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Do you still have TSLA? What is your outlook in the short term? long term? Thanks.
Yes, I'm still very long w/TSLA.
Short-term outlook (1-3 months) ... more worried than excited.
Mid-term outlook (4-15 months) ... more worried than excited.
Long-term outlook (16+ months out) ... more excited than worried.
SCTY acquisition really dampens TSLA's prospects in the next 12 months, in my opinion. TSLA needs more money, largely due to SCTY. And SCTY complicated TSLA's financial position as well. Further, it takes away from focus on Model 3 execution. I don't like that TSLA will be significantly diluted due to SCTY acquisition, and I don't like that Tesla is going to raise more capital so soon when they said they wouldn't need to until much later.
Over the next 12 months, the big unknown is Tesla Energy and how fast they can ramp production of Powerpack/Powerwall, and if it can become a significant contribution to revenue or not. My personal opinion is that it might take longer than expected for Powerpack/Powerwall to make a difference with TSLA's revenue, but I'd love to be proven wrong.
I also think Model 3 will be produced in limited quantities in 2017 (ie., 30k max). So, I think we might have to wait until Q1 2018 for the Model 3 to be produced in larger numbers. I think Q1 2018 could be a huge turning point for Tesla... a sort of coming out party, part 2 (note: part 1 was Q1 2013 earnings report).
JRP3
Hyperactive Member
I think as long as TSLA stock price is over $200, then I view it as 90%+ probablility of SCTY acquisition going through.
JRP3
Hyperactive Member
I guess it depends on how you look at it. From purely a dollars sense, yes it was a mistake (at least looking at it from the current moment). But I sold my SCTY shares because I thought at the time there was a 10-30% or so chance the acquisition wouldn't go through (since TSLA was under $200 at the time) and if the acquisition didn't go through then a 70%+ chance that SCTY would go bankrupt within 6 months of that. I don't like those odds, and I also had soured on SCTY management and no longer believed in them. I knew there was always the possible reward of the arbitrage gain if I held my SCTY shares, but that reward wasn't very enticing to me.
Chickenlittle
Banned
There is still a chance it will not go through with price action if it threatens capital raise. I have come around to liking the merger, although I think tesla over paying. If there is a D.C. To D.C. Charging option from solar battery pack that could stimulate solar demand
Here's an excellent must-watch video by Adam Jonas (Morgan Stanley) giving an overview of the transition to autonomous EVs. It's an 11 minute video. Watch it before the link changes/disappears.
Morgan Stanley
Morgan Stanley
Why does he (and anyone else talking autonomous driving for that matter) not even mention Tesla as a potential contender?
It seems like the whole educated world is fixated on Google model of self driving cars. Fully autonomous in a small area, gradually expanding out.
Tesla operates on a very different model. Be able to use anywhere and everywhere but with incremental capabilities over time.
It's very weird that the entire investing society would write off Tesla model. Or maybe it just doesn't know or understand it.
Hopefully the Oct 17th launch puts a dent in that perspective.
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I think he's looking at it in part from the legal perspective, where the fleets will have to prove themselves in small areas first. I don't think that it'll happen. I think that advanced driver assistance will, despite current concerns, show statistical success quickly. The collisions happen on the edge cases, so as the systems become more advanced and begin better to handle the edge cases, there'll likely be a sudden drop in collisions. Given the combination of the popularity with drivers, and the obvious benefits, there will be something of a fast-tracking.
The important thing that some might be missing is that Tesla isn't building driver assistance systems. It's building autonomy, and deploying driver assistance on the way.
Thanks @DaveT for sharing that.
