Critiquing AGI Hype with Linear Extrapolation Fallacy
Description
This meme is a screenshot of a tweet that juxtaposes a humorous, personal anecdote with a serious technological prediction to highlight a logical fallacy. The top tweet shows a father with his newborn son and then at 3 months old, joking, 'My 3-month-old son is now TWICE as big as when he was born. He's on track to weigh 7.5 trillion pounds by age 10.' Below this, a tweet from AI researcher Leopold Aschenbrenner is shown, which seriously claims, 'AGI by 2027 is strikingly plausible... it just requires believing in straight lines on a graph.' Aschenbrenner's tweet includes a logarithmic graph showing the 'Base Scaleup of Effective Compute' on an exponential trajectory. The humor and technical critique arise from the direct comparison: the top tweet uses a simple, relatable example to demonstrate the absurdity of linear extrapolation, thereby implicitly mocking the AGI prediction for relying on the same flawed reasoning. It's a sophisticated joke for a technical audience that understands the limits of models and the nuances of interpreting data, especially amidst technological hype cycles
Comments
11Comment deleted
Sure, AGI is plausible if you believe in straight lines. So is my junior dev becoming a 100x engineer by next quarter because he closed two tickets this week instead of one
If Moore’s Law really worked like that graph, my toddler’s BMI would crash the AWS billing dashboard long before GPT-7 eats the planet
Every senior engineer knows the danger of extrapolating from two data points, yet here we are watching the entire AI industry draw a straight line through GPT-2 and GPT-4 on a log scale and confidently declaring AGI by 2027 - it's like estimating project completion based on the first sprint's velocity
Ah yes, the classic 'straight line on a log scale' argument for AGI - because if my toddler's growth rate holds, he'll be filing for his own zip code by kindergarten. This is the ML equivalent of estimating your startup's Series B valuation by extrapolating your first week's user signups. Any senior engineer who's watched a promising metric plateau after initial exponential growth knows that reality has a nasty habit of introducing sigmoid curves right when your PowerPoint deck promised hockey sticks. The real question isn't whether we can draw a straight line through GPT-2, GPT-3, and GPT-4 - it's whether we're measuring the right thing, and whether the next order of magnitude in compute will hit architectural bottlenecks, data quality ceilings, or the uncomfortable realization that intelligence doesn't scale linearly with FLOPS. But hey, at least the confidence interval is honest about the uncertainty, which is more than most AI hype cycles offer
Scaling laws don't negotiate with biology or thermodynamics - baby to black hole, flops to femtosecond inference, straight line or bust
AGI by 2027? Sure - if capability scales like GPU invoices; our last straight-line forecast had the cloud bill approaching small‑moon mass by Q4
Believing AGI by 2027 because the line is straight is the same model that promotes my 3-month-old to a 7.5-trillion-pound principal engineer - spectacular R2, catastrophic error bars
If open ai weren't lazy as Mojang and were productive as Epic Games, they would make GPT10 already Comment deleted
i prefer not that big (my computer isn't good enough for large models) open-source (so i can run it on my pc) models Comment deleted
I’m afraid I will have to repost that meme about GPT few more times, lol Comment deleted
And if they would be speedrunning versioning like chromium they would have gpt162 by now? Comment deleted