyes peeled for when it changes 3. Uncover the resource constraint that makes it unfeasible—but keep your eyes peeled for when it changes Nuclear power was theoretically possible in 1920. But uranium enrichment required industrial infrastructure that didn’t exist. By 1945, it existed. The physics didn’t change. The knowledge required was accumulated. Electric cars were “impossible” in 1995 not because of physics, but because battery energy density was too low and cost was too high. By 2010, lithium-ion technology had crossed the threshold. This is why timing matters. This is why timing matters. Some ideas are good and just early. Your job is to know which constraint is blocking you, and whether it’s temporary or permanent. Webvan (1999) tried to do what Instacart (2012) succeeded at. Same idea. Different resource constraints. In 1999, smartphone penetration was near zero, GPS was military-only, and last-mile logistics were unsolved. By 2012, everyone had a GPS-enabled computer in their pocket. The infrastructure Webvan needed didn’t exist in 1999. It existed in 2012. The problem with most people is they can’t tell the difference between impossible and difficult, so they go work on easy things and fail, because easy is a crowded place. They quit because they’ve trapped themselves in a Static Model Error. Static Model Error. They see a multivariate problem and they mistake a temporary conflict for a permanent paradox. The Pathology of the Loop A bad model is a cognitive cage. It occurs when you define your goal using only the shitty tools inherited from your predecessors. This creates the “Logical Loop”—the classic NPC excuse for stasis. Before 1903, the best engineers in the world had mathematically “proven” that powered human flight was impossible. They weren’t idiots. They were trapped in a loop that looked like physics but was actually bad modeling. Here’s what they saw: To get a machine off the ground, you need lift. To generate lift, you need a big, powerful engine to drive the propellers fast enough. But the only powerful engines that existed—steam engines—were massive hunks of iron that weighed hundreds of pounds. So the loop went like this: more power = more weight. More weight = more lift needed. More lift needed = bigger engine required. Bigger engine = even more weight. They calculated the math and hit a wall where the weight of the engine needed to generate enough lift would be so heavy that no amount of additional power could get it off the ground. This wasn’t stupidity. Given the engines available in 1900, their math was correct. They just made one error: they assumed the engines available in 1900 were the only engines that could ever exist. they assumed the engines available in 1900 were the only engines that could ever exist. The Wright Brothers didn’t solve this by finding a “compromise” between power and weight. They built a completely new engine. They commissioned a custom aluminum engine block—almost unheard of at the time—that produced 12 horsepower while weighing only 180 pounds. The “industry standard” engine producing that much power weighed over 400 pounds. But that still wasn’t enough to crack flight. The other trap was the stability model. Every other aviation experimenter was trying to build a plane that was inherently stable—one that would automatically level itself out if it tilted, like a boat floating on water. They thought: “If the plane isn’t stable, the pilot will lose control and crash.” So they built stiff, heavy wings and tail surfaces designed to keep the aircraft level. But a perfectly stable plane can’t turn. It’s a flying brick. They were trapped between “staying level” and “actually being able to steer.” The Wright Brothers looked at this and realized everyone was using the wrong mental model. They weren’t trying to build a boat that flies. They were building a bicycle that flies. bicycle that flies. A bicycle is inherently unstable—it falls over if you’re not actively controlling it. But that’s exactly what makes it maneuverable. The pilot controls it dynamically, in real-time, making constant small adjustments. So the Wrights invented 3-axis control: wing warping (to roll left and right), a movable elevator (to pitch up and down), and a rudder (to yaw). This let them build a lighter, smaller wing because they didn’t need the extra weight of “stability structures.” The pilot would keep it stable. They didn’t find a better point on the power-weight curve. They made the curve irrelevant by solving a different problem entirely. They made the curve irrelevant by solving a different problem entirely. This Static Model Error Occurs Everywhere You See NPCs Folding Their Hands “I need a job to get experience, but I need experience to get a job.” “I need a job to get experience, but I need experience to get a job.” This only