I think painting an entire nation, whether it’s China or the United States, is a far greater evil. That’s the same sort of language being used against Palestinians right now.
This is really poor execution. You're taking a complex, low margin vehicle and introducing even more cost and supply chain complexity. On top of that, you're essentially making a proxy bet that more expensive hardware (LIDAR) will beat Tesla's software bet.
It's absolutely fair to criticize Elon for his ridiculous FSD timeline claims, but here we are now evaluating the market: if you have experienced the latest FSD, Waymo's and now Rivian's bet is just so obviously the exact wrong bet.
> if you have experienced the latest FSD, Waymo's and now Rivian's bet is just so obviously the exact wrong bet
I have. It’s wild for anyone to say this.
Waymo works. FSD mostly works, and I seriously considered getting a Tesla after borrowing one last week. But it needs to be supervised—this is apparent both in its attention requirement and the one time last week it tried to bolt into a red-lit intersection.
The state of the art is Waymo. The jury is still out on whether cameras only can replicate its success. If it can’t, that safety margin could mean game over for FSD on the insurance or regulatory levels. In that case, Rivian could be No. 2 to Waymo (which will be No. 1 if cameras only doesn’t pan out, given they have infinite money from Google). That’s a good bet.
And if cameras only works, you’ll still have the ultra premium segment Tesla seems to have abandoned and which may be wary of licensing from Waymo.
Waymo operates on guardrails, with a lot more human-in-the-loop (remotely) help than most people seem aware of.
Tesla's already have similar capabilities, in a much wider range of roads, in vehicles that cost 80% less to manufacture.
They're both achieving impressive results. But if you read beyond headlines, Tesla is setup for such more more success than Waymo in the next 1-2 years.
> Tesla is setup for such more more success than Waymo in the next 1-2 years
Iff cameras only works. With threshold for "works" beig set by Waymo, since a Robotaxi that's would have been acceptable per se may not be if it's statistically less safe compared to an existing solution.
Waymo also sets the timeline. If cameras only would work, but Waymo scales before it does, Tesla may be forced by regulators to integrate radars and lidars. This nukes their cost advantage, at least in part, though Tesla maintains its manufacturing lead and vertical integration.)
Tesla has a good hand. But Rivian's play makes sense. If cameras only fails, they win on licensing and a temporary monopoly. If cameras only work, they are a less-threatening partner for other car companies than Waymo.
In the increasingly rare instances where Tesla's solution is making mistakes, it's pretty much never to do with a failure of spatial awareness (sensing) but rather a failure of path planning (decision-making).
The only thing LIDAR can do sense depth, and if it turns out sensing depth using cameras is a solved problem, adding LIDAR doesn't help. It can't read road signs. It can't read road lines. It can't tell if a traffic light is red or green. And it certainly doesn't improve predictions of human drivers.
Which begs me the question why Tesla took so long to get here? It's only since v12 it starting to look bearable for supervised use.
The only answer I see is their goal to create global model that works in every part of the world vs single city which is vastly more difficult. After all most drivers really only know how to drive well in their own town and make a lot of mistakes when driving somewhere else.
It was only about 2 years ago that they switched from hard coded logic to machine learning (video in, car control out), and this was the beginning of their final path they are committed to now. (building out manufacturing for Cybercab while still finalizing the FSD software is a pretty insane risk that no other company would take)
Path planning (decision-making) is by far the most complicated part of self-driving. Waymo vehicles were making plenty of comically stupid mistakes early on, because having sufficient spatial accuracy was never the truly hard part.
In "scenarios where vision fails" the car should not be driving. Period. End of story. It doesn't matter how good radar is in fog, because radar alone is not enough.
Too bad conditions can change instantly. You can't stop the car at an alpine tunnel exit just because there's heavy fog on the other side of the mountain.
If the fog is thick enough that you literally can't see the road, you absolutely can and should stop. Most of the time there's still some visibility through fog, and so your speed should be appropriate to the conditions. As the saying goes, "don't drive faster than your headlights."
It seems you’re misinformed about how this sensor is used. The point clouds (plus camera and radar data) are all fed to the models for detection. That makes their detectors much more robust in different lighting and weather conditions than cameras alone.
It's just "sensing depth" the same way cameras provide just "pixels". A fused cameras+radars+lidar input provides more robust coverage in a variety of conditions.
You know it would be even more robust under even more conditions? Putting 80 cameras and 20 LIDAR sensors on the car. Also a dozen infrared heat sensors, a spectrophotometer, and a Doppler radar. More is surely always better. Waymo should do that.
Remarkable. You managed to both misunderstand my point and, in drafting your witty riposte, accidentally understand it and adopt it as your own. More isn't objectively better, less isn't objectively better. There's only different strategies and actual real world outcomes.
> More isn't objectively better, less isn't objectively better.
Great, you finally got there. All it took was one round of correcting misinformation about LiDAR and another round of completely useless back-and-forth about sensor count.
The words you’re looking for are necessary and sufficient. Cameras are necessary, but not sufficient.
> There's only different strategies and actual real world outcomes.
