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Opinion: Tesla and India is the right thing at the wrong time
Tesla and India will not be working together any time soon, as new reports now indicate that Tesla has pulled its team responsible for entrance into the Indian market to other regions. Tesla and India might be a powerful one-two punch in the future, but in 2022, the two are just the right thing at the wrong time.
When Tesla first started making moves toward entering the Indian automotive market, there was a lot of excitement. The unbelievable potential of a partnership between the world’s leading electric car company and a government that primarily focuses on domestic manufacturing efforts, mainly due to the Make in India initiative, had people buzzing. However, there were still hoops to jump through. Any person with any sort of knowledge about India and cars knows that it is an expensive place to own one, especially if it was not built there. Getting cars from outside of India into the country doubles the cost of the vehicle on most occasions due to import duties. This is when Tesla started to realize how difficult this whole process might be.
Tesla places its India entry on hold after failing to secure lower import taxes: report
In routine negotiations, even with companies and governments, there is always a brief standoff period to see who will budge first. The hypothetical game of chicken can be magnified when dealing with two large entities, but eventually, something happens where someone makes a move, and things start to come together. I thought a great, recent, and relevant example of this would be the Elon Musk-Twitter buyout, where, as the board of the platform mozied over the Tesla CEO’s offer, new developments were few and far between, as expected. Nothing was going to move forward until someone budged.
The issue is that sometimes people choose not to budge because their needs in a particular deal are non-negotiable. When the needs of both sides are non-negotiable, it complicates the entire ordeal, and this is what made the Tesla-India deal stagnate: Two large entities that had specific requirements to make something happen. Neither was asking for a small thing, so it is not necessarily unreasonable that Tesla put its plans for India on hold.
Tesla needed to test demand for its cars. It would only be able to do this by building them in Fremont, California, Austin, Texas, Brandenburg, Germany, or Shanghai, China, and then shipping them to India. The problem with this system was it would not be an accurate representation of what Tesla might be able to sell in the market, as the vehicles would still be subjected to massive import duties that would double the cost of the car in some cases. Only a small percentage of the population would be able to afford that, and with very little EV infrastructure in India, it made the company’s products even less attractive. Tesla was effectively stuck between a rock and a hard place because it had an interest in building and selling cars in India, it just needed to confirm that the people of India wanted to buy the cars. Indian government officials rarely offered commentary that was indicative of a willingness to budge.
India wanted Tesla to commit to building a new Gigafactory in their country, which would align with the government’s focus on domestic manufacturing efforts and would likely give officials enough to pull back import duties for Tesla. However, Tesla could not commit to this: there was no indication that demand would be high enough to justify an entire factory, and Tesla was not sure it would be able to export vehicles from the Indian factory to other countries. Given the economic situations across the world during the past two years due to the COVID-19 pandemic, neither entity would be able to budge from their needs.
India and Tesla were the right thing, just at the wrong time. Given the extreme demands that both Tesla and Indian officials needed, it was best to not beat a dead horse any longer and move on from the potential partnership, at least temporarily. Tesla does have a lot of potential in India, but it cannot justify purchasing massive land plots for a new facility, it cannot justify spending millions more on showrooms and service centers, and it can not adequately test the want for its vehicles with massive import taxes trailing behind every car sent to the market.
Try again in a few years, hopefully.
I’d love to hear from you! If you have any comments, concerns, or questions, please email me at joey@teslarati.com. You can also reach me on Twitter @KlenderJoey, or if you have news tips, you can email us at tips@teslarati.com.
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NVIDIA Director of Robotics: Tesla FSD v14 is the first AI to pass the “Physical Turing Test”
After testing FSD v14, Fan stated that his experience with FSD felt magical at first, but it soon started to feel like a routine.
NVIDIA Director of Robotics Jim Fan has praised Tesla’s Full Self-Driving (Supervised) v14 as the first AI to pass what he described as a “Physical Turing Test.”
After testing FSD v14, Fan stated that his experience with FSD felt magical at first, but it soon started to feel like a routine. And just like smartphones today, removing it now would “actively hurt.”
Jim Fan’s hands-on FSD v14 impressions
Fan, a leading researcher in embodied AI who is currently solving Physical AI at NVIDIA and spearheading the company’s Project GR00T initiative, noted that he actually was late to the Tesla game. He was, however, one of the first to try out FSD v14.
“I was very late to own a Tesla but among the earliest to try out FSD v14. It’s perhaps the first time I experience an AI that passes the Physical Turing Test: after a long day at work, you press a button, lay back, and couldn’t tell if a neural net or a human drove you home,” Fan wrote in a post on X.
Fan added: “Despite knowing exactly how robot learning works, I still find it magical watching the steering wheel turn by itself. First it feels surreal, next it becomes routine. Then, like the smartphone, taking it away actively hurts. This is how humanity gets rewired and glued to god-like technologies.”
The Physical Turing Test
The original Turing Test was conceived by Alan Turing in 1950, and it was aimed at determining if a machine could exhibit behavior that is equivalent to or indistinguishable from a human. By focusing on text-based conversations, the original Turing Test set a high bar for natural language processing and machine learning.