I think it is a fascinating view into their thoughts about the ride sharing future... however, if the SAE Level 4 or true autonomy takes longer to actually arrive, many of what they are talking about will end up being seemingly forever into the future. Much like virtual reality lit people's imaginations for a while, and then receded into the background to finally jump forth in the past couple of years. We will likely have driver assistance for quite a while and full autonomy will be further out, even if the cars can handle many more situations. Some of that ride sharing future will require cars that handle very near 100% of driving situations. I think Tesla is well positioned to handle a gentle transition where we are very close to 100%, but not where you really have fully autonomous robot cars that you can just send out into the world while you don't need it. Actually, I think that near full autonomy scenario is the most likely outcome. It will be very interesting to see what would happen to some of these start up efforts if the transition is more like 10-15 years than 3-5 years. I think Tesla does quite well in the most likely scenario, and will be a leader in that effort. It doesn't necessarily matter that much to Tesla if they achieve full autonomy in virtually all scenarios in 5 years or 15 years, while for many others, I think it makes a big difference.
I think it is a fascinating view into their thoughts about the ride sharing future... however, if the SAE Level 4 or true autonomy takes longer to actually arrive, many of what they are talking about will end up being seemingly forever into the future. Much like virtual reality lit people's imaginations for a while, and then receded into the background to finally jump forth in the past couple of years. We will likely have driver assistance for quite a while and full autonomy will be further out, even if the cars can handle many more situations. Some of that ride sharing future will require cars that handle very near 100% of driving situations. I think Tesla is well positioned to handle a gentle transition where we are very close to 100%, but not where you really have fully autonomous robot cars that you can just send out into the world while you don't need it. Actually, I think that near full autonomy scenario is the most likely outcome. It will be very interesting to see what would happen to some of these start up efforts if the transition is more like 10-15 years than 3-5 years. I think Tesla does quite well in the most likely scenario, and will be a leader in that effort. It doesn't necessarily matter that much to Tesla if they achieve full autonomy in virtually all scenarios in 5 years or 15 years, while for many others, I think it makes a big difference.
The biggest strength that Tesla has with regard to autonomy is that it is one of the few companies that is making substantial money on it now. It has a working business model.
The advantage of L5 learning with a for-profit production fleet vs buying your own fleet
I thought this was interesting considering the previous discussion here about Uber vs Tesla.
If you consider the following:
Google has 58 self-driving cars. Assume very conservative numbers of $50K-100K / car = $2.9 - $5.8 million (likely a lot more. I believe LiDAR alone was at one point spec'd at $75K)
Google has a little over 2 million miles of real world self-driving with those 58 cars over a ~4 year period.
2 million miles
Let's makes some assumptions:
- Tesla will ship 2,000 cars/week
- Cars will drive 30 miles per day
That means Tesla will pass Google's accumulated self-driving miles in 3 weeks, while making a profit on every car sold.
That's 4 years -> 3 weeks.
MobileEye, last I read, was talking about deployment of their next-gen system in 2018. All other car companies are talking 2020-2021 for L5 deployment.
This is, at least on the surface, a very very big lead for Tesla.
The startup cost from Uber purchasing their own cars, plus the combined fleet learning mile advantage from Tesla would seem to make it extremely likely that they'll reach an approved Robotaxi before anyone else...
I thought this was interesting considering the previous discussion here about Uber vs Tesla.
If you consider the following:
Google has 58 self-driving cars. Assume very conservative numbers of $50K-100K / car = $2.9 - $5.8 million (likely a lot more. I believe LiDAR alone was at one point spec'd at $75K)
Google has a little over 2 million miles of real world self-driving with those 58 cars over a ~4 year period.
2 million miles
Let's makes some assumptions:
- Tesla will ship 2,000 cars/week
- Cars will drive 30 miles per day
That means Tesla will pass Google's accumulated self-driving miles in 3 weeks, while making a profit on every car sold.
That's 4 years -> 3 weeks.
MobileEye, last I read, was talking about deployment of their next-gen system in 2018. All other car companies are talking 2020-2021 for L5 deployment.
This is, at least on the surface, a very very big lead for Tesla.
The startup cost from Uber purchasing their own cars, plus the combined fleet learning mile advantage from Tesla would seem to make it extremely likely that they'll reach an approved Robotaxi before anyone else...
Attachments
This is going out tomorrow in my newsletter, but I thought I'd post it here first...
