Don’t need ads for something this useful.
(I am extremely bullish on probabilistic productivity tools)
Isn't Search ad revenue >$100Bn per year?
Isn't that >$0.07 per search?
[0] https://abc.xyz/investor/static/pdf/20210203_alphabet_10K.pd... (page 33)
The entire organization and all its products are built around ads. If a new product comes along that drastically reduces the number of pages a user views, what happens to the ad revenue?
Right now, every click, every query is an impression. But if there's an all-knowing AI answering all my questions accurately, what incentive do I, as a user, have to search again, scroll through pages, and look around multiple pages?
Google will have to adopt a radically new business model and there's organizational inertia in doing that.
The vast majority (maybe 95%?) of my searches don't show ads.
Citation?
I assume ads are a big part of Google but I suspect it’s not organized around ads.
Eg I assume the GCP teams don’t report to any ad teams.
I bet Gmail team -which does show ads- is primarily managed to optimize for the paid enterprise customers and they just have an ads guy shove ad boxes in where they can.
I bet no one at Nest reports to an ads team, and they’re organized around making money on a per-device basis instead.
Is Google good at adopting new successful business models? Ask stadia. But I bet there’s plenty of organizational clarity that alternative revenue streams are important.
Disclaimer: I don’t know the internal structure of these teams
Other than GCP, how many products can you name that are not monetized by ads?
Advertising is nearly 80% of their revenue. It has remained stubbornly near that mark despite the massive list of products they keep releasing (and shutting down early).
Large organizations tend to coagulate around their profit centers. Google isn't any more immune to it than, say, IBM.
How many people even experience ads in gmail? They aren't there on Workspace or EDU. They aren't there on gmail.com unless you are using the tabbed inbox with the Promotions tab enabled, and you visit that tab. Which, honestly, who would?
I'll be honest, I was under the impression they stopped showing ads on Gmail when I was writing this. I actually google'd it but couldn't find any news source citing that google stripped ads off gmail so I omitted to mention it.
They [don't] do it, because they have a business model. Same goes for Google. The problem for google is that apparently this other tool is already available, today, though the website is currently overloaded so I can't reach it.
But if that site starts working for me, later today, why would I ever ask Google anything again?
That's not a good analogy. There are architectural reasons why AP/fediverse will never work, no matter how hard anyone tries. It is not business reasons that prevent, say, Facebook from adopting ActivityPub. They are prevented from adopting it by critical thinking.
Rubbish, lad. These platforms manage accounts in their millions within the garden every day, and you're telling me that they can't manage to open up a hole in the API to let someone add and read posts that way, rather than through their sadistic web interfaces? After everything they've already done?
More to the point, ActivityPub is just the current popular prototype, the Bitcoin if you will, of the federated social space. We'll get it sorted just fine.
I think a Google Search Premium that cost $50/month would go over pretty poorly with Google's existing customer base (advertisers), but a startup can experiment with the business model with the right early adopters (e.g. Neeva).
I think Pathways is one approach to tackle this issue at scale by making the network sparsely activated so the computation cost can be somehow bounded based on difficulty of each query. This effectively gives Google knobs for the axis across computational cost and the result quality by limiting the size of network to be activated. If it turns out to work well, then we might be able to see it incorporated to Search in a foreseeable future.
Launching a full-fledged paid product is even harder, I guess you'll need to secure at least 3~40 headcounts just to integrate this into many subsystems inside Google. And this needs some senior executives driving the project since this is a cross-organization project between research and products. This creates a structural problem, in that they usually expect bigger impacts from these kind of projects to justify the cost. It's possible to pursue without involving top-down decision makers, but usually that kinds of project tends to fail to create consensus since everyone has different priority.
So "a separate small, experimental product" is not going to work unless 1. the model becomes fully productionized, generally available inside the company so a single VP (or even director) can quickly build a prototype to demonstrate or 2. someone successfully proposes a convincing path to the major billion user product to draw senior executive's attention or 3. the research team decides to build their own product team from scratch and aggressively invest into the sub team.
Hopefully by the time Google gets this implemented based on cost and latency metrics, we'll have better controls to hold Google accountable for doing so.
What’s funny is that Google itself started out as a “demo that interested nerds could play with” — searching your own name on the internet was so squarely a nerd move in the late 90s.
Google’s disruption here does not lie in the “ChatGPT or LLMs wil kill Search” realm. Rather, the fact that there isn’t a small-scale, dozen users product leveraging LLMs coming from Google, oh idk maybe something like Quora or Google Answers, or maybe the “Google Flights experience with Assistant”
Google did have a key product insight that you didn't need the "web portal" cruft -- just good search was enough.
Large language models like GPT are one of the biggest areas of active ML research at Google, and there's a ton of pretty obvious applications for how they can be used to answer queries, index information, etc. There is a huge budget at Google related to staffing people to work on these kinds of models and do the actual training, which is very expensive because it takes a ton of compute capacity to train these super huge language models. However what I gathered from the talk is the economics of actually using these kinds of language models in the biggest Google products (e.g. search, gmail) isn't quite there yet. It's one thing to put up a demo that interested nerds can play with, but it's quite another thing to try to integrate it deeply in a system that serves billions of requests a day when you take into account serving costs, added latency, and the fact that the average revenue on something like a Google search is close to infinitesimal already. I think I remember the presenter saying something like they'd want to reduce the costs by at least 10x before it would be feasible to integrate models like this in products like search. A 10x or even 100x improvement is obviously an attainable target in the next few years, so I think technology like this is coming in the next few years.