In theory the same was true about the SaaS wave and the mobile wave.

In practice, many startups have built valuable businesses around things that Google could've done, but didn't do successfully.

The same will be true in the AI wave.

In theory, FAANG-co could do it. In practice, they can't.

The exceptions to this will be the things where the winner is determined by a dimension that Google will always win on. In practice, those things are rare.

Google/MS make generic software for businesses and developers. They don't really get involved in specific verticals (aside from obvious ones like cloud, gaming, marketing). Successful start-ups will be the ones taking models from big tech cos and bringing them to the boring verticals those companies have no interest in.

Then one day Google tweaks their model in an unfortunate way and you fall all the way down that vertical.

The same can be said of any of their service offerings. You could build a business around something MS/Oracle/Google provides and they could one day decide to deprecate it.

Anything a SaaS platform provides can be replicated in-house. The same can't be said for a bleeding-edge 10 billion dimensional model.

Can you give an example?

A large majority of SaaS businesses can be done for free with an Excel spreadsheet, but many of them are still making millions a year.

Interesting. Such as?

YNAB, the budgeting app is a good example. Having it as an online app means you can use it from many computers or a phone, but at its core it's really just a spreadsheet.

Just build a product that is even slightly reliant upon customer service. They won't know how to compete.

This comment isn't getting the recognition it deserves.

> In theory, FAANG-co could do it. In practice, they can't.

It's usually more that FAANG doesn't believe in that product.

MS & Blackberry seemed pretty certain that keyboardless smartphones (the iPhone) weren't the future.

That turned out to be a really bad bet.

For Blackberry, it was way more than just the keyboard. And the whole MS-Nokia thing is a whole different story, because both screwed up really bad here, botching the potential shot at owning an Android / iOS alternative.

Or that the particular vertical just isn't big enough to warrant their focus. A $20m MRR is great for a small SaaS but isn't worth the time of any FAANG.

To add,FANGA it does not go after $100 million ideas

And, FAANG doesn't go after ideas that look like $100 million ideas that end up becoming $100B ideas.

For example? Looking at killed by google I don’t see any obvious examples

Many of these https://en.wikipedia.org/wiki/List_of_unicorn_startup_compan... could've been in theory done (to the point that there was no room for a multi-$b startup) by Google (Canva, Instacart, Telegram, Miro, Airtable), but weren't.

Your contributions to this discussion have been very thoughtful and quite interesting to read. Upvotes stopped sufficing. Thanks for adding so much!

Thanks for mentioning, I appreciate it. What was most helpful or interesting?

This is a good list, but most of these Unicorns were product of zero interest rates. The valuations are down, we just don't know how much and some of the companies on the list are already bankrupt like Voyager. Read here [1] about the astonishing rise of the unicorns in the last 3 years.

Also a billion $ unicorn is not the same as billion $ revenue.

[1] https://wolfstreet.com/2023/04/26/what-are-we-going-to-do-wi...

You wouldn't see them there. The vast majority of $100-500m ideas explored by Google are never actually turned into Beta products. They get killed during prototype testing and early alpha, because teams can get that far without meaningful VP sponsorship. But to get the investment required to scale from what a team can do with a UXer, a couple of volunteer SWEs, and a part-time PM ... you need exec support. When I was there, back in 2015-2016 we proposed several product concepts to Prabhakar that we'd already run detailed TAM analysis on, created business cases for, and tested in local markets. All of them estimated at $100-300m ARR. All of them rejected for being too small.

The plus side of allowing experimentation like this at Google, though, is that the work doesn't just go poof even after a rejection. From that core team of 5, two of them are now successful PMs, with one of them having "re-pitched" our concept of appointment booking to the Geo team as a feature addition for Google My Business. It was adopted, as was he, and that's how & why you can book appointments within Maps' business panes now. Another of them took one of our ideas into A120 and ended up mildly pivoting on the advice of the Travel VP. After some additional work, the project was adopted by Travel and is now why you get rich tour creation and destination exploration features inside of travel.google.com. A third member of our team - the UXer - maintained our team site with all the high-res mocks, business cases and pitch decks, and another one of those ideas was ultimate adopted as part of the Workspace team's investment in creating the "Gmail Hub" features that are why you have an expandable right pane with lots of app integration.

The point is not that my team was a bunch of anomalous superstars (we were not - just high achieving normal googlers), but that these kinds of things happen constantly within Google. For every idea you see on a Killed By Google list, there are probably 100 things that were killed as product concepts before release but ended up baked into one of the "15 products with >500m DAUs" Sundar referenced at I/O a couple weeks ago.

The difference in cloud hosting and gpt api costs are enormous Though no?

Faang will tariff all these innovative co

If you or anyone reading this makes a GPT powered product that has enough usage and happy users that the main challenge is API costs, email me (email in profile) - I'd be interested in funding it.

As long as there is a product that users love, and a plausible path to a moat, VCs love businesses where the only bottleneck is hosting/API costs.

right,

or giant corps will treat such startups as marketplace app creators for their LLM API and kinda "outsource" the innovation, while providing the platform and enjoying API income?

That’s assuming there is API income to be had. Assuming the “We Have No Moat” memo is accurate, the open source models may soon catch up to GPT4-level performance—and especially if inference compute requirements drop to consumer-level specs, there won’t be any money on the table.

Well there isn’t a sure way to guarantee they won’t tap into your business

Just look at Apple

They eventually will, and it is totally legitimate for them to do so.

I think it's only a matter of time until open source models catch up.

Closed source models are also improving. Not clear to me why would open source models will catch up.

I mean if GPT-4 is good enough at its current level for people to start companies on it then they will be able to do the current level with open source models at some point.

Has the fundraising environment shifted such that Google/Microsoft can more easily purchase their competitors? I'd assume raising funds has gotten harder, and that that change will make a selling more appealing.