The way to think about AI has always been cost/benefit. If I take the sum total impact of what AI has solved to date, and divide it by the cost, does that number look amazing or disgusting? Stated another way, do the aggregate revenues exceed the costs, and will they ever? Stated yet another way: will productivity go up or down? The Erdos solution going around today is very cool. If it feels like the model found a needle in a haystack, that's because it did. I don't say that to dismiss the accomplishment, but to put it in context. And to do that, you have to understand how the solver works. The strategy itself is actually not new, or even particularly innovative. At the heart is the same search type algorithms that have fueled advances in GO and Chess solvers. You have a wide parameter space, an objective function, and constraints. In chess, the parameter space is all of the moves that can happen in any game possible. The constraints are the size of the board and the rules. The objective is to checkmate. With enough compute, you can "play" every possible game in your head and choose the most productive moves. Moving the knight here leads to 80% winning outcomes vs 60% if I do not move the knight. This is similar to counting cards. Even similar to IBM Watson playing Jeopardy! The strategy is largely the same. What has changed is the amount of money spent on compute to solve problems. In the recent Erdos math solution, the solver is also simple. As far as I can see, the field of math is about creating lemmas (proven steps) and then combining existing lemmas to arrive at larger conclusions. Creating lemmas can be difficult, as it requires abstraction of some observable feature of the universe. Stringing lemmas together to come to a conclusion is also difficult, but in a different way. It's less about abstraction and more about search. It's a game of chess. So you train a model on every lemma that exists, as well as which lemmas can be strung together. Much like defining puzzle pieces to a puzzle with a lot of different possible valid solutions. You then define the objective (how many paths of equal length can I draw between points on a grid). Then you search through all of the possible combinations of lemmas that can optimize that number. So the most crucial aspect of this, and what appears to be somewhat hidden, is the cost of the solution. If it was $1, that is mind blowingly revolutionary. If it was $1M, that is starting to get pricy. If it was $10M , that is a pretty inefficient use of compute. If all scientific breakthroughs are worth it no matter the computational cost, then we should stop running LLMs and start running density functional theory (DFT) calculations, which determine atomic interactions from quantum mechanics. We have barely begun to even begin to fathom how much compute we would need to brute force all possible large ensemble atomic interactions over relevant time scales. But the outcomes are also potentially revolutionary: finding better materials, better drugs, better, well, everything. The benefits and the costs are infinity, and dividing the two is pretty meaningless. And herein lies the problem. The models aren't getting any more efficient, they are just getting bigger. And they cannot continue to get bigger, and more expensive, forever if we are going economically solve large problems with brute force. The only way this strategy works is if the cost of the hardware and the electricity comes down by many orders of magnitude. Renting the compute of a 1 GW datacenter for $1000 per day would be truly revolutionary for scientific discovery. Just the electricity to run that datacenter would be about $4M. The chips themselves would cost another $1-10M. So the cost of compute and energy needs to drop 5,000-15,000x. That's like buying a GB100 for $5. So the hardware and energy costs are going in the wrong direction currently for any of this to make sense. The argument that brute force search is somehow going to get cheaper in software is the big lie that AI labs are pushing to lure investors into buying more compute. The models have always just been different flavors of brute force, and the bottleneck is the cost of hardware. I have seen two trends on X lately. The first is ballmaxxing, where people inject saline into their testicles to make the pouch appear larger. The second is the discussion of paradoxes (Yes, Jevon, you have a weird name and we are sick of Satya screaming it from his goon cave). Therefore, I propose The Gingerballmaxxing Paradox (GBP): where the cost of compute proportionally scales with desire to brute force solutions, while the long term success of the brute force strategy requires the cost of compute to drop. The logical conclusion of the GBP is that productivity will continue to decline as we pursue brute force into increasing hardware prices, and we cannot see the desired compute renaissance until the entire AI investment crashes and is liquidated for next to nothing. I, for one, am looking forward to the crash, and intend to try and capture compute actually cheap enough to brute force some valuable things.