Why Autopilot 2.0 is a Big Deal
To fully appreciate Autopilot 2.0, one must understand the limitations of Autopilot 1.0. Autopilot 1.0 was built upon Mobileye’s object recognition system, which was based on a low-power GPU and rigid rules for object recognition. In some cases, Mobileye’s system would/could not recognize certain objects and this posed a serious limitation (and danger) to Tesla’s Autopilot system. Tesla built a complex and very capable system (that included HD mapping, radar analysis, and driving decision making) on top of Mobileye’s object recognition system. But the flaws of Mobileye’s system could only be mitigated by Tesla and not overcome. In order to truly improve Autopilot in a substantial manner, Tesla needed to develop their own object recognition system and own the entire software stack of autonomous driving. This is what they did for Autopilot 2.0.
Put simply, Autopilot 2.0 takes the input from cameras, radar, sensors and processes them on a high-powered GPU and computer. The key is Tesla is not using rigid rules (that have limitations) but rather they’re using a "neural net" (a type of AI system), which continuously gets better and will easily be 10x (if not 100x) more accurate than their previous system. (note: for more info on neural networks, see link)
As the neural net gets trained, it becomes increasingly accurate. However, initially Tesla needs to get the hardware on a lot of cars and train the software-based neural network. The process should be fairly quick, and Autopilot 2.0 should be more accurate than Autopilot 1.0 within a 3-4 months (my guess). Within 6-12 months, Autopilot 2.0 will likely be 2-3x more capable and accurate than Autopilot 1.0. This is a big deal because it’s safety we’re dealing with, and Autopilot 2.0 will be a substantially safer experience.
The reason why Elon thinks Autopilot 2.0 will reach full self-driving capabilities within a couple years is because he’s confident in the power of the neural net (and the power of AI) to make itself better. In Elon’s words, this is a “solved problem.” Regulatory approval might take longer, depending on the country/jurisdiction, but the technology is at our doorstep.
Why Autopilot 2.0 is a Big Deal
To fully appreciate Autopilot 2.0, one must understand the limitations of Autopilot 1.0. Autopilot 1.0 was built upon Mobileye’s object recognition system, which was based on a low-power GPU and rigid rules for object recognition. In some cases, Mobileye’s system would/could not recognize certain objects and this posed a serious limitation (and danger) to Tesla’s Autopilot system. Tesla built a complex and very capable system (that included HD mapping, radar analysis, and driving decision making) on top of Mobileye’s object recognition system. But the flaws of Mobileye’s system could only be mitigated by Tesla and not overcome. In order to truly improve Autopilot in a substantial manner, Tesla needed to develop their own object recognition system and own the entire software stack of autonomous driving. This is what they did for Autopilot 2.0.
Put simply, Autopilot 2.0 takes the input from cameras, radar, sensors and processes them on a high-powered GPU and computer. The key is Tesla is not using rigid rules (that have limitations) but rather they’re using a "neural net" (a type of AI system), which continuously gets better and will easily be 10x (if not 100x) more accurate than their previous system. (note: for more info on neural networks, see link)
As the neural net gets trained, it becomes increasingly accurate. However, initially Tesla needs to get the hardware on a lot of cars and train the software-based neural network. The process should be fairly quick, and Autopilot 2.0 should be more accurate than Autopilot 1.0 within a 3-4 months (my guess). Within 6-12 months, Autopilot 2.0 will likely be 2-3x more capable and accurate than Autopilot 1.0. This is a big deal because it’s safety we’re dealing with, and Autopilot 2.0 will be a substantially safer experience.
The reason why Elon thinks Autopilot 2.0 will reach full self-driving capabilities within a couple years is because he’s confident in the power of the neural net (and the power of AI) to make itself better. In Elon’s words, this is a “solved problem.” Regulatory approval might take longer, depending on the country/jurisdiction, but the technology is at our doorstep.
Wouldn't it be that the neural net is already trained... but not validated. Tesla will deploy the trained neural net to the AP2 vehicles and run it in shadow mode, where the AP2 responses will be compared to human drivers. Where there is significant discrepancy, they can do further training with those situations. As the discrepancy rate versus correct human driving drops for particular driving situations, they can then enable those features?
