Hard Reset
AGI: When Fever Dreams Chase Your Investment Dollars
A 23-year-old ex-OpenAI researcher just raised $1.5B predicting AGI by 2027—with zero investment experience. History shows fever dreams burn billions while real breakthroughs start small. Are we watching the next Amazon or the next Theranos?
If hype alone created value, we’d already be driving flying cars, vacationing on Mars, and paying for groceries with blockchain. Instead, we get glossy pitch decks, burned billions, and the same old human drivers behind the wheel. Now, with AGI supposedly just around the corner, the real question is: what does it mean for your money?
The Graveyard of Big Ideas
Tech history is littered with shiny visions that never took off. Billions have been burned on projects ranging from flying cars to space colonization. These grand "visions" promised the future but delivered little more than glossy pitch decks.
Below are just a few examples.
Fever Dream | Since | Billions Burned | Status |
---|
Metaverse | 2021 | $100B+ | From Wall Street darling to ghost town |
Self-Driving Cars | 2010s | $100B+ | Still needs humans behind the wheel |
Blockchain Technology | 2009 | $80B+ (VC & ICOs) | Thousands of tokens later, still searching for killer app |
Cold Fusion | 1989 | $100M+ | Consensus: impossible |
Hydrogen Economy | 1970s | $200B+ | Infrastructure still MIA |
Space Colonization | 1960s | $500B+ | A few lonely astronauts in orbit |
Nuclear Fusion | 1950s | $100B+ | “30 years away” for 70 years |
Flying Cars | 1917 | $10B+ | Still circling the runway |
The AGI Gold Rush: 23-Year-Old Prophets
Fast forward to today’s fever: Artificial General Intelligence (AGI). Enter 23-year-old Leopold Aschenbrenner. An ex-Open AI researcher who raised $1.5B for his hedge fund on the back of a 165-page paper, "Situational Awareness", predicting AGI by 2027. The kicker? He doesn't have any prior investment experience. Yet investors handed him billions as if he were some AI oracle.
His fund posted a 47% return within 6 months. Sounds impressive? Well, financial professionals (should?) know that a 6-month track record doesn't mean anything. It's also worth noting that some AI stocks like Palantir have surged 110% within the same time period. Yet, the money keeps pouring in.
And he’s not alone. Mira Murati, ex-OpenAI CTO raised $2B for a stealth startup with no disclosed product. Builder.ai raised $450M promising AI-written code, only to collapse when it turned out humans in India were doing the coding. These aren’t isolated incidents—they’re the modern equivalents of the California Gold Rush: pans, shovels, and a lot of broken dreams.
The Future Rarely Looks Like the Pitch Deck
History shows that real breakthroughs rarely emerge from hyped fever dreams. They grow from humble beginnings and years of grinding it out. Flying cars and space travel might have inspired imaginations, but they didn’t change daily life. No one predicted the true revolutions: social media, e-commerce, and the sharing economy. Nobody asked for cat memes, likes, or renting strangers’ sofas. But those turned out to be the innovations that changed the world.
Amazon began in 1994 as a humble online bookstore selling real books to real customers. It wasn’t billed as “Earth’s Biggest Everything Store” at the start. That grand empire grew gradually.
Long before Airbnb persuaded strangers to sleep in one another’s homes, it was just an idea to earn a bit of extra rent. The founders even sold novelty cereal boxes (“Obama O’s”) to keep the lights on in the early days. No fevered proclamations, just hard work and seeing things through. Today, that humble idea has become a $31 billion empire and the hallmark of the sharing economy.
Company | Humble Start | Global Impact Today |
---|
Instagram | Location check-in app (2010) | $100B+ photo-sharing giant |
Airbnb | Air mattresses for rent (2008) | $75B travel disruptor, sharing economy |
Twitter (now X) | Internal SMS tool (2006) | Digital “town square” for news |
YouTube | Dating video site (2005) | 2B+ monthly viewers, global video platform |
Facebook | Harvard directory (2004) | 3B+ global users, social media giant |
Amazon | Online bookstore (1994) | $1.7T empire across retail & cloud |
Why the Money Keeps Flowing
The answer is simple: incentives. Venture capitalists get paid whether their bets work or not. The “2 and 20” fee structure guarantees them a 2% management fee on all capital raised, plus 20% of any upside. So a $1B fund yields $20M a year—win or lose. It’s poker with other people’s money, where the house never really loses.
And whose money is on the table? Often pension funds and university endowments. When the next Theranos or Builder.ai implodes, it’s teachers’ retirements and students’ tuition that vanish. Your hard earned money, not the VCs’ personal wealth.
VC Fee | Who Pays | Effect |
---|
2% Mgmt Fee | Pension & endowments (among others) | Incentive to raise bigger funds |
20% Carry | Only on wins | Swing for the fences, hype or bust |
~1% Own Capital | VC partners | Minimal skin in the game |
Pattern Recognition: Fever Dreams vs. Breakthroughs
There’s a pattern here:
Fever Dreams | Breakthroughs |
---|
Start with utopian hype | Start with a real problem |
Billions upfront | Scrappy beginnings |
Market adoption assumed | Market validated first |
Fail spectacularly | Scale organically |
Spot the difference, and you spot the alpha.
The Last Word
AGI may one day arrive. Or not. But pouring billions into half-formed startups, stealth projects, and 23-year-old prophets looks more like fever than foresight. Real breakthroughs often look boring at first—until they quietly conquer the world.
So ask yourself: are you betting on the next Amazon—or the next fever dream? The fever dreams will keep chasing dollars. The trick is not to let them catch yours.
Hard Reset – Author Slug: Lynn Raebsamen
About the columnist
Lynn Raebsamen
European Editor · Implicator.ai
Technologist with financial expertise (CFA). Author of Artificial Stupelligence: The Hilarious Truth About AI.
A hype-skeptic who believes in technology that actually works. Based in Switzerland—and still waiting for an AI that
can finally perfect snow forecasts.
Want more of Lynn’s take on what AI can—and can’t—do?