What can we expect from large language models? [AI alignment]

Second order effects of the rise of large language models:

— Russell Kaplan (@russelljkaplan) April 10, 2022

1/ Soon, all products for creators will have embedded intelligence from massive language models (think Copilot in VSCode, DALL-E 2 in Photoshop, GPT-3 in GDocs). Companies making these products will need to roll their own massive language models or pay a tax to OpenAI/Google/etc.

2/ Over time, companies will become stratified into Compute Rich and Compute Poor. Many Compute Poor companies will become existentially dependent on the ML models of the Compute Rich.

3/ These Compute Rich companies will be the new platform gatekeepers of the coming decade. Just like Apple or FB can expel companies dependent on their ecosystems today (Epic Games, Zynga), in the future, if you lose access to your language model, your product won't function.

4/ The most serious Compute Rich companies will aggressively secure their compute supply chains: their access to chips. Like how Tesla is buying lithium mining rights, the Compute Rich companies must also ensure they can feed the ever growing hunger of their largest models.

5/ This is also why most serious AI companies are now designing their own training chips. You can either pay NVIDIA their 65% gross margins, or have each marginal dollar go ~3x as far on your inevitable billions in capex by using in-house training chips.

6/ Governments will eventually realize that having the computational infrastructure to train the largest language models is essential to national security. Within a decade we will see a new Manhattan project for AI supercomputing, that makes existing clusters look like peanuts.

8/ Generative language models will slowly replace search. Why Google something when you can get the exact answer you need, embedded in the product you’re using? We see inklings of this already with things like Copilot (https://t.co/4s6f7PBTX1). This trend has many implications.

— Russell Kaplan (@russelljkaplan) April 10, 2022

9/ Web properties with user-generated content will change their licensing terms to demand royalties when their data is used to train AI models. StackOverflow is valuable, but why would you visit it when your editor already knows the answer to your question?

10/ Instead of SEO optimization, marketers will start maximizing the log likelihood of their content being generated by an ML model. This will have unexpected consequences, like marketing data poisoning attacks (https://t.co/3WAUG2Hp1B).

— Russell Kaplan (@russelljkaplan) April 10, 2022

11/ We will also see Sponsored Outputs for language models. Advertisers will be able to pay to condition model outputs on their products. Significant research effort will one day go into v2 AdWords, now paying for likelihood that your ad is generated instead of search placement.

12/ Only thing that’s certain is: it’s gonna get weird. 

Frankly, up against these possibilities, I'm afraid that fretting about the possibility that the super-intelligent AI of the future will go rogue, that seems like a self-indulgent distraction. Look at what social media have already done.