Elon Musk Explains Future Plans of Tesla
Not many people could look back ten years and see how accurate their predictions were. Elon Musk is not one of these people as he founded Tesla Motors and their electric sports vehicle the Roadster.
Not many people could look back ten years and see how accurate their predictions were. Elon Musk is not one of these people as he founded Tesla Motors and their electric sports vehicle the Roadster. His aim was to produce many more models and head towards an economy based on solar electric.
Twice as powerful as a Prius and challenging Ferrari and Porsche, the question still asked is regarding the need for another electric sports car. At present, costs are high, but this will change and with Tesla, customers will pay the initial price until volume lowers the price. The family version is already going to be 50% the price of the Roadster, and the battery is not toxic as sometimes stated.READ ALSO: Greenland is Melting Too Early This Year – Natural Causes or Global Warming?
Musk sees his plan in stages – produce sports car and use money raised for an affordable car, then a more affordable car and still aim for zero emissions.
The Roadster may look like a high-performance car, and 86MJ works the battery per 100MJ charged. Even compared to the Prius that is a good figure. At present, the so-called hybrid cars are running on petrol with help from the battery. Oil will still be needed to recharge their batteries as well.
It is not only cars that are being produced to help the environment, as other items will soon be on the market. By using a solar panel from SolarCity, you could glean around 50 miles of electricity each day. Again, Elon Musk is involved in this innovative product.
Elon Musk’s Blog Post, Back in 2006
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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