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Elon Musk’s OpenAI to battle in Dota 2 World Championship video game tournament
OpenAI, a research lab co-founded by Elon Musk, has developed a new breed of AI agents that are capable of playing Dota 2, a complex strategy game, in 5-on-5 multiplayer matches. OpenAI’s new bots have so far been able to beat amateur and semi-professional teams. With this accomplished, the research lab is now looking to bring its bots to The International, a prolific Dota 2 tournament, this coming August.
The new bots go by the name of OpenAI Five, a reference to the number of neural networks working together in the team. To train the neural networks, the AI has been playing roughly 180 years worth of gameplay every day using reinforcement learning. This enables the AI to learn the intricacies of the game, considering that it is far more complicated than board games like Chess and Go. Dota 2, for example, involves hiding data from players, preventing the system from perceiving the entire playing field at a given time.
The hardware employed by the research lab to train OpenAI Five is impressive. The five neural networks train through a scaled-up version of Proximal Policy Optimization running on 256 GPUs and 128,000 CPU cores. The same setup was adopted in a much smaller scale last year when OpenAI rolled out an artificial intelligence system that proved capable of beating the best Dota 2 players in the world in 1-on-1 matches.
Currently, however, OpenAI Five can only play the game with several restrictions. For one, the AI system can only use five of the 115 heroes available in the game. Skills such as Invisibility, Summons, and the placement of wards are also disabled. The research lab, however, hopes that through time, the neural networks would be able to play the game without any restrictions at all.
As could be seen in a recent video shared by the research lab, OpenAI Five is actually being received well by the Dota 2 community. Professional Dota 2 player Blitz, for one, noted that the bots are adopting strategies that are incredibly effective. In a match against OpenAI Five, Blitz, together with four employees of the research lab, put up a fight before getting dominated by the articificial intelligence. In a statement after the game, Blitz sheepishly stated that the bots capitalized on every small error he made during the match.
“I think the team fight aspect of the bot(s) was excellent. It didn’t mess up. When it came to coordination, it was some of the best pure team fighting because it felt like I was getting hammered every single time I made a mistake. I feel like normal humans don’t do that,” the professional Dota 2 player said.
So what’s the secret behind OpenAI Five? In a statement to The Verge, OpenAI CTO Greg Brockman noted that unlike human players, the bots have “no ego” when they play the game. The teamwork aspect of the bots was also trained by allowing them to work individually at first, then encouraging them to work together.
“The bots are totally willing to sacrifice a lane or abandon a hero for the greater good. For fun, we had a human drop in to replace one of the bots. We hadn’t trained them to do anything special, but he said he just felt so well-supported. Anything he wanted, the bots got him,” Brockman said.
Ultimately, Brockman is encouraged by OpenAI Five’s development so far. The research, after all, is motivated by the idea that if AI systems can be trained to perform complex tasks such as learning a game as intricate as Dota 2, it could eventually be used to solve equally complex real-world challenges. Some examples of real-world applications could be designing and managing a city’s transport structure, or the logistics of a massive business.
“This an exciting milestone, and it’s really because it’s about transitioning to real-life applications. If you’ve got a simulation of a problem and you can run it large enough scale, there’s no barrier to what you can do with this,” he said.
Turn-by-turn navigation is not new technology.
For over two decades, drivers have relied on Garmin, TomTom, and later smartphone apps like Google Maps and Waze to receive precise, reliable directions. These systems have guided millions safely through unfamiliar cities, highways, and backroads with remarkable effectiveness. They handle real-time traffic, construction detours, and complex intersections with minimal fuss.
Yet Tesla, the company that promised revolutionary Full Self-Driving (FSD), continues to struggle with this foundational capability. As FSD (Supervised) v14.3.4 has started rolling out to cars this week, navigation remains its glaring Achilles’ heel, undermining the entire autonomous vision.
