3 ms·
Google seems to have anorexia when it comes to model intelligence. They have an internal hard constraint on price per token it seems, and they are trying to squ
by spyckie2 4mo ago
Google seems to have anorexia when it comes to model intelligence. They have an internal hard constraint on price per token it seems, and they are trying to squeeze out intelligence with limited compute.
I wonder if there is something with their TPU cycles that makes them want to postpone training a new model. My guess is that they have been on the same base model for 6 months and they may have waited for the next gen TPUs to train Gemini 4, which greatly limits how much intelligence they can increase and forces them to do cost efficiency increases.
- WarmWash 4mo agoGoogle Cloud is probably Google Deepminds biggest competitor. Big company kinda bullshit.
- platinumrad 4mo agoHow so?
- WarmWash 4mo agoGoogle cloud sells compute out from under Deepmind to other labs. So they basically are in competition with Google cloud for compute.
- logicchains 4mo agoI'd guess they did model-hardware codesign but the design ended up limiting the scaling capability of the model (i.e. they overoptimized too soon).
- JacobAsmuth 4mo agoCould it be that they have to serve their models to billions of users?
- ur-whale 4mo ago> Could it be that they have to serve their models to billions of users? And how is that different from their competitors exactly?
- inquirerGeneral 4mo ago[dead]
- JacobAsmuth 4mo agoTheir competitors aren't able to serve models as good as Gemini 3.6 Flash to billions of users
- Zigurd 4mo agoIt's interesting to see a competitive landscape shift out from under the early adopters. Anthropic appears to have anticipated this, but everyone else is going to get caught out by the major platform providers' multitude of distribution channels and use cases. Meta might also be an exceptional case if they can find a way to goose up ad effectiveness.
- zmmmmm 4mo agoIt's probably a mix of things but I do think they are viewing "edge AI" as their strategic play: on-device, small efficient models (Android / iOS) and instant AI summaries in google search etc. So all of their focus is on delivering strong performance in a compute constrained environment. I do think it's still also simultaneously true that they have an actual problem with competing with current frontier progress. It's just that has gone from an existential threat to something they are willing to defer addressing because they see the long game for them sitting at the smaller end.