Anyone that's been in the Amazon offices in the last few years is not likely to have witnessed a culture that fosters lab-grade innovation.
Amazon chose for itself a reputation as a place that nobody with a choice will work. I'm pleased to see that pay off.
This is a fair take. However, when it comes to warehouse automation/logistics, I constantly see what they're adopting in their hardware stack.
They're a good data point on where the robotics labor frontier is.
This is what I've heard from my friends both there and at Meta. It definitely doesn't carry the prestige it once did. It's more like, "willing to get yelled at, be afraid of being put on a PIP every day, and trying to ensure output outpaces your peers". That's sad, but it's where the industry is at so many places right now.
Was s3 the last time Amazon showed lab grade innovation? Lambda?
I could be wrong because I haven't paid much attention but Amazon has just fast followed everyone else innovation for a awhile so it's in like one ecosystem yeah?
I'm not trying to be snarky, I just haven't thought about aws as having any innovation for a while and I always figured it was because ruthless companies that churn through people as disposable commodities generally don't do tech innovation.
I'm talking on the AWS side, on the logistics side they clearly are doing insane things on supply chain perf optimization.
Your comment was likely focused on dysfunctional parts of the culture there (and that seems to be how most replies to you interpreted it as well). There's truth to that, sure. But I think respect for data sensitivity and security is also a big part of what makes the culture ill-suited for frontier AI lab work.
When I was at AWS, we had data access locked down very tightly. I was trying to train a model to predict how long various dynamic workloads running on behalf of customers would take. Just getting access to anonymized production data from a DB that my team owned took well over a month and had me compromise heavily on what attributes I could access. I also spent a decent amount of time in meetings with lawyers and compliance folks to explain and justify all our data classifications for PII, retention, access controls, etc. We would review our data policies quite frequently. We needed break-glass procedures to access prod data in our own systems, and it was uncommon to need to resort to that. Even when you broke glass, certain classes of sensitive data would still not be visible to you. On the plus side, We did get quite good at operating and maintaining systems without needing to see sensitive info.
If you contrast that with Meta's disregard for privacy or the way frontier labs disregard the law, it's easy to see why Amazon would struggle in this space.