Andrej Karpathy, former Tesla Autopilot Director, offers a fascinating perspective on Artificial General Intelligence (AGI) by drawing parallels with the evolution of self-driving technology. As the world grapples with the implications of AGI, Karpathy suggests that the trajectory of autonomous vehicles provides valuable insights into what AGI might entail and its impact on society.
The blog post has since been deleted, but you can still check it out here; Karpathy defines AGI as an autonomous system exceeding human capabilities in most economically valuable tasks. This definition hinges on two criteria: complete autonomy and broad economic applicability. According to Karpathy, the journey of self-driving technology exemplifies the societal dynamics of increasing automation and, by extension, AGI's potential evolution.
The Gradual Rise of Automation
Self-driving technology stands out due to its high visibility, significant economic footprint, large human workforce, and the complex challenge of automating driving. Unlike other sectors that have seen automation, the path to self-driving is a prime example of AGI's characteristics: accessibility, economic importance, workforce impact, and technical challenge.
Karpathy outlines the gradual development of driving automation. Initially, vehicles featured Level 2 driver assistance, where AI collaborates with humans in navigation, handling many low-level driving aspects while allowing human intervention. This partial automation is analogous to AI tools in various industries, like GitHub Copilot in programming, highlighting the incremental nature of AI advancement.
The leap to full automation, as seen in Waymo's driverless cars, marks a significant milestone. In cities like San Francisco, Waymo offers autonomous rides in a small, geo-fenced area, however, it helps showcase a future where AI will surpass human driving abilities. The transition to full autonomy will depend on public awareness, trust, preferences, and supply constraints in creating a large automated fleet.
Global Expansion: Challenges and Opportunities
The globalization of full automation, Karpathy notes, is a gradual, resource-intensive process. Waymo's current limitations to specific cities illustrate the challenges of expanding automated services, including adapting to local conditions and regulations. This expansion mirrors the broader trajectory of AGI deployment across various sectors, where scalability is both achievable and gradual.
Society's reaction to self-driving technology parallels AGI's potential impact. Despite significant advancements, public awareness and acceptance vary. Some view autonomous vehicles with curiosity and skepticism, while others adapt quickly. This range of responses suggests how society might adapt to AGI in various industries.
Economically, self-driving technology has both eliminated and created jobs. While driver roles are phased out, new positions in data labeling, remote support, fleet maintenance, and sensor technology emerge. This transformation reflects the broader economic implications of AGI, where work is not merely eliminated but refactored and reshaped.
The competitive landscape in self-driving technology, with companies like Waymo, Tesla, and others, mirrors the expected consolidation in AGI-related industries. As with self-driving, only a few companies may dominate the AGI space after an initial burst of growth and competition.
Karpathy envisions AGI as a gradual, society-involved evolution rather than a sudden, uncontrollable leap. Just as self-driving technology is transforming transportation, making it safer and more efficient, AGI promises to reshape various sectors.
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Tesla appears to be preparing to expand its Robotaxi geofence in Austin, Texas, with numerous engineering vehicles taking to the road. One of the most interesting sights, between the short and tall LiDAR rigs, was a Cybertruck validation vehicle, which we don’t often see.
Tesla’s expansion is moving the Robotaxi Network into downtown Austin, a dense urban environment that is currently outside the geofence. It appears Tesla is content with the latest builds of Robotaxi FSD and is ready to take on urban traffic.
The inclusion of a Cybertruck in the validation fleet is noteworthy, as the rest of the vehicles are Model Ys. This suggests that Tesla may be addressing two challenges simultaneously: expanding its service area while also addressing the FSD gap between the Cybertruck and other HW4 Tesla vehicles.
Tesla Validating Downtown Austin before expanding the Robotaxi geo-fence area. pic.twitter.com/ylFATtjcDi
Recent sightings have shown a fleet of Tesla vehicles, equipped with rooftop validation sensor rigs, running routes throughout downtown Austin and across the South Congress Bridge. While these rigs include LiDAR, it’s not a sign that Tesla is abandoning its vision-only approach.
Instead, Tesla uses the high-fidelity data from the LiDAR as a ground truth measurement to validate and improve the performance of its cameras. In short, it essentially uses the LiDAR measurements as the actual distances and then compares the distances determined in vision-only to the LiDAR measurements. This allows Tesla to tweak and improve its vision system without needing LiDAR.
This data collection in a new, complex environment right outside the Robotaxi geofence is an indicator that plans to expand the geofence. Tesla has previously indicated that they intend to roll out more vehicles and expand the geofence slowly. Given that their operational envelope includes the entire Austin Metro Area, we can expect more locations to open up gradually.
Once they expand the operational radius to include downtown Austin, they will likely also have to considerably increase the number of Robotaxis active in the fleet at any given time. Early-access riders are already saying that the wait time for a Robotaxi is too long, with them sometimes having to wait 15 minutes to be picked up.
With a larger service area, we expect Tesla to also increase the number of vehicles and the number of invited riders to try out the service.
