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Tesla Autopilot and artificial intelligence: The unfair advantage

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Serial tech entrepreneur and Tesla CEO Elon Musk has had a longstanding fear of artificial intelligence, but his company’s investments in artificial intelligence have been noted as an attempt to keep track of developments in the field of AI. In an interview for Vanity Fair in April 2017, he outright expressed his concerns with AI and claimed that one of the reasons for the development of SpaceX was that it could be an interplanetary escape route for humanity if artificial intelligence goes rogue. However, even Musk realizes the importance of AI in real-world applications, specifically for self-driving cars. At the end of June, Musk hired Andrej Karpathy as the new Director of Artificial Intelligence at Tesla, and MIT Technology Review claims it is the start of a plan to rethink automated driving at Tesla.

Karpathy comes from OpenAI, a non-profit company founded by Musk that focuses on “discovering and enacting the path to safe artificial general intelligence.” Afterwards, he moved on to intern at DeepMind, a place that spotlighted reinforcement learning with AI. Karpathy’s previous research focuses are on image understanding and recognition, which directly translates into applying proven image recognitions algorithms in Tesla’s Autopilot.

Recently, the popular question of morality was brought up in context to AI learning in Autopilot cars. It’s very interesting to consider how to teach technology to respond to an innately human moral problem. The Moral Machine, hosted by Massachusetts Institute of Technology, is a platform built to “gather human perspectives on moral decisions made by machine intelligence, such as self-driving cars.” It questions how the machine would act in human decisions such as whether to crash the driver or keep driving into a pedestrian that is crossing the street where there are no traffic regulators. How exactly do you teach a logical machine the mechanisms of ethical decision-making?

Although Musk and Tesla are the leaders in the self-driving field, a number of other companies are also entering into the competition sphere. Google, Uber, and Intel’s Mobileye have all been considering the application of reinforcement learning in the context of self-driving cars. Uber, Waymo, GM (Cruise Automation), Mobileye (camera supplier), Mercedes and Velodyne (LiDAR Supplier) could be potential competitors in the realm of self-driving vehicles. However, most of the technology does not encompass full self-driving, which is Musk’s aim. While other companies are investing heavily in autonomous fleets, Tesla far outpaces them in terms of data collection and release of finished product.

What are the differentiators for Tesla in the growing field of AI directed driverless cars?

Historically, Musk has focused on “narrow AI” which can enable the car to make decisions without driver interference. The vehicles would increasingly rely on radar as well as ultrasonic technology for sensing and data-gathering to form the basis for Tesla’s Autopilot algorithms. A technology that isn’t derived from LiDAR, the combination of radar and camera system said to outperform LiDAR especially in adverse weather conditions such as fog.

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With the introduction of Autopilot 2.0 and Tesla’s “Vision” system, and billions of miles real-world driving data collected by Model S and Model X drivers, Tesla continues to create a detailed 3D map of the world that has increasingly finer resolution as more vehicles are purchased, delivered and placed onto roadways. The addition of GPS allows Tesla to put together a visual driving map for AI vehicles to follow, paving the path for newer and more advanced vehicles.

The addition of Karpathy will be a notable asset for Tesla’s Autopilot team. In specific, the team will be able to apply Karpathy’s deep knowledge of reinforcement learning systems. Reinforcement learning for AI is similar to teaching animals via repetition of a behavior until a positive outcome is yielded. This type of machine learning will allow Tesla Autopilot to navigate complex and challenging scenarios. For example, AI will allow cars to determine in real-time how to navigate a four-way stop, a busy intersection or other difficult situations present on city streets. By making cars smarter with the way they navigate drivers, Tesla will put itself ahead of the curve with a fully-thinking, fully self-driving car.

Tesla is expected to demonstrate a fully autonomous cross-country drive from California to New York by the end of this year as a showcase for its upcoming Full Self-driving Capability. If you’re buying a Tesla Model 3, or an existing Model S or Model X owner, just know that you’re contributing to a self-driving future, mile by mile.

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SpaceX Starship Flight 13 faces wrath of the Texas skies

SpaceX pushed Starship Flight 13 to Friday, blaming weather instead of the previous engine issues.

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SpaceX called off Thursday’s launch attempt of Starship Flight 13, pushing the mission to Friday because of weather tied to Tropical Storm Bertha. The company confirmed the delay on X, noting “Now targeting Friday, July 24 for Starship’s thirteenth flight test, due to weather. A key objective for the flight test is to get clear imagery from the ground of Starship’s heatshield as it flies at a higher dynamic pressure during ascent, which won’t be possible with today’s weather conditions.”

This is the second delay for Flight 13 in two weeks. SpaceX first tried to launch the mission on July 16, but the countdown ended in an automated abort at T-0 when four of Super Heavy Booster 20’s 33 Raptor engines failed to ignite. Musk said at the time that two Raptors would need to be removed and replaced, as Teslarati reported. The company spent the following week destacking Ship 40 and Booster 20, swapping engines, and running leak checks before restacking the vehicle on Pad 2 Wednesday night, according to Spaceflight Now’s live coverage.

Elon Musk debunks $52 billion SpaceX-NVIDIA GPU deal

 

Unlike the engine problem, Thursday’s delay has nothing to do with the hardware. SpaceX wants clean footage of Starship’s heat shield captured from the ground as the vehicle flies through max dynamic pressure, something the storm’s cloud cover over South Texas would not allow. The company said visibility should improve for Friday’s attempt, with the same 90 minute window opening at 5:45 p.m. CT.

