Leopold Aschenbrenner's "Situational Awareness"

[Skip to content](https://situational-awareness.ai/from-gpt-4-to-agi/#content)

**AGI by 2027 is strikingly plausible. GPT-2 to GPT-4 took us from ~preschooler to ~smart high-schooler abilities in 4 years. Tracing trendlines in compute (~0.5 orders of magnitude or OOMs/year), algorithmic efficiencies (~0.5 OOMs/year), and “unhobbling” gains (from chatbot to agent), we should expect another preschooler-to-high-schooler-sized qualitative jump by 2027.**

In this piece:

[Toggle](https://situational-awareness.ai/from-gpt-4-to-agi/#)

* * *

> Look. The models, they just want to learn. You have to understand this. The models, they just want to learn.
>
> _Ilya Sutskever (circa 2015,_ via [Dario Amodei](https://www.dwarkeshpatel.com/p/dario-amodei))

GPT-4’s capabilities came as a shock to many: an AI system that could write code and essays, could reason through difficult math problems, and ace college exams. A few years ago, most thought these were impenetrable walls.

But GPT-4 was merely the continuation of a decade of breakneck progress in deep learning. A decade earlier, models could barely identify simple images of cats and dogs; four years earlier, GPT-2 could barely string together semi-plausible sentences. Now we are rapidly saturating all the benchmarks we can come up with. And yet this dramatic progress has merely been the result of consistent trends in scaling up deep learning.

There have been people who have seen this for far longer. They were scoffed at, but all they did was trust the trendlines. The trendlines are intense, and they were right. The models, they just want to learn; you scale them up, and they learn more.

I make the following claim: **it is strikingly plausible that by 2027, models will be able to do the work of an AI researcher/engineer.** That doesn’t require believing in sci-fi; it just requires believing in straight lines on a graph.

![](https://situational-awareness.ai/wp-content/uploads/2024/06/base_scaleup.png)_Rough estimates of past and future scaleup of effective compute (both physical compute and algorithmic efficiencies), based on the public estimates discussed in this piece. As we scale models, they consistently get smarter, and by “counting the OOMs” we get a rough sense of what model intelligence we should expect in the (near) future. (This graph shows only the scaleup in base models; “unhobblings” are not pictured.)_

In this piece, I will simply “count the OOMs” (OOM = order of magnitude, 10x = 1 order of magnitude): look at the trends in 1) _compute_, 2) _algorithmic efficiencies_ (algorithmic progress that we can think of as growing “effective compute”), and 3) _”unhobbling” gains_ (fixing obvious ways in which models are hobbled by default, unlocking latent capabilities and giving them tools, leading to step-changes in usefulness). We trace the growth in each over four years before GPT-4, and what we should expect in the four years after, through the end of 2027. Given deep learning’s consistent impr

... (truncated, 75 KB total)