After all, Tesla’s goal is to expand the Robotaxi Network to multiple cities within the United States by the end of 2025. Tesla has already been running an employees-only program in California, and we’ve seen validation vehicles as far away as Boston and New Jersey, on the other side of the country.
Cyber FSD Lagging Behind
One of the most significant details from these recent sightings is the presence of a Cybertruck. Cybertruck’s FSD builds have famously lagged behind the builds available on the rest of Tesla’s HW4 fleet. Key features that were expected never fully materialized for the Cybertruck, and the list of missing features is quite extensive.
Start FSD from Park
Improved Controller
Reverse on FSD
Actually Smart Summon
It may not look like a lot, but if you drive a Cybertruck on FSD and then hop in any of the rest of Tesla’s HW4 vehicles, you’ll notice a distinct difference. This is especially evident on highways, where the Cybertruck tends to drift out of the lane, often crossing over the lane markings.
Tesla was testing parts of Downtown Austin, TX with this Cybertruck which had a massive roof rack, and sensors.
We previously released an exclusive mentioning that a well-positioned internal source confirmed with us that a new FSD build for the Cybertruck was upcoming, but we never ended up receiving that particular build, only a point release to V13.2.9. The AI team’s focus had clearly shifted to getting the latest Robotaxi builds running and validated, and while a flagship, the Cybertruck fleet was small and new, and really a secondary task.
The Cybertruck’s larger size, steer-by-wire, rear-wheel steering, and different camera placements likely present a bigger set of challenges for FSD. Deploying it now as a validation vehicle in a complex environment like downtown Austin suggests that Tesla is finally gathering the specific data needed to bring the Cybertruck’s capabilities up to par. This focused effort is likely the necessary step to refine FSD’s handling of the Cybertruck before they begin rolling out new public builds.
When?
Once Tesla’s validation is complete, we can probably expect the Robotaxi Network to expand its borders for the first time in the coming days or weeks. However, we’ll likely see more signs of the expansion, such as Robotaxi vehicles driving themselves around the area, before the expansion actually happens.
Hopefully, the Cybertruck will also learn from its older siblings and receive the rest of its much-needed FSD features, alongside an FSD update for the entire fleet.
Tesla is rolling out a fairly big update for its iOS and early-access-only Robotaxi app, delivering a suite of improvements that address user feedback from the initial launch last month. The update improves the user experience with increased flexibility, more information, and overall design polish.
The most prominent feature in this update is that Tesla now allows you to adjust your pickup location. Once a Robotaxi arrives at your pickup location, you have 15 minutes to start the ride. The app will now display the remaining time your Robotaxi will wait for you, counting down from 15:00. The wait time is also shown in the iOS Live Activity if your phone is on the lock screen.
How Adjustable Pickups Work
We previously speculated that Tesla had predetermined pickup locations, as the pickup location wasn’t always where the user was. Now, with the ability to adjust the pickup location, we can clearly see that Tesla has specific locations where users can be picked up.
Rather than allowing users to drop a pin anywhere on the map, the new feature works by having the user drag the map to their desired area. The app then presents a list of nearby, predetermined locations to choose from. Once a user selects a spot from this curated list, they hit “Confirm.” The pickup site can also be changed while the vehicle is en route.
This specific implementation raises an interesting question: Why limit users to predetermined spots? The answer likely lies in how Tesla utilizes fleet data to improve its service.
Here is the new Tesla Robotaxi pickup location adjustment feature.
While the app is still only available on iOS through Apple’s TestFlight program, invited users can download and update the app.
Tesla included these release notes in update 25.7.0 of the Robotaxi app:
You can now adjust pickup location
Display the remaining wait time at pickup in the app and Live Activity
Design improvements
Bug fixes and stability improvements
Nic Cruz Patane
Why Predetermined Pick Up Spots?
The use of predetermined pickup points is less of a limitation and more of a feature. These curated locations are almost certainly spots that Tesla’s fleet data has identified as optimal and safe for an autonomous vehicle to perform a pickup or drop-off.
This suggests that Tesla is methodically “mapping” its service area not just for calibration and validation of FSD builds but also to help perform the first and last 50-foot interactions that are critical to a safe and smooth ride-hailing experience.
An optimal pickup point likely has several key characteristics identified by the fleet, including:
A safe and clear pull-away area away from traffic
Good visibility for cameras, free of obstructions
Easy entry and exit paths for an autonomous vehicle
This change to pick-up locations reveals how Tesla’s Robotaxi Network is more than just Unsupervised FSD. There are a lot of moving parts, many of which Tesla recently implemented, and others that likely still need to be implemented, such as automated charging.
Frequent Updates
This latest update delivers a much-needed feature for adjusting pickup locations, but it also gives us a view into exactly what Tesla is doing with all the data it is collecting with its validation vehicles rolling around Austin, alongside its Robotaxi fleet.
Tesla is quickly iterating on its app and presumably the vehicle’s software to build a reliable and predictable network, using data to perfect every aspect of the experience, from the moment you hail the ride to the moment you step out of the car.
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