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Flight 13 will be the second outing for the V3 versions of Starship and Super Heavy, following their debut on Flight 12 in May. The mission carries 20 production Starlink V3 satellites, the first time SpaceX has flown operational satellites rather than mass simulators on Starship. Six of those satellites are fitted with cameras to inspect the heat shield from a different angle during ascent, giving engineers a second data source beyond the ground imagery the weather is currently blocking.

Booster 20 will attempt a boostback burn and a splashdown landing burn in the Gulf of America, while Ship 40 follows a suborbital trajectory toward a landing in the Indian Ocean. The flight plan largely mirrors Flight 12, though the booster will run a more aggressive ascent burn after max Q this time, and the ship’s heat shield includes load sensing tiles meant to measure stress at the higher dynamic pressure SpaceX is targeting.

If Friday’s attempt succeeds, Flight 13 could be the last suborbital test in the program. SpaceX is already looking to push for an orbital flight on Flight 14.

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Investor's Corner

Tesla stock tumbles after earnings, one of its sharpest single-day declines

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Credit: Tesla

Tesla stock (NASDAQ: TSLA) endured one of its sharpest single-day declines in years on July 23, tumbling approximately 14.5 percent and closing near $320 after opening the session around $374. The drop erased more than $140 billion in market value amid heavy trading volume and left the shares at multi-week lows.

The sell-off followed the company’s second-quarter 2026 results, released the previous evening. Tesla reported record revenue of $28.2 billion, up 26 percent year over year, driven by a Q2-record 480,126 vehicle deliveries. Energy storage deployments also rose strongly.

Tesla (TSLA) Q2 2026 earnings results: miss on EPS, beat on revenue

Yet profitability disappointed sharply. Operating income fell 57 percent to $398 million, compressing the operating margin to just 1.4 percent. Non-GAAP earnings per share came in at $0.33, well below the roughly $0.53 analysts had expected. Free cash flow turned negative by $1.1 billion as capital expenditures surged 142 percent to $5.8 billion, largely tied to accelerated spending on artificial intelligence, robotics, and autonomous systems.

The losses on capex were expected, as Tesla said it would be spending heavily in 2026.

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Investors also reacted to lingering uncertainty surrounding key product timelines. During the Earnings Call, management reiterated ambitions for Robotaxi deployment and the Optimus humanoid robot, but offered limited new concrete milestones, renewing questions about execution pace that have long accompanied Tesla’s ambitious roadmap.

The magnitude of the decline places it among Tesla’s more severe one-day percentage losses since its 2010 initial public offering. Historically, the two largest single-day drops (split-adjusted) remain September 8, 2020, when shares fell 21.1 percent amid broader market volatility and valuation concerns, and January 13, 2012, with a 19.3 percent plunge during the company’s early growth struggles.

Other notable declines include an 18.6 percent drop on March 16, 2020, at the onset of pandemic-related market turmoil. Thursday’s move ranks roughly ninth on the all-time list but stands out as the steepest in more than a year.

Despite the short-term pain, Tesla’s long-term trajectory has repeatedly recovered from such volatility. The latest results underscore both the strength of its core automotive and energy businesses and the near-term costs of heavy investment in next-generation technologies.

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Elon Musk is not happy about this Tesla Full Self-Driving approval delay

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Credit: Tesla

Elon Musk clapped back at France’s decision to withhold the approval for Tesla’s Full Self-Driving (FSD) Supervised system, projecting a clear and blunt message to French Transport Minister Phillippe Tabarot, after he publicly rejected the technology in its current form.

Tabarot outlines several concerns with Tesla Full Self-Driving in a detailed video statement, where he said, “The safety trade-offs are not yet sufficient to authorize it as it currently stands,” he said. He emphasized that FSD is not a true self-driving system and that the driver remains fully responsible.

Key issues Tabarot also brought up included allowing speeding when surrounding traffic exceeds limits and what he believes are insufficient guarantees of driver attention during complex urban maneuvers such as lane changes, intersections, and roundabouts.

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While acknowledging technological progress and France’s support for autonomous innovation, Tabarot stressed that deployment must prioritize road safety. He noted ongoing technical discussions with Tesla, the Netherlands, and other European partners, with further ecosystem meetings planned for the fall.

Musk’s rebuke highlights the human cost of regulatory caution. Tesla’s latest safety reports provide compelling data supporting accelerated adoption. In the most recent 12-month period, vehicles using FSD (Supervised) recorded one major collision per approximately 5.1 million miles driven, dramatically better than the U.S. national average of one crash per 698,000 miles.

Even Tesla vehicles driven manually with active safety features outperform the average by a wide margin. These figures come from billions of real-world miles of telemetry, showing FSD vehicles involved in far fewer incidents than both manual Teslas and the broader U.S. fleet.

Critics argue Tesla’s comparisons require careful scrutiny regarding reporting thresholds and fleet demographics, yet the data consistently positions FSD as a potential lifesaver. With road fatalities remaining a leading cause of death worldwide, Musk contends that proven safer technology should not face prolonged bureaucratic hurdles.

France’s measured approach reflects the broader European regulatory caution, which many, especially Musk, have been critical of in the past. However, as autonomous systems from Tesla and competitors like Waymo demonstrate superior safety in independent studies, pressure is mounting for harmonized approvals.

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Musk’s warning carries the belief that every month of delay may equate to avoidable tragedies on European roads.

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