Tesla is focused on developing a scalable approach to autonomy
By starting out with trying to make FSD work almost everywhere, Tesla is forcing itself to make a scalable solution. There won't need to be as much "going back to the drawing board" as there might be at Waymo / Cruise etc who barely have gotten things to work outside of one city. Of course a downside of that is going to be performance of the general algorithm will be much worse at first. The data shows most of the disengagements are currently due to mapping issues. Tesla clearly hasn't finished whatever their generalized mapping solution is, but I'm confident they have the diverse data to figure it out. It's hilarious to think people believe Tesla has flatlined progress when even that one issue clearly has a path to improve.
The problem with this is the idea that L2 Anywhere means you have a scalable solution to L4 (everywhere) or L5.
Anyone who has actually developed an application for companies would know that scalable applications are not based on what feature sets you have.
But the architectural design. So yes you can have a L4 system in one city that is DESIGNED to be scalable. If it can't work in one city it won't work anywhere else.
Think about it, if you can't go 1 million miles without a safety disengagement in one city, you won't be able to do that in other cities.
This is also proven by the fact the Tesla FSD Beta has similar categorized safety disengagement stats everywhere.
Meanwhile Waymo has "solved" perception, but nary a peep about global deployment? If it was scalable, they would be touting their scaling prowless, demo in 50 cities and IPO for a trillion dollar valuation. Oh, not happening?
Scalability has just as much to do with infrastructure and logistics as it does with tech.
You need to look at tech scalability alot closer.
There are three techs involved in AV: Perception, Prediction and Planning.
When you say they "solved" perception. You have to remember that there are different road types: suburbs, city, dense urban, highway and rural
and different conditions: good weather (sunny, cloudy), inclement weather (light/moderate rain, light snow, fog), harsh weather (heavy rain, heavy fog, moderate/heavy snow).
Lets use Waymo as an example.
By going driverless in chandler, phoenix, they solved the 3 Ps for suburbs in good weather.
When they went driverless this year in downtown, phoenix and SF, they solved the 3 Ps for city and urban in good weather.
To be able to mass scale (5-10 new cities a year). You need to solve most use case.
That means surburb, city, dense urban, highway, good weather and inclement weather.
That way when you scale, you don't have growing pains trying to map out where you can deploy.
They are almost there, they have highway and inclement weather left.
Tesla is doing the best compromise between what a data scientist and an accountant would do.
If you told the data scientist you had unlimited funds to solve FSD, he/she would load 100,000 cars with cameras / radars, lidars and have them driving around every area of the U.S. or world, collecting the data and working to develop a robust, scalable solution. But that's not cost effective, so no one is doing that. Tesla is chosing to collect more diverse data while competitors are focused on collecting less diverse, but better resolution data.
The problem with this statement is that its factually wrong. You say no one is doing that. But the truth is that alot of companies ARE doing that.
And any good data scientist knows you need a wealth of diverse data in order to make a complicated algorithm generalize well. That's something that Tesla has and competitors absolutely do not.
If this statement was correct and we take Waymo as an example of companies whose tech (perception) doesn't generalize. You do realize that the tens of millions of people (Adults, Teens and children) who visit SF from around the world and from all over the country are in danger of being maimed/killed by being ran over by a driverless Waymo?
The same also applies to the millions of cars from out of state.