Tesla could have more camera data in sum (that's not even clear - transmitting and storing data from all the cars on the road is no easy task - L4 companies typically pysically remove drives and use appliances to suck data off the hard drives), but Waymo has more camera data per car (29 cameras) and higher fidelity data overall (including lidar, radar, and microphone data). Tesla can't magically enhance data it didn't collect.
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.