Is this a bit similar to what Microsoft went through in the early 2000s?
I worked at MS at this time, and I think BigG now is like MS in 2003 roughly speaking. I'm waiting for miniggl to appear, then the circle will be complete. :)
As a Xoogler I use & value the engineering chops I learned at Google every day, but I'm not naive enough to think that'll last forever. There are some really exciting developments in multiple areas of computer science - notably blockchains, Rust, GPGPU, serverless - that Google is poorly positioned to take advantage of, as well as others (machine learning, search, big data, distributed systems, capability security) that Google has historically been the market leader at but that are rapidly being commoditized by very high quality open-source projects.
Two big teams use Rust at Google in production. I guess Google didn't make the TPU or Tensor Flow as well. Take your pitch forks out but once you're done have a look at some facts.
I was at Google from 2009-2014. When I joined, Google was literally the only place you could work if you wanted to do data science on web-scale data sets. Nobody else had the infrastructure or the data. Now if you want to do Search, ElasticSearch has basically the same algorithms & data structures as Google's search server, with Dremel + some extra features thrown in. (The default ranking algorithm continues to suck, though.) If you want to do deep learning, you reach for Keras, and it'll use TensorFlow behind the scenes but with a much more fluent API. Hadoop was a major PITA to use when I joined Google; now in many ways it's easier & more robust than MapReduce, and the ecosystem has many more tools. Spark compares well with Flume. Zookeeper over Chubby. There are a number of NoSQL databases that operate on the same principle as BigTable, though I'd pick BigTable over them for robustness. Take your pick of HTML5 parsers (I even wrote and open-sourced one while I was at Google). Google was struggling mightily with headless WebKit for JS execution when I left, now you can stand up a Splash proxy in minutes or use one of the many SaaS versions. Protobufs, Bazel, gRPC, LevelDB have all been open-sourced, as have many other projects.
I mean, I first wrote a text-categorization algorithm using a k-NN algorithm about 12-13 years ago, and in order to make it run with acceptable results I only needed to manually categorize about 200 articles for each category training set. That was very doable, both in terms of time spent for constructing the training set and in terms of storage costs. Now, I have been thinking for some time to write a ML algorithm that would automatically identify the forests from present-day satellite images or from some 1950s Soviet maps (which are very good on the details). I’m pretty sure that there already is some OS code that does that, but the training set requirements I think would “kill” the project for me. I read a couple of days ago (the article was shared here in HN) about some people at Stanford implementing a ML algorithm for identifying ships included in satellite images, and I remember reading that they used 1 million high-res images as a training set. Now, for me as a hobbyist or even for a small-ish company there’s no cheap way to store that training set. Never mind the costs of labeling those 1 million training images. Otherwise I totally agree with you, we live in a golden age of AI/ML code being made available for the general public, but unfortunately is the data that makes all the difference.
Switching from KNN to DL in machine translations is impressive as a technical achievement ... but not really an innovation, and I doubt all this "innovation" impacts their bottom line in any way.
Quantity: Google has the highest number of deep learning papers accepted into top conferences among all institutions, even when papers from DeepMind are not counted in Google's.
Quality: Transformer and the recent BERT have, pun intended, transformed the entire NLP field. Batch normalization is now a staple of all neural networks, as are its descendants instance normalization, group normalization, etc.
These are just on top of my head. Google may have done many things wrong these days, but it definitely has not lost any edge in machine learning.
One of the hardest parts in many software markets is in designing incentives, making sure that the user has a reason to perform the action you want them to perform. And for startups, there's the added problem of getting users to trust that the incentives you advertise will actually hold. I might trust Stripe or Google to actually deliver the money they say they are collecting on my behalf to me because they are big established companies, but I'm certainly not going to trust a random payment processor who just started up and is advertising on a forum somewhere. But once platforms like Ethereum actually have decent UIs and reasonable transaction processing rates, you can just inspect the code of the smart contract that collects Ether (or Dai is the new payment hotness, now) from users and disburses it to the parties that were involved in producing whatever service they use.
The permissionless aspect of this whole system is very similar to the early WWW, where you could just stand up a website to do something useful and if it was good users would flock to it. That's why I'm excited. The cryptocurrency world gets a lot of bad press because a lot of the early users were quite gullible and a lot of the early use cases were in finding better ways to scam them, but there's real, fundamental technological innovation behind it.