This, but non-sarcastically. Google has spectacularly, so far, failed to execute on products (even of the “selling shovels” kind, much less end-user products) for generative AI, despite both having lots of consumer products to which it is naturally adaptable and a lot of the fundamental research work in generative AI.
The best explanation is that they actually are, institutionally and structurally, bad at execution in this domain, because they have all the pieces and incentives that rule out most of the other potential explanations for that.
> OpenAI bought into the field. They are good at execution but i havent seen anything novel coming out of them.
Right, OpenAI is good at execution (at least, when it comes to selling-shovels tools, I don’t see a lot of evidence beyond that yet), whereas Google is, to all current evidence, not good at execution in this space.
Then somebody else reads the papers, decides to execute on it, and hires all the researchers who are frustrated at discovering all this cool stuff but never seeing it launch.
It seems like that's what they were doing with DeepMind for the last decade. But it's also possible DeepMind as an institution lacked the pressure/product sense/leadership to produce consumable products/services. Maybe their instincts were more centered around R&D and being isolated left them somewhat directionless?
So now that AI suddenly really matters as a business, not just some indefinite future potential, Google wants to bring them inside.
They could have created a 3rd entity, their own version of OpenAI, combining DeepMind with some Google management/teams and other acquisitions and spinning it off semi-independently. But this play basically has to be from Google itself for their own reputation's sake - maybe not for practicality's sake but politically/image-wise.
Being hungry and scrappy seems to be a necessary precondition for bringing innovative products to market. If you don't naturally come from hungry & scrappy conditions (eg. Gates, Zuckerburg, Bezos, PG), being in an environment where you're surrounded by hungry & scrappy people seems to be necessary.
For that matter, a number of extremely well-resourced startups (eg Color, Juicero, WebVan, Secret, Pets.com, Theranos, WeWork) have failed in spectacular ways. Being well-resourced seems to be an anti-success criteria even for independent companies.
Cisco has done a great job balancing this, actually - they keep contact with engineers who leave to do startups, and then acquire their companies if they become successful enough to prove the product.
It seems like this is more a Google problem than a DeepMind problem though, no? Google created one of the most successful R&D labs for ML/AI research the world has ever known, then failed to have their other business units capitalize on that success. OpenAI observed this gap and swooped in to profit off all of their research outputs (with backing from Microsoft).
IMO what they’re doing here is doubling down on their mistakes: instead of disciplining their other business units for failing to take advantage of this research, they’re forcing their most productive research team to assume responsibility and correct for those failures. I expect this will go about as well as any other instance of subjecting a bunch of research scientists to internal political struggles and market discipline, i.e. very poorly.
Is this really the solution? Is there an example of a company that escaped its fate with this tactic?
I think this is what Christensen and Schumpeter suggest, but I don’t think it works.
Maybe the closest is Microsoft, but they didn’t do this. They changed their revenue model by emulating AWS.
They're also paying for their product managers' cancellation culture. (Sorry.) I'm seeing a lot of AI pitch decks; none suggest trusting Google. That saps not only network effects, but what ill term earned research: work done by others on your product. Google pays for all its research and promotion. OpenAI does not.
I thought OpenAI’s unique advantage over many big tech companies is that they’ve somehow figured out how to fast track research into product, or have researchers much more willing to worry about “production”.
I saw quotes from independent scientists referring to it as the greatest breakthrough of their lifetime, and I saw similarly strong language used in regard to the potential for good of alpha fold as a product.
So they gave it away, but it is still a product they followed through on and continue to.
Was it wrong of them that they gave it away, and right, that Microsoft’s primary intent with their open AI technology, seems to be to provoke an arms race with google?
And if it isn't? Literally every single argument I've seen towards this being AGI is "We don't know at all how intelligence works, so let's say that this is it!!!!!"
> nowhere near the game changer ChatGPT(4) is, even if ChatGPT was only available for the subset of scientists that benefit from Alpha Fold
This is utter nonsense. For anyone who actually knows a field, ChatGPT generates unhelpful, plausible-looking nonsense. Conferences are putting up ChatGPT answers about their fields to laugh at because of how misleadingly wrong they are.
This is absolutely okay, because it can be a useful tool without being the singularity. I'd sure that in a couple of years time, most of what ChatGPT achieves will be in line with most of the tech industry advances in the past decade - pushing the bottom out of the labor market and actively making the lives of the poorest worse in order to line their own pockets.
I really wish people would stop projecting hopes and wishes on top of breathless marketing.
It isn't and nobody with any experience in the field believes this. This is the Alexa / IBM Watson syndrome all over again, people are obsessed with natural language because it's relatable and it grabs the attention of laypeople.
Protein folding is a major scientific breakthrough with big implications in biology. People pay attention to ChatGPT because it recites the constitution in pirate English.
For example, it can describe concepts like risk neutral pricing and replication of derivatives but it cannot apply that logic to show how to replicate something non-trivial (i.e., not repeating well published things).
/s
Except its not, because they gave it away without any kind of commercialization. Its possible to give something away for free in some context and still have it be a product (Stable Diffusion is doing quite a bit of that, though its very unclear if they’ll be able to do it sustainably), but AlphaFold doesn’t seem to be an example. It seems to be an example of something cool they did that they had no desire to make into a product. Which is great! But isn’t the same as executing on product in a space.
Android is the biggest OS in the world
Chrome is the biggest browser in the world
Gmail is the biggest email service in the world
YouTube is the biggest video platform in the world
Google is the biggest search engine in the world
Google is the biggest digital advertiser in the world
and I'm probably missing more things they're #1 in.
Not bad for a company that has "spectacularly failed to execute on products"
Here’s the whole thing (leaving out a parenthetical that isn’t important here):
“Google has spectacularly, so far, failed to execute on products […] for generative AI”
You listed a bunch of products in other domains, some of which are the reasons why it has institutional incentives not to push generative AI forward, even if it also stands to lose more if someone else wins in it.
That's a pretty short time ago. So it seems that so far it hasn't really been a failure to execute, but more about problems with product vision or with reading the market right leading to not even attempting to have actual products in this space. That's definitely a problem, but not one that's particularly predictive of how well they'll be able to execute now that they're actually working on products.
The hardware costs alone of running something like GPT 3.5 for real time results is 6-7 figures a year. By the time you scale for user numbers and add redundancy... The infra needs to be doing useful work 24/7 to pay for itself.
It's more than possible Google knows exactly what it can do, but was waiting for it to be financially viable before acting on that. Meanwhile Microsoft has decided to throw money at it like no tomorrow - if they corner the market and it becomes financially viable before they lose that it could pay off. That is a major gamble...
Can you unpack your thinking there? Even at 5% interest for ownership costs to be six figures a year you're talking about millions of dollars in hardware. Inference is just not that expensive, not even with gigantic models.
To the extent that there is operating cost (e.g. energy)-- that isn't generated when the system is offline.
I don't know how big GPT 3.5 is, but I can _train_ LLaMA 65B on hardware at home and it is nowhere near that expensive.
Moreover, it has been quite some time since Google successfully developed and sustained a high-quality product without ultimately discontinuing it. The organizational structure at Google seems to inadvertently hinder the creation of exceptional products, exemplifying Conway's Law in practice.
Read more about this topic here: https://www.wsj.com/articles/google-ai-chatbot-bard-chatgpt-...
