Yet a post about AI at Google gets... No comments at all.
Back in 2005, this would have been the most talked about news of the day. Now, nobody cares what Googles up to. They lost their way.
Not my field & this could be super wrong. But my skepticism that a TPU5e launch was going to help anyone but some niche well connected other big players has been very high. I'm interested from an academic sense, and I love work like the Keras 3.0 release that makes me much more universal/portable, such that hardware like this or others can burn f-ing metal. But I don't have much trust this will actually be available at a reasonable price/with any kind of availability. https://news.ycombinator.com/item?id=38446353 https://keras.io/keras_3/
I'd love to be wrong & have this be actually available at a good price to change what is possible for people, & still interesting. Your call out is right. There's no lightning rod here, nothing to be hyoer-critical of, and few step outside the comfort zone of CUDA have anywhere near enough idea how to assess. This is an unknown on the radar for most.
Between its support for K8s, networking stack/infra, and TPUs/ML support we have a lot of huge well known tech companies knocking down our door.
But none of those things really matter as a differentiator for the average startup, scale up, or small business. So all in all, i’m not surprised the majority of HN engineers just aren’t excited by GCP.
I have no knowledge of Cloud’s long term strategy, but it wouldn’t surprise me if by leaning into its success in big tech, they are neglecting the startup scene in terms of marketing or building out new features.
Context: https://www.theregister.com/2023/11/27/google_drive_files_di...
A good LLM allows one to basically bypass the internet for many use cases and drastically reduces ad time. As cool as LLM capabilities are the thing that delights me most about them is I can get high quality (YMMV) answers from the internet without all the godforsaken ad bullshit and wonky websites made only as mazes to keep users in ad gardens.
Google is stuck in one hell of an innovators dilemma, one that I can't really see them getting unstuck from.
As far as this particular product, it's touting cost efficiency, but that's not really dominating many decisions yet. There is an all-in sprint to get models trained and take their piece of the pie before the moats get too deep and AI hardware everywhere is back-ordered 12+ months.
Until they advertise during your Chat with the LLM. The question is just when this happens not if.
But in the beginning at least, there will be high competition which will hold down the ads a bit, with enshitifcation happening after the market entrenches around the winners.
But even know, ChatGPT 4 can use search to get update-to-date info and use it's knowledge store to augment the current information.
Why do these LLM startups have such large investments and such large spending? It's because the infrastructure costs of a GenAI startup is huge. That's most of the money that's changing hands. So, from the VCs down to accelerator platforms cost efficiency is a huge decision factor on all of this.
Just by looking at their core hook, search, you notice a clear reduction in quality. Their core products, ads, are also rife with dubious practices - even more so than in the past. I doubt these are due to technical issues but rather a result of poor product management. Lesson is that no technical company should be led by non technical people. They all turn to shit when that happens.