Alright, here's a few more thoughts:
1. Stop giving NVIDIA credit.
The entire software package is created by Tesla (according to Elon). The neural net is created by Tesla, and this includes all the object processing/recognition, radar and mapping analysis, decision making by car, etc. NVIDIA is only providing the computer and GPU (and maybe some minor software tools to interface w/GPU). Give credit where credit is due. This system is all Tesla. Sure it uses cameras made by "X company", radar by Bosch, computer/GPU by NVIDIA, etc. But that's not where the difficulty lies. The difficulty lies in the software... made by Tesla.
2. Mobileye sucks.
Mobileye's system doesn't use deep learning (or AI). It's a rather primitive system with major limitations. It's intended to be more of a better adaptive cruise control (vs self-driving). It uses a low powered GPU and rigid rules, and I think we can confidently blame a lot of AP accidents to date on Mobileye's bad system. Mobileye is way behind Tesla, and that's an understatement. I think Mobileye might not even be around in 5 years. Comma.ai has a much better chance at making a good autonomous driving system for OEMs than Mobileye has. There will be other startups and true tech companies as well who provide solutions to OEMs. I even think NVIDIA is preparing their own autonomous software package to eventually offer to OEMs.
3. Tesla Network
With Autopilot 2.0, Tesla has pulled a few years ahead of the competition. And this is going to help when Tesla releases their ride-sharing network, Tesla Network. I don't think it's going to be easy to take on Uber, but I think Tesla has a decent chance. If Tesla can get regulatory approval for driver-absent Level 5 autonomous driving up to 35mph (ie., let's say in 2018), then Tesla can effectively start scaling their ride-sharing network. Here's how. They can have cars (driver-absent) circle urban areas driving under 35mph. When they pick up a passenger, the passenger sits in the driver seat. If the passenger agrees to be the "backup driver" then the car can go faster than 35mph, and if there's a problem it can fall back to the passenger in the driver's seat who can take control of the car. If the passenger doesn't agree to be the "backup driver" then the car is limited to 35mph, which might be ok for certain urban areas.
1. Stop giving NVIDIA credit.
The entire software package is created by Tesla (according to Elon). The neural net is created by Tesla, and this includes all the object processing/recognition, radar and mapping analysis, decision making by car, etc. NVIDIA is only providing the computer and GPU (and maybe some minor software tools to interface w/GPU). Give credit where credit is due. This system is all Tesla. Sure it uses cameras made by "X company", radar by Bosch, computer/GPU by NVIDIA, etc. But that's not where the difficulty lies. The difficulty lies in the software... made by Tesla.
2. Mobileye sucks.
Mobileye's system doesn't use deep learning (or AI). It's a rather primitive system with major limitations. It's intended to be more of a better adaptive cruise control (vs self-driving). It uses a low powered GPU and rigid rules, and I think we can confidently blame a lot of AP accidents to date on Mobileye's bad system. Mobileye is way behind Tesla, and that's an understatement. I think Mobileye might not even be around in 5 years. Comma.ai has a much better chance at making a good autonomous driving system for OEMs than Mobileye has. There will be other startups and true tech companies as well who provide solutions to OEMs. I even think NVIDIA is preparing their own autonomous software package to eventually offer to OEMs.
3. Tesla Network
With Autopilot 2.0, Tesla has pulled a few years ahead of the competition. And this is going to help when Tesla releases their ride-sharing network, Tesla Network. I don't think it's going to be easy to take on Uber, but I think Tesla has a decent chance. If Tesla can get regulatory approval for driver-absent Level 5 autonomous driving up to 35mph (ie., let's say in 2018), then Tesla can effectively start scaling their ride-sharing network. Here's how. They can have cars (driver-absent) circle urban areas driving under 35mph. When they pick up a passenger, the passenger sits in the driver seat. If the passenger agrees to be the "backup driver" then the car can go faster than 35mph, and if there's a problem it can fall back to the passenger in the driver's seat who can take control of the car. If the passenger doesn't agree to be the "backup driver" then the car is limited to 35mph, which might be ok for certain urban areas.
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