Its a moat for services, not models, and its only a moat for AI services as long as that compute isn’t hobbled by being used for models which are so inefficient compared to SOTA as to waste the advantage, which underlines why leaning into open source the way this piece urges is in Google’s interests, the same way open source has worked to Google and Amazon’s benefits as service providers in other domains.

(Not so much “the ability to offer free/low-cost compute” but “the advantages of scale and existing need for widely geographically dispersed compute on the cost of both marginal compute and having marginal compute close to the customer where that is relevant”, but those are pretty close to differenly-focussed rephrasings of the same underlying reality.)

Feb 24, 2023: Meta launches LLaMA, a relatively small, open-source AI model.

March 3, 2023: LLaMA is leaked to the public, spurring rapid innovation.

March 12, 2023: Artem Andreenko runs LLaMA on a Raspberry Pi, inspiring minification efforts.

March 13, 2023: Stanford's Alpaca adds instruction tuning to LLaMA, enabling low-budget fine-tuning.

March 18, 2023: Georgi Gerganov's 4-bit quantization enables LLaMA to run on a MacBook CPU.

March 19, 2023: Vicuna, a 13B model, achieves "parity" with Bard at a $300 training cost.

March 25, 2023: Nomic introduces GPT4All, an ecosystem gathering models like Vicuna at a $100 training cost.

March 28, 2023: Cerebras trains an open-source GPT-3 architecture, making the community independent of LLaMA.

March 28, 2023: LLaMA-Adapter achieves SOTA multimodal ScienceQA with 1.2M learnable parameters.

April 3, 2023: Berkeley's Koala dialogue model rivals ChatGPT in user preference at a $100 training cost.

April 15, 2023: Open Assistant releases an open-source RLHF model and dataset, making alignment more accessible.

So GP point still stands. FAAMG can run much larger immediate deficits in order to corner the market on the eventual profitability of AI.

This assumes that there is a corner-able market. Previously, the cost of training was the moat. That appears to have been more of a puddle under the gate than an actual moat.

Beyond that, they are making a lot of money from their enterprise offerings (API products, custom partnerships, etc.) with more to come soon, like ChatGPT for Business.

It will only get less and less expensive for Microsoft in terms of cost. And more and more effective for Microsoft in terms of results delivered.

How do you compete with free? That's the question. The previous internet experience has already shown us that "also be free" is not really a sustainable or even effective answer. You have to be better in some fundamental dimension.

Not to mention it's easy to run stable diffusion, but midjourney is still a good business. I can run sd on my laptop, I still pay for midjourney because it's convenient, the out of the box experience is better than any competition, and it keeps improving.

If the result here is as marginal as it seems - a few months of advantage in output quality and a slightly more sleek UI - the capital-intensive play doesn't work. The featuresets that industrial users want the most depend on having more control over the stack, not on UI or output quality. The open source models are stepping up to this goal of "cheap and custom". Casual users can play with the open models without much difficulty either, provided they take a few hours to work through an installation tutorial - UI isn't a major advantage when the whole point is that it's a magic black box.

That can be quite a barrier for entry for non-powerusers. I wouldn't underestimate serving casual users, considering that the alternative is OSS i.e. giving your shit away for free.

Creating a service that can compete with it on that regard implies you can scale GPU farms in a cost effective way.

It's not as easy as it sounds.

Meanwhile, openai still improves their product very fast, and unlike google, it's their only one. It's their baby. It has their entire focus.

Since for most consumers, AI == ChatGPT, they have the best market share right now, which mean the most user feedback to improve their product. Which they do at a fast pace.

They also understand that to get mass adoption, they need to censor the AI, like MacDonald and Disney craft their family friendly image. Which irritate every geeks, including me, but make commercially sense.

Plus, despite the fact you can torrent music and watch it with VLC, and that Amazon+Disney are competitors, netflix exists. Having a quality service has value in itself.

I would not count open ai as dead as a lot of people seem to desperately want it to be. Just because Google missed the AI train doesn't mean wishful thinking the market to be killed by FOSS is going to make it so.

As usual with those things it's impossible to know in advance what's going to happen, but odds are not disfavoring chatgpt as much as this article says.

Anthropic already does, with its models. They are same price or cheaper than OpenAI, with comparable quality.

> Having enough scale to perpetually offer free/low-cost compute is a moat.

Rather than a moat it is a growth strategy. At one point in time you need to start to monetize and this is the moment when rubber hits the road. If you can survive monetization and continue to grow, now you have a moat.