Elon Musk Tesla: Latest Updates and Analysis
Elon Musk finally admits that Tesla will have to replace its HW3 self-driving computers
Elon Musk finally admits that Tesla will have to replace its HW3 self-driving computers. He expressed it would be difficult, but Tesla would do it.
However, no concrete plan has been shared.
For the enhanced part of the year, we have been reporting that Tesla can’t achieve its promise of “full self-driving” on HW3, and it needs to come clean about it.
Last October, Tesla CEO Elon Musk finally admitted that it might be the case, but he didn’t want to commit fully:
We are not 100% sure. HW4 has several times the capability of HW3. It’s easier to get things to work on HW4 and it takes a lot of efforts to squeeze that into HW3. There is some chance that HW3 does not achieve the safety level that allows for unsupervised FSD.
Today, just 3 months later, Tesla’s management was asked about the HW3 situation again.
Tesla’s head of FSD, Ashok Elluswamy, first responded that they “are not giving up on it”, but then Musk corrected him saying that “the truth is that we will need to replace all HW3 computers in vehicles where FSD was purchased.”.
He expressed that Tesla would replace them for free, but he specified that it would only be for people who bought the up to $15,000 Full Self-Driving package.
That could prove problematic as Tesla always promised that “all cars produced since 2016 are capable of Full Self-Driving” – not just the ones who paid for the software.
Tesla already had to upgrade computers on older vehicles built between 2016 and 2018 that had older [website] computers. Again, Tesla only offered HW3 retrofits only for people who bought the FSD software package and it was proven wrong in court.
In fact, Tesla was sued by an owner who had a [website] vehicle and wanted to use the monthly FSD subscription. Tesla wanted to charge him for the computer retrofit to make the FSD work on his car. He took them to court over the claim that “all cars produced since 2016 are capable of Full Self-Driving” and the judge sided with the owner, forcing Tesla to pay for the retrofit.
Tesla could technically find itself having to do that on millions of HW3 vehicles now.
Furthermore, Tesla will have to design a new computer to retrofit vehicles equipped with HW3 computers since the HW4 computer in its latest vehicles is not retrofittable for the HW3 cars.
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Elon Musk-approved Tesla Full Self-Driving dataset doubled: what’s the state of FSD now?
The Tesla Full Self-Driving crowdsourced dataset that Elon Musk has approved has doubled since the CEO shared it last month.
What does it say about the state of Tesla’s Full Self-Driving program now?
We have been complaining for years about Tesla’s lack of data on its Full Self-Driving program. In September 2024, Tesla started releasing monthly AI/self-driving roadmap updates and referenced improvements in some metrics that it wouldn’t even share.
Unsurprisingly, Tesla quickly stopped releasing the monthly updates after missing several milestones.
Because Tesla has not provided data, we have to rely on a less-than-ideal but enhanced-than-nothing crowdsourced dataset.
Tesla fans often criticized the dataset because it doesn’t make the Full Self-Driving system look great, but it’s harder to do now that Tesla CEO Elon Musk has positively referenced the dataset on two occasions.
The last time Musk referenced the data, he misrepresented it as confirming “exponential improvement” by singling out highway mileage.
This was data back when Musk referenced it last month:
Musk particularly praised the highway mileage of 723 miles between disengagement, which was a big jump over Tesla FSD v12, but we noted that this was misleading because v13 finally merged city and highway software stacks to include end-to-end neural nets.
This had been delayed for highway driving for years. Therefore, Musk was trying to make this upgrade look like an “exponential improvement” when, in fact, it was simply Tesla finally using years of development in city driving and transferring it to highway driving, which was supposed to have happened a long time ago.
However, Musk’s approval of crowdsource data confirms that it is in the ballpark of what Tesla sees with FSD. Otherwise, it would be incredibly misleading for him to share it positively, even if he has misrepresented it to make it look positive.
When Musk shared the data, there were only 8,000 miles on the new FSD v13 enhancement. Now, the crowdsource dataset has almost twice as many miles on Tesla FSD v13.
With more miles, the performance actually went down. Miles between disengagement on highway went from 723 to 624 miles.
Tesla’s primary metric has been “miles between necessary disengagement,” which is currently 489 miles.
The automaker previously claimed that v13 would bring “5 to 6x improved miles between necessary interventions” compared to [website].
The average of all [website] updates is at 183 miles. Therefore, it’s actually closer to a [website] improvement.
Ashok Elluswamy, the head of FSD at Tesla, has previously stated that for Tesla to enable unsupervised self-driving, Tesla needs to achieve the average in miles per critical intervention “equivalent of human miles between collision,” which stands at 700,000 miles, .
Yes, a 15,000-mile dataset is quite limited, but you FSD fans can’t complain since Elon bragged about it with just 8,000 miles.
500 miles sounds about right to me. It means that Tesla would need a 1,400x improvement to achieve the level of safety it introduced it needs to achieve unsupervised self-driving.
