You Don’t Clean the Garbage Can by Labeling the Trash

Ethan Mollick is out with a great piece called The Bitter Lesson versus The Garbage Can. He explores a decision companies face: when to build solutions that work within their existing processes, and when to let technology forge an entirely new path. Mollick shares a story about a CEO discovering how broken his organization actually was—something we've seen play out many times in our own work:

For example, a manager walked the CEO through the map, presenting him with a view he had never seen before and illustrating for him the lack of design and the disconnect between strategy and operations. The CEO, after being walked through the map, sat down, put his head on the table, and said, "This is even more fucked up than I imagined." The CEO revealed that not only was the operation of his organization out of his control but that his grasp on it was imaginary.”

The Bitter Lesson

The core of Mollick's argument builds on Rich Sutton's Bitter Lesson:

The lesson is bitter because it means that our human understanding of problems built from a lifetime of experience is not that important in solving a problem with AI. Decades of researchers' careful work encoding human expertise was ultimately less effective than just throwing more computation at the problem. We are soon going to see whether the Bitter Lesson applies widely to the world of work.

The Compute Wager

This betting-on-computation approach shows up everywhere in Silicon Valley. In one of our first articles at Alephic we wrote Pascal's Wager in AI, where we argued that today's tech giants are behaving as if the stakes of artificial intelligence are nearly infinite: if AI succeeds, the payoff is existentially large; if it fails, the sunk cost is tolerable. From the piece:

Just as Pascal argued that believing in God was a rational choice due to the potential infinite reward versus finite loss, tech companies are applying similar logic to their heavy AI investments. The wager for these hyperscalers can be broken down as follows:

The Pascalian nature of the wager can be seen in the unprecedented capital expenditures going into compute. The biggest companies in the world are betting on the Bitter Lesson—that if you look across the history of AI, scaling general‑purpose computation has outperformed hand‑tuned domain expertise. The belief is simple: more compute → better AI. The numbers are now so staggering that the spending itself feels almost religious.