Investor Oguz O. just dropped a thread that might burst the AI hype balloon. Here’s why the numbers don’t add up—and why everyone’s arguing.
Scroll through X for five minutes and you’ll see two camps: the “AGI is months away” cheerleaders and the “we’re in a hype cycle” skeptics. This morning, one investor’s cold, hard math tipped the debate into overdrive. Let’s unpack the thread that’s lighting up timelines, timelines, and timelines.
The Thread That Started the Fire
Oguz O. kicked things off with a single post: “AI labs are burning cash faster than they can mint it.”
He laid out a simple spreadsheet. In 2023, training a frontier model cost about $100 million. Labs charged $20 a month for premium access. Revenue barely covered the electricity bill.
Fast-forward to 2025. Training costs have leapt to $1 billion. Subscription prices? Still hovering around $20–$50. The gap is canyon-sized.
Labs can’t cut spending without falling behind rivals. Raise prices and users revolt. The only escape hatch is a sudden, magical drop in compute costs—something no roadmap guarantees.
The thread exploded. Within an hour, it had 51 likes, 27 retweets, and a flood of quote tweets ranging from “he’s spot on” to “he just doesn’t get exponential curves.”
Why the Math Terrifies Investors
Venture capitalists hate uncertainty. Oguz’s numbers strip away the gloss and show a balance sheet bleeding red.
Imagine pouring a billion dollars into a model that might, maybe, earn back a few hundred million in subscriptions. That’s not a business; that’s philanthropy with extra steps.
Worse, every lab is locked in the same arms race. If one player eases off the accelerator, competitors race ahead. The result is a collective sprint toward a cliff.
Investors are quietly asking: what if the next breakthrough never arrives? What if today’s scaling laws hit a wall? The fear is palpable—and contagious.
The Ripple Effect on Jobs and Regulation
When bubbles burst, it’s not just shareholders who feel the pain.
Massive layoffs could ripple through data centers, chip fabs, and the armies of contractors labeling data. Entire cities that banked on AI campuses might watch tax revenues evaporate.
Regulators, already nervous about deepfakes and surveillance, could seize the moment to tighten the screws. Picture congressional hearings where burned investors demand safeguards against “speculative AI ventures.”
On the flip side, a crash might slow the automation wave, giving workers a breather. But that silver lining comes with a cloud: stalled innovation and missed climate or health breakthroughs.
Voices from the Battlefield
Quote-tweets reveal a community split down the middle.
One user wrote, “I cancelled my premium plan last week. The model still can’t plan my vacation without hallucinating a hotel that doesn’t exist.”
Another fired back, “You’re judging a toddler for not running a marathon. GPT-5 is months away, and the cost curve bends once photonic chips arrive.”
A third chimed in with a sobering anecdote: “My startup pivoted to AI last year. We just laid off 30% of staff because customer acquisition costs tripled. The hype sold, the product didn’t.”
Each reply is a data point in a living survey of hope versus skepticism.
What Happens Next—and What You Can Do
No one can time a bubble’s pop, but you can prepare.
If you’re an investor, diversify away from pure-play AI labs. Look for picks-and-shovels companies—chip designers, cooling systems, data-security firms—that win no matter whose logo is on the model.
If you’re a worker, double down on uniquely human skills: negotiation, empathy, creative leaps. Models can mimic, but they can’t originate.
And if you’re simply curious, keep asking hard questions. Hype thrives in silence; scrutiny keeps the dream honest.
So—are we watching the birth of superintelligence or the makings of a spectacular bust? Drop your take below, tag a friend who needs to see the numbers, and let’s keep the conversation alive.