Thanks for making my point. Actual real world outcomes are exactly what matter: 125M+ fully autonomous miles versus 0 fully autonomous miles.
Highly ironic considering you started this comment chain with a bunch of fanboy talking points and misinformation. Clearly, you’re not interested in being factual. Bye.
Tesla literally has a human in the driver seat for each and every mile. Their robotaxi which operates on geofenced “guardrails” has a human in the driver seat or passenger seat depending on area of its operation, and also has active remote supervision. That’s direct supervision 100% of the time. It is in no way similar in capability to Waymo.
We’ve been hearing Tesla will “surpass Waymo in the next 1-2 years” from the past 8 years, yet they are nowhere close. It’s always future tense with Tesla and never about the current state.
My first instinct is also that Rivian's strategy doesn't make sense. Self-driving is a monumentally hard problem, to be successful you need a world-class engineering and research team, resources and time.
I suspect that when Rivian has an L3 product, Waymo will be already offering an L4 package to car manufacturers.
Waymo's AI so far has been narrowly focused few cities. Good start, but remains to be seen who will scale out quicker. IMO both will succeed.
Right now if you want a personal car Tesla's FSD is the only option and will remain so for likely a decade. Waymo doesn't seem to be excited about their mission at all. If it moves to Google's graveyard they'll be like "meh" while it's mission critical for Tesla.
This is such a wild take. Waymo is expanding to cities across the country, doing millions of paid rides every month. Meanwhile Tesla's "Robotaxi" is tooling around Austin with a few cars, every one of which has a driver in the front seat. On the personal vehicle side, Tesla hasn't done anything new or interesting in years, and sales are slumping. FSD never seems to actually become good enough to actually be "full self driving", it's just year after year of Tesla stans coming in here to tell us how "the latest version is incredible, actual full self driving is just around the corner!"
Waymo is delivering millions of paid rides per month all over the country with no one in the driver's seat. Tesla still can't manage that in one small city without a backup driver in the front.
But yes, just like the dozens of other times I've read this comment for years now, I'm sure "the latest version of FSD" is so groundbreaking, and it's all about to change!
Your statement on more expensive hardware likely isn't true if you factor in full costs. Lidar gives you things for free with little extra processing (or power) that optical takes extra work to do poorly with higher latency.
Also LIDAR has just plain dropped in price, well over 10x, while nVidia hardware (even the automotive specific variants) have not.
Taxi services are not low margin. A taxi typically does about 500,000 miles over its lifetime; adding $10,000 to that cost is 2 cents per mile, increasing price by about 1%.
You’re exactly wrong. In the race to supply AI data center, there is no “consumer” (in the sense I think you mean) making or influencing a buying decision. Without a clear path to increase supply, why take $1 when you can have $6 or $7?
Even less consumer supply means higher prices. What’s fascinating is the very thing that enabled AI — gaming - may be one of the early casualties as it gains in priority.
Whatever you think about AI, it is a good that Anthropic go public and I argue it’s consistent with their mission. It’s better for the public to have a way to own a piece of the company.
In an interview Sam Altman said he preferred to stay away from an IPO, but the notion of the public having an interest in the company appealed to him. Actions speak louder than words, and so it is fitting from a mission standpoint that Anthropic may do it first.
This is exactly my experience. And it's funny -- this crowd is so skeptical of OpenAI... so they prefer _Google_ to not be evil? It's funny how heroes and villains are being re-cast.
A hardware device from OpenAI is exactly why I would prefer it over Anthropic or Google. Why give up on differentiation? I would assume the model team is separate from the consumer hardware team.
Congrats. This is the first time I remember reading a genuine, authentic story about a sale. Much preferred over “this is about continuing the mission until my earn-out is complete.”
One of my early Macs was a Performa 638CD with no dedicated FPU. I had upgraded to a Performa 6400 (which felt like an absolute dog despite its size) but finally had an opportunity to move to the PowerComputing PowerTower Pro 225. What a beast! I hate to say it, but it was probably my favorite Mac I'd ever owned before the first iMac.
The Megahertz wars in the 1990s made it really difficult to understand relative performance across even the same ISA like this, and I think computers with the 603 CPU were a bit of a wrench in people's perception of the Mac.
The 180 or 200MHz 603e with 16k L1 cache in that Performa 6400 wasn't slow by any stretch, but it probably didn't have L2 cache. Coupled with the gradual transition to PPC native code of the OS and apps, these machines were often a little mismatched to expectations and realities of the code.
Meanwhile that PowerTower had a 604e with 32/32k L1 and 1MB L2 cache. That was a fast flier with a superscalar and out of order pipeline more comparable to the Pentium Pro and PII.
I have a PowerCenter Pro 210 in my basement right now! It's not quite as nice as the newer architecture in the PowerTower Pro machines, but it runs MacOS 7.6.1 wonderfully. It is more than enough for classic Mac games of that era - and a joy to use.
The later PowerCenter Pro’s could run with a 60 MHz FSB whereas the PowerTower Pro’s were usually 45-50 MHz FSB. There are a variety of tasks where my PowerCenter Pro 240 outruns my PowerTower Pro 250 for precisely that reason.
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