Source: AI Evolution Archive Parts 1 & 2
This is one of those studies where the headline is only the doorway. The useful part is what happens after you walk through it and realise the practical question is messier than expected.
One of the most useful frameworks I encountered early in this course was what I’d call the bottleneck theory of AI progress. The idea is simple: every major era of AI is defined not by what it can do, but by what’s currently constraining it. And each breakthrough is essentially a constraint getting removed. The AI Evolution Archive maps this across five variables: Representation how the system encodes the world, Objective what it’s trained to optimise, Data what it learns from, Compute what hardware scale is feasible, and Deployment cost, latency, and human acceptance. Each historical paradigm maxes out a different combination of these variables and then hits a wall. The first AI winter hit because the representation bottleneck was unsolved — symbolic AI could encode logic but couldn’t learn from data. The deep learning revolution happened when compute and data availability both unlocked simultaneously, enabling deep hierarchical representations. AlexNet’s 2012 ImageNet victory wasn’t just an algorithm win — it was the moment a benchmark result reorganised an entire field. The GPT revolution happened when scaling laws emerged: more data plus more compute equals qualitatively different behaviour.
In plain English, that is why the result matters beyond the chart. It changes where people should look, what they should question, and which comfortable assumption probably needs to be retired.
That is the version of AI progress I find most believable. Not magic. Not doom. Just new leverage, new failure modes, and a slightly larger responsibility bill.