It’s now almost 6 months since Google declared Code Red, and the results — Jeff Dean’s recap of 2022 achievements and a mass exodus of the top research talent that contributed to it in January, Bard’s rushed launch in Feb, a slick video showing Google Workspace AI features and confusing doubly linked blogposts about PaLM API in March, and merging Google Brain and DeepMind in April — have not been inspiring.

Google’s internal panic is in full display now with the surfacing of a well written memo, written by software engineer Luke Sernau written in early April, revealing internal distress not seen since Steve Yegge’s infamous Google Platforms Rant. Similar to 2011, the company’s response to an external challenge has been to mobilize the entire company to go all-in on a (from the outside) vague vision.

Google’s misfortunes are well understood by now, but the last paragraph of the memo: “We have no moat, and neither does OpenAI”, was a banger of a mic drop.

Combine this with news this morning that OpenAI lost $540m last year and will need as much as $100b more funding (after the complex $10b Microsoft deal in Jan), and the memo’s assertion that both Google and OpenAI have “no moat” against the mighty open source horde have gained some credibility in the past 24 hours.

Many are criticising this memo privately:

* A CEO commented to me yesterday that Luke Sernau does not seem to work in AI related parts of Google and “software engineers don’t understand moats”.

* Emad Mostaque, himself a perma-champion of open source and open models, has repeatedly stated that “Closed models will always outperform open models” because closed models can just wrap open ones.

* Emad has also commented on the moats he does see: “Unique usage data, Unique content, Unique talent, Unique product, Unique business model”, most of which Google does have, and OpenAI less so (though it is winning on the talent front)

* Sam Altman famously said that “very few to no one is Silicon Valley has a moat - not even Facebook” (implying that moats don’t actually matter, and you should spend your time thinking about more important things)

* It is not actually clear what race the memo thinks Google and OpenAI are in vs Open Source. Neither are particularly concerned about running models locally on phones, and they are perfectly happy to let “a crazy European alpha male” run the last mile for them while they build actually monetizable cloud infrastructure.

However moats are of intense interest by everybody keen on productized AI, cropping up in every Harvey, Jasper, and general AI startup vs incumbent debate. It is also interesting to take the memo at face value and discuss the searing hot pace of AI progress in open source.

We hosted this discussion yesterday with Simon Willison, who apart from being an incredible communicator also wrote a great recap of the No Moat memo. 2,800 have now tuned in on Twitter Spaces, but we have taken the audio and cleaned it up here. Enjoy!

Timestamps

* [00:00:00 ] Introducing the Google Memo

* [00:02:48 ] Open Source > Closed?

* [00:05:51 ] Running Models On Device

* [00:07:52 ] LoRA part 1

* [00:08:42 ] On Moats - Size, Data

* [00:11:34 ] Open Source Models are Comparable on Data

* [00:13:04 ] Stackable LoRA

* [00:19:44 ] The Need for Special Purpose Optimized Models

* [00:21:12 ] Modular - Mojo from Chris Lattner

* [00:23:33 ] The Promise of Language Supersets

* [00:28:44 ] Google AI Strategy

* [00:29:58 ] Zuck Releasing LLaMA

* [00:30:42 ] Google Origin Confirmed

* [00:30:57 ] Google's existential threat

* [00:32:24 ] Non-Fiction AI Safety ("y-risk")

* [00:35:17 ] Prompt Injection

* [00:36:00 ] Google vs OpenAI

* [00:41:04 ] Personal plugs: Simon and Travis

Transcripts

[00:00:00 ] Introducing the Google Memo

[00:00:00 ] Simon Willison: So, yeah, this is a document, which Kate, which I first saw at three o'clock this morning, I think. It claims to be leaked from Google. There's good reasons to believe it is leaked from Google, and to be honest, if it's not, it doesn't actually matter because the quality of the analysis, I think stands alone.

[00:00:15 ] If this was just a document by some anonymous person, I'd still think it was interesting and worth discussing. And the title of the document is We Have No Moat and neither does Open ai. And the argument it makes is that while Google and OpenAI have been competing on training bigger and bigger language models, the open source community is already starting to outrun them, given only a couple of months of really like really, really serious activity.

[00:00:41 ] You know, Facebook lama was the thing that really kicked us off. There were open source language models like Bloom before that some G P T J, and they weren't very impressive. Like nobody was really thinking that they were. Chat. G P T equivalent Facebook Lama came out in March, I think March 15th. And was the first one that really sort of showed signs of being as capable maybe as chat G P T.

[00:01:04 ] My, I don't, I think all of these models, they've been, the analysis of them has tend to be a bit hyped. Like I don't think any of them are even quite up to GT 3.5 standards yet, but they're within spitting distance in some respects. So anyway, Lama came out and then, Two weeks later Stanford Alpaca came out, which was fine tuned on top of Lama and was a massive leap forward in terms of quality.

[00:01:27 ] And then a week after that Vicuna came out, which is to this date, the the best model I've been able to run on my own hardware. I, on my mobile phone now, like, it's astonishing how little resources you need to run these things. But anyway, the the argument that this paper made, which I found very convincing is it only took open source two months to get this far.

[00:01:47 ] It's now every researcher in the world is kicking it on new, new things, but it feels like they're being there. There are problems that Google has been trying to solve that the open source models are already addressing, and really how do you compete with that, like with your, it's closed ecosystem, how are you going to beat these open models with all of this innovation going on?

[00:02:04 ] But then the most interesting argument in there is it talks about the size of models and says that maybe large isn't a competitive advantage, maybe actually a smaller model. With lots of like different people fine tuning it and having these sort of, these LoRA l o r a stackable fine tuning innovations on top of it, maybe those can move faster.

[00:02:23 ] And actually having to retrain your giant model every few months from scratch is, is way less useful than having small models that you can tr you can fine tune in a couple of hours on laptop. So it's, it's fascinating. I basically, if you haven't read this thing, you should read every word of it. It's not very long.

[00:02:40 ] It's beautifully written. Like it's, it's, I mean, If you try and find the quotable lines in it, almost every line of it's quotable. Yeah. So, yeah, that's that, that, that's the status of this

[00:02:48 ] Open Source > Closed?

[00:02:48 ] swyx: thing. That's a wonderful summary, Simon. Yeah, there