Tesla Summon got insanely good in FSD v14.3.2 — Navigation? Not so much
Tesla’s FSD excels in many driving behaviors—smooth acceleration, confident lane changes in ideal conditions, and responsive handling of visible obstacles. However, when it comes to following a route accurately, the system falters repeatedly.
Owners report wrong turns, missed exits, inefficient routing through local roads instead of highways, phantom speed limit errors, and even directing vehicles to building rear entrances. Interventions for navigation issues often outnumber those for core driving maneuvers. Tesla has begun surveying owners specifically about these errors, acknowledging the problem after years of complaints.
Navigation is perhaps my biggest complaint when it comes to FSD, because sometimes, we do know better. Some of us have been living in our areas for our entire lives, but even those who have not have years or even decades of experience driving on local roads. We might know a little better about routing.
But the navigation mistakes are more than just FSD potentially taking a slightly different route that may or may not save you a few minutes. Sometimes, they’re genuinely mind-boggling.
This isn’t just annoying; it cascades into broader failures. A flawed route plan confuses the AI’s decision-making, leading to hesitant behavior, unnecessary disengagements, or dangerous maneuvers like attempting impossible U-turns or ignoring clear ramps. In a system meant to operate with minimal supervision, unreliable navigation erodes trust.
More often than not, false or plain incorrect navigation is what causes me to interrupt FSD operation. Unfortunately, I believe the latest FSD version is the worst example of it, and it leads me to believe that Tesla might be making some changes; they’ve just made them in the wrong direction.
It makes you wonder: Why is a company that has done so much with the progress of FSD and autonomy struggling so much with navigation, something that is not new and has been around a long time?
Multiple Data Sources
First, Tesla’s navigation relies on a fragile patchwork of multiple data sources—Google Maps, TomTom, OpenStreetMap, Valhalla, and its own fleet-derived data—stitched together rather than a single authoritative map. When these conflict on lane geometry, road status, or turn details, the system hesitates or chooses incorrectly.
Traditional GPS providers maintain centralized, regularly validated databases with professional curation and rapid updates. Tesla’s hybrid approach, while innovative in crowdsourcing, introduces inconsistencies that a purely vision-based or end-to-end AI approach may not easily reconcile in real time.
Persistent Learning
FSD seems to struggle with persistent learning from driver interventions.
Unlike consumer apps that quickly adapt to repeated corrections or user preferences (e.g., avoiding certain routes or remembering habitual detours), Tesla’s FSD often fails to internalize fixes on the same trip or across similar scenarios. Owners note making the same manual override multiple times without the routing engine updating its behavior meaningfully.
This stems from the neural architecture prioritizing real-time perception and control over long-term route memory and personalization, making navigation feel rigid and “opinionated” compared to the adaptive logic in Waze or Google Maps.
I noticed that when I asked Grok to try and get me home a certain way (a way that FSD routinely took in the past because it was the most efficient), it had to place a waypoint between my location at the time and my house. When I went to edit the waypoint out, as Grok had placed it for a way to get FSD to get off the highway at the right exit, it was stumped again, rerouted, and took a longer way home.
The next thing I’ve noticed, and this might be controversial, is that Nav has gotten even worse.
I think that might actually be a good thing; Tesla seems to be adjusting it. They just need to adjust it the opposite way.
The car is taking extremely strange routes to very… https://t.co/UHg3tVfNA2
— TESLARATI (@Teslarati) June 16, 2026
Reasoning, Scaling, and Intuition
Third, scaling navigation for unsupervised or robotaxi ambitions requires not just accuracy but adaptability and user-like reasoning. Current FSD often defaults to single routes that ignore driver preferences or real-world nuances like time-of-day traffic patterns. It fails to match the intuitive, context-aware planning that traditional systems have refined over the years.
Resolving navigation is critical for several reasons. Practically, it is the backbone of any autonomous journey: without trustworthy routing, the car cannot reliably reach destinations, rendering FSD useless for robotaxis or hands-free commutes. Safety depends on it—mismatched plans create hesitation in merges or intersections, increasing accident risk.
