Want a VM? You'll also need this network security group, network interface, network manager, ip, virtual network... and maybe it'll be connected to the internet so you can SSH in? Compare to GCP or EC2 -- you just pick an instance and start it. You can SSH in directly, or even do it in the browser.

Billing also a nightmare: if you're running a startup, AWS and Google make it relatively easy to see how many credits you have left. The Azure dashboard makes you navigate a maze, and the button to click that says "Azure Credits" is _invisible_ for 30s until ostensibly some backend system finds your credits, then it magically shows up. Most people don't wait around and just assume there's no button.

And if you click it, maybe you will happen to be in the correct billing profile, maybe not! Don't get confused: billing profile and billing scope are different concepts too! And in your invoice, costs just magically get deducted, until they don't. No mention of any credits. Credits inaccessible through API (claude tried everything).

VMs, bucket storage, and copying data are the _simplest_ parts of the stack. Why would anyone bother trying to use other services if they can't get these right?

They literally give startups 2x the credits as GCP, 20x the credits of AWS and nobody wants to use them.

Investment is a weird term because most people would consider keeping cash or cash equivalents (gold) to be investments, even if they don't generate wealth. Cash is also an opinion, in terms of the market.

Its price reflects that utility and like any modern asset, a lot of speculation. You can speculate on whether it's more or less useful given current events -- nothing wrong with speculating that it is only going to be increasingly useful.

    global_step = 1377; phase = continuous; lr = 5.00e-03; average_loss = 0.609497
  current tokens: ' Superman' '$MESS' '.");' '(sentence' '");' '.titleLabel' ' Republican' '?-'

    global_step = 1956; phase = continuous; lr = 5.00e-03; average_loss = 0.589661
  current tokens: ' Superman' 'marginLeft' 'iers' '.sensor' '";' '_one' '677' '».'

    global_step = 2468; phase = continuous; lr = 5.00e-03; average_loss = 0.027065
  current tokens: ' cited' '*>(' ' narrative' '_toggle' 'founder' '(V' '(len' ' pione'

    global_step = 4871; phase = continuous; lr = 5.00e-03; average_loss = 0.022909
  current tokens: ' bgcolor' '*>(' ' nomin' 'ust' ' She' 'NW' '(len' ' pione'

This was for classifying sentiment on yelp review polarity.

In terms of performance, their agents differ. The base model their agents use are the same, but for example how they look at your codebase or decide to farm tasks out to lesser models, and how they connect to tools all differ.

Would be nice to see how inference speed stacks up against say llama.cpp

This is a crippling disadvantage. Consider what it takes to evaluate a single software release for a robotaxi.

If you have a simulator, you can take long tail distribution events and just resimulate your software to see if there are regressions against those events. (Waymo, Zoox)

If you don't, or your simulator has too much error, you have to deploy your software in cars in "ghost mode" and hope that sufficient miles see rare and scary situations recur. You then need to find those specific situations and check if your software did a good job (vs just getting lucky). But what if you need to A/B test a change? What if you need to A/B test 100 changes made by different engineers? How do you ensure you're testing the right thing? (Tesla)

And if you have a simulator that _sucks_ because it doesn't have physics-grounded understanding of distances (i.e. it's based on distance estimates from camera), then you can easily trick yourself into thinking your software is doing the right thing, right up until you start killing people.

Another way to look at it is: most driving data is actually very low in signal. You want all the hard driving miles, and in high resolution, so that you can basically generate the world's best unit testing suite for the software driver. You can just throw the rest of the driving data away -- and you must, because nobody has that much storage and unit economics still matter.

This is to say nothing of the fact that differences between hardware matter too. Tesla has a bunch of car models out there, and software working well one one model may not actually work well on another.

So you _do_ agree that willingness to pay is only helpful if there is segmentation.

> Pre-Uber, we had both standard yellow cabs and black car services at different levels

There is more to the gap between yellow cab and black car than cleanliness. Stuff like service / helping you with bags, ETAs, partitions between yourself and the driver, niceness of the car itself, etc.

I'm sure we'll see segmentation along the lines of vehicle size and capability, but I expect cleanliness to be the same across those segments.

> Services often launch with non-scalable attention to detail to control the initial public impression (eating the cost), and then relax over time.

I don't think cleaning is the burden you're making it out to be. These cars return to depot when their battery is down. If you're to clean them at all, you should clean them when they return for charging, and then to your set standard. It's not a big knob for controlling costs.