This test has been passed by today’s large language models. However, the capability to converse in a humanlike manner is a completely different challenge from performing real-world problem-solving or physical interactions. Thus, Fan introduced the Physical Turing Test, which challenges AI systems to demonstrate intelligence through physical actions.
Based on Fan’s comments, Tesla has demonstrated these intelligent physical actions with FSD v14. Elon Musk agreed with the NVIDIA executive, stating in a post on X that with FSD v14, “you can sense the sentience maturing.” Musk also praised Tesla AI, calling it the best “real-world AI” today.
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Tesla AI team burns the Christmas midnight oil by releasing FSD v14.2.2.1
The update was released just a day after FSD v14.2.2 started rolling out to customers.
Tesla is burning the midnight oil this Christmas, with the Tesla AI team quietly rolling out Full Self-Driving (Supervised) v14.2.2.1 just a day after FSD v14.2.2 started rolling out to customers.
Tesla owner shares insights on FSD v14.2.2.1
Longtime Tesla owner and FSD tester @BLKMDL3 shared some insights following several drives with FSD v14.2.2.1 in rainy Los Angeles conditions with standing water and faded lane lines. He reported zero steering hesitation or stutter, confident lane changes, and maneuvers executed with precision that evoked the performance of Tesla’s driverless Robotaxis in Austin.
Parking performance impressed, with most spots nailed perfectly, including tight, sharp turns, in single attempts without shaky steering. One minor offset happened only due to another vehicle that was parked over the line, which FSD accommodated by a few extra inches. In rain that typically erases road markings, FSD visualized lanes and turn lines better than humans, positioning itself flawlessly when entering new streets as well.
“Took it up a dark, wet, and twisty canyon road up and down the hill tonight and it went very well as to be expected. Stayed centered in the lane, kept speed well and gives a confidence inspiring steering feel where it handles these curvy roads better than the majority of human drivers,” the Tesla owner wrote in a post on X.
Tesla’s FSD v14.2.2 update
Just a day before FSD v14.2.2.1’s release, Tesla rolled out FSD v14.2.2, which was focused on smoother real-world performance, better obstacle awareness, and precise end-of-trip routing. According to the update’s release notes, FSD v14.2.2 upgrades the vision encoder neural network with higher resolution features, enhancing detection of emergency vehicles, road obstacles, and human gestures.
New Arrival Options also allowed users to select preferred drop-off styles, such as Parking Lot, Street, Driveway, Parking Garage, or Curbside, with the navigation pin automatically adjusting to the ideal spot. Other refinements include pulling over for emergency vehicles, real-time vision-based detours for blocked roads, improved gate and debris handling, and Speed Profiles for customized driving styles.
Elon Musk
Elon Musk’s Grok records lowest hallucination rate in AI reliability study
Grok achieved an 8% hallucination rate, 4.5 customer rating, 3.5 consistency, and 0.07% downtime, resulting in an overall risk score of just 6.
A December 2025 study by casino games aggregator Relum has identified Elon Musk’s Grok as one of the most reliable AI chatbots for workplace use, boasting the lowest hallucination rate at just 8% among the 10 major models tested.
In comparison, market leader ChatGPT registered one of the highest hallucination rates at 35%, just behind Google’s Gemini, which registered a high hallucination rate of 38%. The findings highlight Grok’s factual prowess despite the AI model’s lower market visibility.
Grok tops hallucination metric
The research evaluated chatbots on hallucination rate, customer ratings, response consistency, and downtime rate. The chatbots were then assigned a reliability risk score from 0 to 99, with higher scores indicating bigger problems.
Grok achieved an 8% hallucination rate, 4.5 customer rating, 3.5 consistency, and 0.07% downtime, resulting in an overall risk score of just 6. DeepSeek followed closely with 14% hallucinations and zero downtime for a stellar risk score of 4. ChatGPT’s high hallucination and downtime rates gave it the top risk score of 99, followed by Claude and Meta AI, which earned reliability risk scores of 75 and 70, respectively.
Why low hallucinations matter
Relum Chief Product Officer Razvan-Lucian Haiduc shared his thoughts about the study’s findings. “About 65% of US companies now use AI chatbots in their daily work, and nearly 45% of employees admit they’ve shared sensitive company information with these tools. These numbers show well how important chatbots have become in everyday work.
“Dependence on AI tools will likely increase even more, so companies should choose their chatbots based on how reliable and fit they are for their specific business needs. A chatbot that everyone uses isn’t necessarily the one that works best for your industry or gives accurate answers for your tasks.”
In a way, the study reveals a notable gap between AI chatbots’ popularity and performance, with Grok’s low hallucination rate positioning it as a strong choice for accuracy-critical applications. This was despite the fact that Grok is not used as much by users, at least compared to more mainstream AI applications such as ChatGPT.
NVIDIA Director of Robotics: Tesla FSD v14 is the first AI to pass the “Physical Turing Test”
Tesla AI team burns the Christmas midnight oil by releasing FSD v14.2.2.1