exists if you define “Experience” as “Time spent sitting in a cubicle.” If you define it as “Proof of Competence,” the loop vanishes. You can build. You can ship. You can iterate in public. A 19-year-old can build a portfolio website with 5 client projects they did for free or cheap just to get reps. That’s experience. A software engineer can contribute to open-source projects and show commit history. That’s experience. A designer can post daily work on Twitter and build a following. That’s experience. Volunteering to help a soup chicken, doing something for your community, and a billion things you could do. The wall was actually just a door you refused to turn the handle on. “I can’t start a business without money, but I can’t get money without a business.” “I can’t start a business without money, but I can’t get money without a business.” Only true if you define “business” as “thing that requires $500K in capital.” Redefine it as “thing that solves a problem people will pay for,” and the loop breaks. You can sell before you build. You can pre-sell. You can offer services before productizing them. You can bootstrap with sweat equity. Airbnb started by renting air mattresses in their own apartment and taking photos with a cheap camera. Stripe started by manually onboarding every customer and handling payments by hand. The constraint was never money. The constraint was whether you were willing to do unscalable things until you learned enough to scale. “I can’t raise funding without traction, but I can’t get traction without funding.” “I can’t raise funding without traction, but I can’t get traction without funding.” Only true if you believe the only path to traction is paid ads and a full engineering team. Redefine traction as “proof someone wants this,” and you can get it with: A landing page and email list (cost: $20/month) Cold outreach to 100 potential customers (cost: your time) A no-code MVP that barely works (cost: $0-$100) A landing page and email list (cost: $20/month) Cold outreach to 100 potential customers (cost: your time) A no-code MVP that barely works (cost: $0-$100) The loop exists because you’re using the incumbent’s definition of the variables instead of rewriting them. Ideas Don’t Matter, Execution Through Entropy Does People love to say “These companies stole my ideas” or “I thought of that years ago.” Cool. You thought of it. Mark Zuckerberg got the variables right. got the variables right. Here’s what was in 2004 for Facebook to work: 1. Broadband penetration crossed a threshold 1. Broadband penetration crossed a threshold In 1999, most people were on dial-up. Uploading photos took minutes. Myspace worked, barely, because it was designed for slow connections. By 2004, enough college students had broadband that photo-heavy profiles were feasible. 2. Digital cameras became common 2. Digital cameras became common In 1999, most people didn’t have digital cameras. By 2004, phones had cameras, and cheap digital cameras were everywhere. User-generated photos were suddenly abundant. 3. The “real identity” social model didn’t exist yet 3. The “real identity” social model didn’t exist yet Friendster (2002) and Myspace (2003) allowed pseudonyms and fake profiles. This created trust problems. Facebook’s “real names tied to college emails” was a new model. It worked because: College email verification created scarcity and authenticity College social hierarchies made people care about their online presence The walled garden (you could only see your school) made it feel safe College email verification created scarcity and authenticity College social hierarchies made people care about their online presence The walled garden (you could only see your school) made it feel safe 4. Timing of the college demographic 4. Timing of the college demographic Millennials who grew up with AIM and early social tools were hitting college age in 2004. They were digitally native enough to adopt quickly, but the general public wasn’t on social media yet, so there was still a “cool” factor to being early. 5. Platform infrastructure existed (but wasn’t commoditized yet) 5. Platform infrastructure existed (but wasn’t commoditized yet) LAMP stack (Linux, Apache, MySQL, PHP) was mature enough to build on cheaply. AWS didn’t exist yet, so scaling was still hard—but possible if you were technical. This created a moat. Non-technical founders couldn’t easily copy. Friendster had the idea earlier but couldn’t scale (technical failure). Myspace had scale but couldn’t maintain quality (moderation failure and trust failure). Google+ came later but missed the authenticity window (everyone was already on Facebook, and real identity wasn’t novel anymore). “Timing” is real, but it’s not fate. It’s a proxy for who built the best explanation given what was known. Facebook could’ve been