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SpaceX Falcon Heavy rocket to launch record-breaking communications satellite
A report on the latest in a long line of SpaceX launches significantly delayed by customer payload readiness has been updated to confirm that the satellite in question will launch on Falcon Heavy, not Falcon 9.
Hughes revealed that it had selected SpaceX to launch its Maxar-built Jupiter-3 geostationary communications satellite during an industry conference on March 21st, 2022. At the time, Hughes stated that the satellite was on track to launch in the fourth quarter of 2022, a refinement but also a delay from earlier plans to launch sometime in H2 2022. Just six weeks later, manufacturer Maxar reported that the completion of Jupiter 3 – like many other Maxar spacecraft – had been delayed, pushing its launch to no earlier than (NET) “early 2023.”
At the same time, Maxar revealed that Jupiter 3 – also known as Echostar 24 – was expected to weigh around 9.2 metric tons (~20,300 lb) at liftoff when that launch finally happens. That figure immediately raised some questions about which SpaceX rocket Hughes or Maxar had chosen to launch the immense satellite.
Earlier on, regulatory documents revealed that Jupiter 3 would have a dry weight of 5817 kilograms (~12,825 lb). In July 2018, SpaceX broke the record for heaviest commercial geostationary satellite launch when a Falcon 9 rocket successfully delivered Telesat’s 7076-kilogram (15,600 lb) Telstar 19V to geostationary transfer orbit (GTO). To account for the satellite’s weight and still allow for Falcon 9 booster recovery, SpaceX launched Telstar 19V to a transfer orbit with its apogee (high point) well below geostationary orbit, meaning that the satellite had to do more of the work of orbit-raising. In other words, it wasn’t inconceivable that Jupiter 3 would also be launched to a low (subsynchronous) GTO on a recoverable Falcon 9.
However, in hindsight, Jupiter 3’s 5.8-ton dry mass should have already made it clear that that was unlikely. Telstar 19V, for example, had a reported dry mass of just over 3 tons (~6700 lb), meaning that more than half its wet mass was fuel for orbit-raising and maneuvers. In more normal cases, large geostationary satellites tend to launch with an extra 50-80% of their dry mass in fuel, not ~130%. Even at the low end of large geostationary satellites, Jupiter 3 was likely to have a launch mass of well over 8 tons.
Small bit of breaking news at this session: The @HughesConnects Jupiter 3 satellite will be launched on a @SpaceX rocket. #SATShow— Seth Miller (@WandrMe) March 21, 2022
At 9.2 tons, Jupiter 3 will leapfrog the world record for the largest commercial geostationary satellite ever launched by 30%. Barring the possibility of secret military spacecraft, it will likely be the heaviest spacecraft of any kind to reach geostationary orbit 35,785 km (22,236 miles) above Earth’s surface. More importantly, Jupiter 3 may also have the heaviest dry mass of any spacecraft to reach GEO, meaning that the actual hardware it will use to fill its role as a communications hub will also be exceptionally large and powerful. Jupiter 3 will deliver a maximum bandwidth of 500 gigabits per second.
With its exceptional heft, a recoverable Falcon 9 launch may have only been able to loft Jupiter 3 around half the way to GTO from low Earth orbit (LEO). It was little surprise, then, to learn that Hughes and Maxar had actually selected SpaceX’s far more capable Falcon Heavy rocket to launch the satellite. Even with full recovery of all three Falcon Heavy first-stage boosters, there’s a good chance that the rocket would be able to launch Jupiter 3 most of or all the way to a nominal geostationary transfer orbit. If the center core is expended and the side boosters land at sea, Falcon Heavy would likely be able to launch Jupiter 3 to a highly supersynchronous GTO, meaning that the spacecraft’s apogee would end up well above GEO. For example, on Falcon Heavy’s Block 5 launch debut, the rocket sent the ~6.5-ton (~14,250 lb) Arabsat 6A communications satellite to a GTO with an apogee of almost 90,000 kilometers (~56,000 mi), shaving about 20% off of the satellite’s orbit-raising workload.
Falcon Heavy’s Jupiter 3 mission won’t beat the record for total payload to GTO in a single launch, held by Arianespace’s Ariane 5 rocket after a 2021 mission to GTO launched two communications satellites weighing 10.27t, but it will be just one ton shy.
Jupiter 3 is the 10th mission firmly scheduled to launch on SpaceX’s Falcon Heavy rocket between now and 2025.
News
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