But I'm saying we _don't_ have that for Waymo, and it's very unlikely to happen, for many reasons. A big reason is simply that managing a fleet in heterogeneous fashion as you're describing (different cleaning schedules for the cars) doesn't really make sense IRL. It's a purely imagined scenario on your part.

> Incorrect.

Pray tell how I can pay for a cleaner car when there's only one option, car or no car?

> No, I'm not assuming that.

Then please explain how cars would get dirtier as the service scales up? If today is already seeing the cars at full utilization, barring a cost-cutting measure that determined that cleaning less frequently would be a significant cost savings (which is a big assumption on your part), then we should be seeing roughly how clean the cars will be into perpetuity.

> Again, Uber Black.

Uber Black achieves higher standards for cleaning by farming that out to the people renting out their personal vehicles. The drivers are incentivized to clean the cars more (than UberX drivers) to get more expensive fares.

But again, fleet management companies already do this for _all_ their cars. So for Waymo this is moot.

So it's not like pure evil. In many cases there's a service being provided to match users to products they want / that don't suck.

> with targeted ads in the mix, there's a huge incentive to collect it and correlate it with all the other data Google has, which is creepy.

Strongly agree that in theory this shit can be used nefariously. That said, Google is far from the scariest of the bunch despite being the biggest. Telecom for example wants to deep inspect your network packets, and they can tell where you are physically today, anywhere in the country without even having cameras driving around 5 US cities.

Stronger regulations around data rights and privacy have been proven to work by the EU. I don't really see another solution apart from a legislative one.

"Once the tech becomes cheap, expect the car quality and cleanliness to go down" -- here you were positing that the _average_ willingness to pay for cleanliness will go down enough to affect things, barring any market segmentation. And I disagree:

1. You're assuming that the reason _why_ Waymo's cars tend to be cleaner than your typical Uber / Lyft is to satisfy a wealthy clientele. This elides a big reason for the existing gap: Uber / Lyft drivers aren't professionally managed. You don't directly pay for your Uber driver's interior car cleaning when you buy a ride, but the salary of folks managing Waymo's fleets is factored directly into pricing. Even if Waymo's clientele were less wealthy, you have to clean your cars and pass the cost on to all users. Additionally, interior cameras are pretty motivating to not mess up cars!

2. You're assuming that the cars today don't get maximum utilization, and that with more utilization you'd see dirtier cars. This is a pretty bad assumption - the cars are being utilized about as heavily as you can hope. In SF for most of Waymo's existence demand has outstripped supply. And the cars are still very clean :)

So if the usage is the same, and peoples' expectations for cleanliness are the same, why would rate of cleaning change as time goes on for the service?

The only thing I can think is if no reasonable alternatives to Waymo arise - in that case, cleanliness could go down but it has less to do with the clientele / willingness to pay, and more to do with competition / monopoly.

As another note, I just don't see how cleaning-based market segmentation would make good operational sense. Is cleaning the car slightly less frequently really gonna help the bottom line? Is the price differential big enough at single-ride scale? Do even rental car companies do this for their fleets - the cheap ones still seem to clean their cars.

Honest question - what's the harm in being targeted by ads? Is it just scrolling youtube more often than you should? Or is there a nefarious side that I'm failing to consider?

For me the thing I hate about location tracking and the ilk is primarily about its harmful externalities (e.g. put into use by gov't, abusive users, or by Google for anticompetitive reasons), not targeted advertising itself.

That being said, I have an anecdote as a former googler: the reality with Google though is very thoughtful and favorable for users if you ask Googlers who've worked on their software products. There are audit trails that can result in instant termination if it's determined that you accessed data without proper business justification. I've known an engineer who was fired for an insufficiently justified user lookup (and later re-hired when they did a deeper look -- hilariously they made this person go through orientation again).

And safeguards / approvals required to access data, so it's not just any joe shmoe who can access the data. Wanna use some data from another Google product for your Google product? You're SOL in most cases. Even accessing training data sourced from youtube videos was so difficult that people grumbled "if I were outside of Google at OpenAI or something I'd have an easier time getting hold of youtube videos -- I'd just scrape them."

This isn't to say any of this is a fair thing to make decisions on for most people, because companies change and welp how do you actually know they're doing the right thing? Imo stronger industry-wide regulations would actually help Google because they already built so much infra to support this stuff, and forcing everyone else to spend energy getting on their level would be a competitive advantage.