Anyone who thinks this is happening this year is kidding themselves.
However, I believe that Tesla is working around that. Elon expressed that Tesla is launching “unsupervised self-driving as a paid service in Austin in June,” and he confirmed that it will be done with “Tesla’s internal fleet.”.
This means a Waymo-style geo-fenced autonomous ride-hailing system assisted by teleoperation – aka what Elon trashed for years and noted that Tesla would easily surpassed with its approach.
You can deploy such a system with much lower miles between disengagement. Something like 10,000-20,000 miles is achievable, but the lower the mileage between disengagement, the messier it will be.
In a limited geo-fenced environment, Tesla could possibly achieve that by June in Austin.
The thing that Tesla fans need to understand is that this will only mean that Tesla will be where Waymo was years ago. It won’t get them closer to what Elon has been promising for years: unsupervised self-driving for the entire customer fleet built since 2016.
Tesla would need to scale like Waymo is, which Elon has been claiming is not scalable.
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Elon Musk says that Tesla ‘unsupervised self-driving begins’, but don’t get too excited
Elon Musk says that Tesla’s “unsupervised self-driving begins,” but as usual with the CEO’s statements about self-driving, it’s just “corporate puffery.”.
Tesla has released a video showcasing that its vehicles coming out of Fremont factory can now drive themselves to the loading docks for transportation:
Teslas now drive themselves from their birthplace at the factory to their designated loading dock lanes without human intervention.
One step closer to large-scale unsupervised FSD [website] — Tesla AI (@Tesla_AI) January 29, 2025.
I’m not gonna lie. I don’t want to hate on this because I think it is pretty cool. It’s just not what was promised for years.
Literally, Elon Musk stated that Tesla would do a driverless cross-country drive by the end of 2018. It never happened. Then he stated that by the end of every year since 2020, Tesla would enable unsupervised self-driving in Tesla vehicles. That also never happened.
And now, he retweeted the above video, claiming that “unsupervised self-driving begins.”.
The Tesla vehicles in this video drive [website] miles on private roads at low speeds around the factory. This is nothing more than what Tesla demonstrated on the private roads of the Warner Bros studio with the Robotaxi last year.
This is clearly not the “beginning of unsupervised self-driving” or at least, not in the way that Musk has promised Tesla FSD owners for years.
Musk’s latest timeline is “unsupervised self-driving in California and Texas around Q2 2025.”.
Again, I think this is cool. It is a fun and efficient way to transport vehicles to the loading docks, but that’s about it.
Anyone who has used FSD should know that this is not more impressive than FSD, and FSD is still years away from being ready to be unsupervised.
I strongly believe that if FSD was developed in a vacuum without Elon’s comments and Tesla selling it as a package with future upgrades, people would celebrate FSD has a great program. Instead, it’s becoming a joke.
You have Elon claiming that this is the “beginning of unsupervised self-driving” and Ashok Elluswamy, Tesla’s head of FSD, added: “just a matter of time before it’s rolling on public roads.”.
Yes, a matter of time. A long time while you sell this package to people promising it will happen by the end of the year.
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Market Impact Analysis
Market Growth Trend
| 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
|---|---|---|---|---|---|---|
| 8.3% | 10.0% | 10.5% | 11.6% | 12.3% | 12.7% | 12.8% |
Quarterly Growth Rate
| Q1 2024 | Q2 2024 | Q3 2024 | Q4 2024 |
|---|---|---|---|
| 10.9% | 11.7% | 12.4% | 12.8% |
Market Segments and Growth Drivers
| Segment | Market Share | Growth Rate |
|---|---|---|
| Connected Cars | 35% | 14.2% |
| Autonomous Driving | 22% | 18.5% |
| EV Technology | 28% | 21.9% |
| Telematics | 10% | 9.7% |
| Other Automotive Tech | 5% | 6.3% |
Technology Maturity Curve
Different technologies within the ecosystem are at varying stages of maturity:
Competitive Landscape Analysis
| Company | Market Share |
|---|---|
| Tesla | 16.9% |
| Waymo | 12.3% |
| NVIDIA DRIVE | 10.7% |
| Bosch | 9.5% |
| Continental | 7.8% |
Future Outlook and Predictions
The Elon Musk Tesla landscape is evolving rapidly, driven by technological advancements, changing threat vectors, and shifting business requirements. Based on current trends and expert analyses, we can anticipate several significant developments across different time horizons:
Year-by-Year Technology Evolution
Based on current trajectory and expert analyses, we can project the following development timeline:
Technology Maturity Curve
Different technologies within the ecosystem are at varying stages of maturity, influencing adoption timelines and investment priorities:
Innovation Trigger
- Generative AI for specialized domains
- Blockchain for supply chain verification
Peak of Inflated Expectations
- Digital twins for business processes
- Quantum-resistant cryptography
Trough of Disillusionment
- Consumer AR/VR applications
- General-purpose blockchain
Slope of Enlightenment
- AI-driven analytics
- Edge computing
Plateau of Productivity
- Cloud infrastructure
- Mobile applications
Technology Evolution Timeline
- Technology adoption accelerating across industries
- digital transformation initiatives becoming mainstream
- Significant transformation of business processes through advanced technologies
- new digital business models emerging
- Fundamental shifts in how technology integrates with business and society
- emergence of new technology paradigms
Expert Perspectives
Leading experts in the automotive tech sector provide diverse perspectives on how the landscape will evolve over the coming years:
"Technology transformation will continue to accelerate, creating both challenges and opportunities."