pulled off earlier or later by harnessing other variables. The important thing is to be creative. “Timing” is real, but it’s not fate. It’s a proxy for who built the best explanation given what was known. Facebook could’ve been pulled off earlier or later by harnessing other variables. The important thing is to be creative. This is why “idea theft” is a loser’s complaint. This is why “idea theft” is a loser’s complaint. Ideas are cheap. The continuous ability to solve the next 50 problems that arise after you start is what matters. The continuous ability to solve the next 50 problems that arise after you start is what matters. Here’s what Facebook had to solve after launch: after launch: Scaling infrastructure (the site kept crashing as it grew) Moderation at scale (how do you police millions of profiles?) Monetization without destroying user experience (ads that don’t suck) Mobile transition (2007-2012, everyone moved to phones—Facebook had to rebuild everything) International expansion (different cultures, languages, regulations) Competitor threats (Twitter, Snapchat, Instagram—each required a response) Platform abuse (fake news, election interference, data privacy) and a gazillion other problems that I wouldn’t know about. Scaling infrastructure (the site kept crashing as it grew) Moderation at scale (how do you police millions of profiles?) Monetization without destroying user experience (ads that don’t suck) Mobile transition (2007-2012, everyone moved to phones—Facebook had to rebuild everything) International expansion (different cultures, languages, regulations) Competitor threats (Twitter, Snapchat, Instagram—each required a response) Platform abuse (fake news, election interference, data privacy) and a gazillion other problems that I wouldn’t know about. Each of these was a new multivariate problem. Each of these was a new multivariate problem. If Zuckerberg stopped after solving the initial “college social network” problem, Facebook would be dead. The winners aren’t the people with the best initial idea. The winners are the people who can continuously generate new solutions as new problems emerge. continuously generate new solutions as new problems emerge. Because problems don’t stop coming. And if you stop solving them, entropy will eat you. Booms, Wrappers, and the Crowd Whenever there’s a big gap of difficulty that gets breached, it creates what people call a “boom.” The internet boom happened because TCP/IP, HTTP, and broadband solved the “global connectivity” problem. Suddenly, anyone could build on top of that infrastructure. The AI boom is happening because transformers, GPUs, and massive datasets solved the “language understanding” problem. Suddenly, anyone can build on top of LLMs. This is good. This is good. When infrastructure gets solved, it creates a new realm of possibilities. A Cambrian explosion of applications. Most of them are wrappers—simple interfaces on top of the hard thing someone else built. These people are important. That’s how we generate massive use cases, create wealth, and solve problems for everyone. But because of the crowd, shitty projects slip in. They build a landing page with an OpenAI API call and call themselves “AI founders.” They charge $29/month for a ChatGPT wrapper with a custom prompt. This isn’t inherently bad. It’s just illegible. This isn’t inherently bad. It’s just illegible. Some of those wrappers will find real use cases and iterate into real businesses. Most won’t. The difference is whether they can solve the next problems that emerge, or whether they’re just playing dress-up. The test is simple: When the infrastructure changes, do you die or adapt? When the infrastructure changes, do you die or adapt? When OpenAI releases a new model that makes your wrapper obsolete, can you pivot? Or do you just tweet “they stole my idea” and fade into irrelevance? In conclusion. Your smartphone works because a large number of uncountable things didn’t kill each other. Battery chemistry that doesn’t explode. Touchscreen physics that responds to human skin. Wireless signals that penetrate concrete. Semiconductors that don’t melt. Supply chains across 47 countries. Software that doesn’t crash when you open Instagram while your GPS is running while you’re on a call. Remove any one of these. The iPhone becomes a brick. Your startup. Your research. Your invention. Your music. Your art. Your fashion. Your writing. The thing you say you’re “working on.” All of it lives or dies on whether you can hold multiple variables simultaneously while solving the problems that emerge continuously. Solve them forever. Unlock better problems. Enjoy life. Because entropy doesn’t stop. Problems don’t stop. Competitors don’t stop. And if you stop, you die. Thank you for reading. Check out my free newsletter: https://crive.substack.com
