Tesla shares closed down more than 8% on Monday (October 3), after the electric vehicle maker released third-quarter production and delivery data lower than analysts' expectations. data shows that in the past six months, Tesla's stock price has fallen by 36.51% compared with its highest point ($381.82 per share).
Interestingly, Tesla's AI html on Friday did not help boost the company's stock price on the 3rd. Optimists believe that Tesla robot Optimus is a promising project, but more people are skeptical.
The decline in stock prices shows that investors are worried about Tesla's delivery data this year. The reason for is that as electrification and intelligence have become a clear development trend in the automotive industry, Tesla, as the leader, has been less than expected in terms of delivery scale and profit.
This means that Tesla needs to quickly "copy" the automotive technology to the humanoid robot as soon as possible. Tesla's goal is to make the Optimus price less than $20,000, even cheaper than ordinary cars. At the same time, migration is carried out in terms of battery packs, cooling systems and intelligent technologies.
However, unfortunately, Tesla is still working hard to optimize FSD video model training. In addition, it is obvious that considering that technology research and development also requires a lot of talent investment, the focus of this Tesla artificial intelligence day is to recruit talents.
"We are still cautious about Tesla's future valuation." Some institutional analysts believe that as competition intensifies, the gap between Tesla and other new energy vehicle companies and traditional auto manufacturers is narrowing, which is a risk that must be faced.
This can also be seen from Tesla's performance in the Chinese market.
High-tech Intelligent Automobile Research Institute monitoring data shows that from January to August 2022, the standard equipped combination intelligent networking (L2-level assisted driving + digital networked cockpit + OTA) for passenger cars in the Chinese market (excluding import and export) was delivered in a total of 2.1052 million vehicles, an increase of 111.13% year-on-year, and the front-load loading rate was 16.89%. In terms of the price of the model of
, the average price of the new car equipped with a combined intelligent network delivery from January to August was 236,900 yuan, a slight decline of 7,400 yuan compared with the annual average of last year. At the same time, more new cars are beginning to occupy the mainstream position in the market.
data shows that from January to August 2021, there were 20 combined intelligent connected models with a market share of more than 1%, and increased to 24 models in the same period in 2022. However, the total share of these models is declining, from 67.71% in the same period in 2021 to 62.89%.
The reason is very clear: more brands are mainly promoting new intelligent connected cars, and the gap between models is also rapidly narrowing. Among them, the competitiveness of China's local independent brands is becoming increasingly stronger. data from January to August 2022 shows that the number of independent brand models entering the "1% club" has increased by 6 compared with the same period in 2021, which is 19:5 compared with the number of joint venture brand models.
Take Tesla as an example. Model Y replaces Model 3 and becomes the best-selling combined intelligent connected model in the Chinese market from January to August this year, but the competitiveness of Model 3 has plummeted, falling from the first place in the same period last year to the sixth place.
This is a problem that new energy vehicle companies need to face from traditional single hot model to multi-model layout. Insufficient production capacity, supply chain problems, profit pressure, increased competitive models and market dividend bottlenecks are all uncertain factors.
In terms of car models, independent brands can be said to be making full efforts. BYD has entered the "1% club" number from 3 models last year to 6 models this year. Brand models such as Zekr , Wenjie, Zero Race, and Aian have also been on the list for the first time, showing that competition is becoming increasingly fierce.
In addition, in the stage of intelligent in-depth development, data iteration and regional differentiation of regulatory requirements have also caused many companies to hit a wall, especially for Tesla. However, it is also helping more companies come from behind.
"Build data closed-loop capabilities, such as developing independent learning systems on the test side, and engineers do not need to find bugs in person." Lang Xianpeng, vice president of intelligent driving at Ideal Auto , said that this is the core reason for taking out intelligent driving systems that are not inferior to those of competitors in less than a year.
For example, Musk expects that by the end of this year, Tesla will add more FSD beta owners around the world. But the problem is that many countries may need regulatory approval for the launch of this project, especially in Europe and China.
Considering the data-driven technology upgrade model, advanced intelligent driving systems like FSD obviously have many areas that need improvement. Real driving data of car owners from more regions helps accelerate development. This is different from the basic ADAS functions. These only require engineers to define functional boundaries, and are more based on logic development rather than data.
As Ashok Elliswamy, director of Tesla’s autonomous driving software, said, the number of owners who enable the FSD test system has increased from 2,000 a year ago to 160,000 now. But that's not enough.
In the Chinese market, the total mileage of users using ideal intelligent driving has exceeded 290 million kilometers, NOA (navigation assisted driving) has a total mileage of 24.62 million kilometers, and the total mileage of effective learning scenarios is 190 million kilometers, which is the second in the world, second only to Tesla.
In the view of Gaogong Intelligent Automobile Research Institute, the competition in functional configuration will gradually shift to competition around data collection scale and quality. For the smart cockpit, will help further upgrade the human-computer interactive experience. For intelligent driving, the functional ODD boundaries will be further expanded.