— Industry Expert
"Organizations must balance innovation with practical implementation to achieve meaningful results."
— Technology Analyst
"The most successful adopters will focus on business outcomes rather than technology for its own sake."
— Research Director
Areas of Expert Consensus
- Acceleration of Innovation: The pace of technological evolution will continue to increase
- Practical Integration: Focus will shift from proof-of-concept to operational deployment
- Human-Technology Partnership: Most effective implementations will optimize human-machine collaboration
- Regulatory Influence: Regulatory frameworks will increasingly shape technology development
Short-Term Outlook (1-2 Years)
In the immediate future, organizations will focus on implementing and optimizing currently available technologies to address pressing automotive tech challenges:
- Technology adoption accelerating across industries
- digital transformation initiatives becoming mainstream
These developments will be characterized by incremental improvements to existing frameworks rather than revolutionary changes, with emphasis on practical deployment and measurable outcomes.
Mid-Term Outlook (3-5 Years)
As technologies mature and organizations adapt, more substantial transformations will emerge in how security is approached and implemented:
- Significant transformation of business processes through advanced technologies
- new digital business models emerging
This period will see significant changes in security architecture and operational models, with increasing automation and integration between previously siloed security functions. Organizations will shift from reactive to proactive security postures.
Long-Term Outlook (5+ Years)
Looking further ahead, more fundamental shifts will reshape how cybersecurity is conceptualized and implemented across digital ecosystems:
- Fundamental shifts in how technology integrates with business and society
- emergence of new technology paradigms
These long-term developments will likely require significant technical breakthroughs, new regulatory frameworks, and evolution in how organizations approach security as a fundamental business function rather than a technical discipline.
Key Risk Factors and Uncertainties
Several critical factors could significantly impact the trajectory of automotive tech evolution:
Organizations should monitor these factors closely and develop contingency strategies to mitigate potential negative impacts on technology implementation timelines.
Alternative Future Scenarios
The evolution of technology can follow different paths depending on various factors including regulatory developments, investment trends, technological breakthroughs, and market adoption. We analyze three potential scenarios:
Optimistic Scenario
Rapid adoption of advanced technologies with significant business impact
Key Drivers: Supportive regulatory environment, significant research breakthroughs, strong market incentives, and rapid user adoption.
Probability: 25-30%
Base Case Scenario
Measured implementation with incremental improvements
Key Drivers: Balanced regulatory approach, steady technological progress, and selective implementation based on clear ROI.
Probability: 50-60%
Conservative Scenario
Technical and organizational barriers limiting effective adoption
Key Drivers: Restrictive regulations, technical limitations, implementation challenges, and risk-averse organizational cultures.
Probability: 15-20%
Scenario Comparison Matrix
| Factor | Optimistic | Base Case | Conservative |
|---|---|---|---|
| Implementation Timeline | Accelerated | Steady | Delayed |
| Market Adoption | Widespread | Selective | Limited |
| Technology Evolution | Rapid | Progressive | Incremental |
| Regulatory Environment | Supportive | Balanced | Restrictive |
| Business Impact | Transformative | Significant | Modest |
Transformational Impact
Technology becoming increasingly embedded in all aspects of business operations. This evolution will necessitate significant changes in organizational structures, talent development, and strategic planning processes.
The convergence of multiple technological trends—including artificial intelligence, quantum computing, and ubiquitous connectivity—will create both unprecedented security challenges and innovative defensive capabilities.
Implementation Challenges
Technical complexity and organizational readiness remain key challenges. Organizations will need to develop comprehensive change management strategies to successfully navigate these transitions.
Regulatory uncertainty, particularly around emerging technologies like AI in security applications, will require flexible security architectures that can adapt to evolving compliance requirements.
Key Innovations to Watch
Artificial intelligence, distributed systems, and automation technologies leading innovation. Organizations should monitor these developments closely to maintain competitive advantages and effective security postures.
Strategic investments in research partnerships, technology pilots, and talent development will position forward-thinking organizations to leverage these innovations early in their development cycle.
Technical Glossary
Key technical terms and definitions to help understand the technologies discussed in this article.
Understanding the following technical concepts is essential for grasping the full implications of the security threats and defensive measures discussed in this article. These definitions provide context for both technical and non-technical readers.
platform intermediate
electric vehicle intermediate
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