Economically, Tesla’s valuation and future hinge on FSD delivering unsupervised driving; persistent navigation flaws delay regulatory approval and erode consumer confidence. For owners who paid premiums for FSD, these issues represent unfulfilled promises. While it is unlikely Tesla will lose too many customers due to bad navigation, some will be frustrated with the constant need for human input.
Tesla has achieved miracles in electric vehicles and battery tech. Mastering turn-by-turn—technology Garmin nailed in the early 2000s—should not be this hard. By investing in tighter data integration, faster learning loops from interventions, and more intuitive routing algorithms, Tesla could close this gap.
Until then, FSD’s navigation struggles highlight a humbling truth: even the most ambitious innovator must sometimes master the basics before conquering the future.
A Tesla Cybertruck driver in the United Kingdom had their all-electric pickup seized by local police in the Greater Manchester area after the department cited “legitimate concerns.”
Last Thursday, police saw the pickup on the roads and decided to pull the driver over. Greater Manchester Police said:
“Whilst this may seem trivial to some, legitimate concerns exist around the safety of other road users or pedestrians if they were involved in a collision with the Cybertruck.”
🚨 A Tesla Cybertruck, which is illegal to drive in the UK due to safety concerns, has been seized by police in Greater Manchester
“Whilst this may seem trivial to some, legitimate concerns exist around the safety of other road users or pedestrians if they were involved in a… pic.twitter.com/cqhdPok3DM
— TESLARATI (@Teslarati) June 16, 2026
The Cybertruck in question was, according to the BBC, registered and insured abroad and was confiscated. The driver, who is a UK resident, was reported.
The Greater Manchester Police Department then added:
“The Tesla Cybertruck is not road-legal in the UK and does not hold a certificate of conformity.”
The Cybertruck cannot be legally driven in the UK because it has no UK Type Approval for operation in the country. This is due to some safety concerns, which are related to its angular shape and design. The stainless steel exoskeleton has sharp edges and projections that violate UK/EU rules on pedestrian protection.
Tesla has considered creating what it referred to as an “international version” that would be approved for operation in Europe. However, there has been no real movement on that front by the company, as it has been focused on the Robotaxi rollout primarily.
A new report claims that Apple is in the process of developing what would be the missing link for Tesla to get CarPlay.
Apple and Tesla have been reportedly working together for some time to give Tesla owners the opportunity to utilize CarPlay within their vehicles. While many owners are more than happy with Tesla’s in-house UI, which is seamless, effective, and smooth, some still want CarPlay, which does have its advantages.
A report from 9to5Mac now states that a new CarPlay technology that was highlighted during the Worldwide Developers Conference (WWDC) would potentially be the bridge between Tesla and Apple. With the addition of a feature known as “Route Sharing,” which gives a navigation app the ability to share routing data with the vehicle, Tesla would be able to launch CarPlay in its vehicles, the report states.
CarPlay has not been a priority for Tesla because it has done extremely well with its in-house UI, but some drivers are just used to it. Additionally, it could improve Tesla’s subpar Navigation or offer improved app capabilities, especially with iMessage.
Route Sharing is an intended addition to CarPlay’s iteration in iOS 26.4, which was released in March:
The addition of CarPlay would undoubtedly be welcome, but at the same time, it seems like Tesla realizes it is not of the utmost priority. There are so many things that Tesla is working on currently within its own vehicles, especially attempting to solve self-driving.
Back in February, Bloomberg had reported that Tesla was still working on bringing CarPlay to its vehicles, but it had not due to app compatibility issues and incredibly low adoption rates of iOS 26.
This bottleneck could buy Tesla the proper amount of time to develop CarPlay for its vehicles. It would be a welcome addition, and could be brought on with either the Summer or Fall 2026 Software Updates.
Tesla’s Navigation Nightmare: Why the easiest part of FSD might be the hardest
Tesla Cybertruck driver gets pickup seized for ‘legitimate concerns’ in UK
