No, I just realize these for what they are - reasonable projects at the exploitation (rather than exploration) stage of any industry.

I’d say I have an average CV in the EECS world, but also relatively humble perspective of what is and isn’t bleeding edge. And as the industry expands, the volume „inside” the bleeding edge is exploitation, while the surface is the exploration.

Waymo? Maybe; but that’s acquisition and they haven’t done much deep work since. Tensorflow is a handy and very useful DSL, but one that is shallow (builds heavily on CUDA and TPUs etc); Android is another acquisition, and rather incremental growth since; Go is a nth C-like language (so neither Dennis Richie nor Bjarne Stroustrup level work); MapReduce is a darn common concept in HPC (SGI had libraries for it in the 1990s) and implementation was pretty average. AlphaGo - another acquisition, and not much deep work since; Kubernetes is a layer over Linux Namespaces to solve - well - shallow and broad problems; Chrome/Chromium is the 4th major browser that reached dominance and essentially anyone with a 1B to spare can build one.. gVisor is another thin, shallow layer.

What I mean by deep software, is a product that requires 5-10y of work before it is useful, that touches multiple layers of software stack (ideally all from hardware to application) etc. But these types of jobs are relatively rare in the 2020s software world (pretty common in robotics and new space) - they were common in the 1990s where I got my calibration values ;) Netscape and Palm Pilot was a „whoa”. Chromium and Android are evolutions.

> No, I just realize these for what they are - reasonable projects at the exploitation (rather than exploration) stage of any industry.

I get that bashing on Google is fun, but TensorFlow was the FIRST modern end-user ML library. JAX, an optimizing backend for it, is in its own league even today. The damn thing is almost ten years old already!

Waymo is literally the only truly publicly available robotaxi company. I don't know where you get the idea that it's an acquisition; it's the spun-off incarnation of the Google self-driving car project that for years was the butt of "haha, software engineers think they're real engineers" jokes. Again, more than a decade of development on this.

Kubernetes is a refinement of Borg, which Google was using to do containerized workloads all the way back in 2003! How's that not a deep project?

True, for some definition of first and some definition of modern. I’d say it builds extremely heavily on the works inside XTX (and prior to that, XFactor etc) on general purpose linear algebra tooling, and still doesn’t change the fact that it remains shallow, even including JAX. Google TPUs change this equation a bit, as they are starting to come to fruition; but for them to reach the level of depth of NVDA, or even DEC to SUN, they’d have to actually own it from silicon to apps… and they eventually might. But the bulk of work at Google is narrow end-user projects, and they don’t have (at large) a deep engineering excellence focus.

Waymo is an acquihire from ‘05 DARPA challenges, and I’d say Tesla got there too (but with a much stricter hardware to user stack, which ought to bear fruits)

I’d say Kubernetes would be impressive compared to 1970s mainframes ;) Jokes aside, it’s a neat tool to use crappy PCs as server farms, which was sort of Google’s big insight in 2000s when everyone was buying Sun and dying with it, but that makes it not deep, at least not within Google itself.

But this may change. I think Brin recognizes this during the Code Red, and they start very heavily on building a technical moat since OpenAI was the first credible threat to the user behavior moat.

You think that Tesla, which has not accepted liability for a single driverless ride, has "gotten there?" I'm not even going to look up how many Waymo does in a month, I'm sure it's in the millions now.

Come on, man.

> Google's TPUs change this equation a bit

Google has been using TPUs to serve billions of customers for a decade. They were doing it at that scale before anyone else. They use them for training, too. I don't know why you say they don't own the stack "from silicon to apps" because THEY DO. Their kernels on their silicon to serve their apps. Their supply chain starts at TSMC or some third-party fab, exactly like NVIDIA.

Google's technical moat is a hundred miles deep, regardless of how dysfunctional it might look from the outside.

I think Theano takes the crown as first modern end-user library for autodiff and tensor operations.

Original Torch too. https://torch.ch/

Ok, that's fair.