I’m seriously thinking on how to avoid the risk of a bubble in the field, as the fact is that the industry is very heavily subsidized and it’ll get problematic once the subsidy ends. The problem is scaling laws - small models are bad, big models are smart (in terms of agentic capabilities). This is inherent to knowledge storage efficiency and at this point industry is just “distribution shaping” to find the best distribution to maximize solved problems for inference users while trying to minimize model storing useless information. Storage in the AI world means the model weights that need to be loaded in GPU and ran. AI models can be assumed to be lossy compression algorithms that learn to be more efficient in compression by learning rules in the training data. Smarter models require as such heavier infra that has a bigger fixed cost and variable cost. Smaller models are not feasible economically either, the arms race led to a price war, and in turn the price war led to margins that are either too thin or in the negative. You release a model, people use it while it’s SOTA and quickly jump ship once another model becomes SOTA. Very little moat. The tech is strong and will likely get somewhere, but I’m unsure if it’s soon. VC likely is already losing patience, so my question is as follows - how much time left before that happens and models either are forced to massively ramp up prices (30x with some usecases, 5x on average from the estimates I’ve read leads to some profitability) or the bubble pops instead of growing but at a slower pace? I’m seriously thinking if it’s worth spending time on the tech and trying to monetize it or focus my energy on something else. submitted by /u/incorporo
Originally posted by u/incorporo on r/ArtificialInteligence