reaching asi probably requires discovering and inserting more, and stronger, rules of logic into the fine-tuning and instruction tuning steps of training

it has been found that larger data sets and more compute result in more intelligent ais. while this method has proven very effective in increasing ai intelligence so that it approaches human intelligence, because the data sets used are limited to human intelligence, ais trained on them are also limited to the strength of that intelligence. for this reason scaling will very probably yield diminishing returns, and reaching asi will probably depend much more upon discovering and inserting more, and stronger, rules of logic into the models.

another barrier to reaching asi through more compute and larger human-created data sets is that we humans often reach conclusions not based on logic, but rather on preferences, needs, desires and other emotional factors. these artifacts corrupt the data set. the only way to remove them is to subject the conclusions within human-created data sets to rigorous rules of logic testing.

another probable challenge we face when we rely solely on human-created data sets is that there may exist many more rules of logic that have not yet been discovered. a way to address this limitation is to build ais specifically designed to discover new rules of logic in ways similar to how some now discover materials, proteins, etc.

fortunately these methods will not require massive data sets or massive compute to develop and implement. with r1 and o3 we probably already have more than enough reasoning power to implement the above methods. and because the methods rely much more on strength of reasoning than on the amount of data and compute, advances in logic and reasoning that will probably get us to asi the fastest can probably be achieved with chips much less advanced than h100s.