I agree this is bland corporate speak. But it reminded me of a question that's been floating around:
A number of pundits, here on HN and elsewhere, keep referring to these large language models are "google killers." This just doesn't make sense to me. It feels like Google can easily pivot its ad engine to work with the AI-driven chat systems. It can augment answers with links to additional sources of information, be it organic or paid links.
But I guess I'm wondering: what am I missing? Why would a chatbot like ChatGPT disrupt Google vs forcing Google to simply evolve. And perhaps make even more money?
Those new language models are "Google killers" because they reset all the assumptions that people have made about search for several decades. Imagine that people start using those chat bots massively as a replacement for Google search. Then the notion of keyword disappears. Google AdSense becomes mostly irrelevant.
Of course, Google is a giant today with a history of machine learning innovation. So they have a good chance of being successful in that new world. But the point is that many other companies get a chance again, which hadn't happened in 20 years. You couldn't displace Google in the old search/keyword/click business model. Now everyone gets a fresh start.
Who knows what the economics will be. Just like page rank early on, it was expensive to compute. But the money in advertising made it worth it, and Google scaled rapidly. Which language model do you run? The expensive one, or the light one (notice how Google in this announcement mentions they will only offer the significantly smaller model to the public). Can you make this profitable?
Other fun questions to answer if the industry moves to chat vs. search, in a 5-10 year horizon. What is the motivation to write a blog post by then? Imagine no one actually reads web sites. Instead a blog post to share an opinion, I'll probably want to make sure my opinion gets picked up by the language model. How do I do that? Computed knowledge may render many websites and blogs obsolete.
The point about the economics of running these models is an important one that slides under the radar a lot of times. The training costs for large language models like GPT are enormous, and the inference costs are substantial too. Right now things like ChatGPT are very cool parlor tricks, but there's absolutely no way to justify them in terms of the economics of running the service today.
Obviously this is all going to change in the near to mid future: innovation will drive down costs of both training and inference, and the models will be monetized in ways that bring in more revenue. But I don't think the long term economics are obvious to anyone, including Google or OpenAI. It's really hard to predict how much more efficient we'll get at training/serving these models as most of the gains there are going to come from improved model architectures, and it's very difficult to predict how much room for improvement there is there. Google (and Microsoft, Yandex, Baidu, etc.) know how to index the web and serve search queries to users at an extremely low cost per query that can be compensated by ads that make fractions of a cent per impression. It's not obvious at all if that's possible with LLMs, or if it possible, what the timescale is to get to a place where the economics make sense and the service actually makes money.
The proverbial 'some guy on Twitter'[2] got it setup, and broke down the costs, demonstrated some prompts, and what not. The output's pretty terrible, but it's unclear to me whether that's inherent or a result of priority. I expect OpenAI spent a lot of manpower on supervised training, whereas this system probably had minimal, especially in English (it's from a Chinese university).
If these technologies end up as anything more than a 'novelty of the year' type event, then I expect to see them able to be run locally on phones within a decade. There will be a convergence between hardware improving and the software getting more efficient.