Rivian CEO says supervised point-to-point self-driving will arrive this year, and he’s comparing it directly to Tesla’s FSD



TL;DR

Rivian CEO RJ Scaringe says supervised point-to-point self-driving arrives this year on Gen 2 and R2 vehicles, with eyes-off driving in 2027.

Rivian CEO RJ Scaringe said the company will ship supervised point-to-point self-driving on all of its second-generation vehicles and the R2 later this year, describing the capability as “very similar to Tesla’s FSD.” Speaking at the Masters of Scale event in Anaheim on Thursday, Scaringe laid out a three-stage autonomy roadmap: supervised point-to-point driving in 2026, eyes-off unsupervised driving in 2027, and a commercial robotaxi service with Uber beginning in 2028.

The announcement represents a significant jump from Rivian’s current driver-assistance system. Universal Hands-Free, which rolled out in late 2025, handles steering and speed on roughly 3.5 million miles of marked roads in the US and Canada. It does not navigate turns, traffic lights, roundabouts, or parking lots.

Point-to-point driving would extend the system’s capabilities to handle complete journeys from origin to destination, similar to what Tesla’s Full Self-Driving Supervised already attempts. The leap from highway lane-keeping to full urban navigation is the hardest problem in autonomous driving, and no company has solved it without significant constraints.

Later this year, we’ll have full supervised point-to-point, which will be very similar to Tesla’s FSD,” Scaringe said. “And that’ll roll out to all of our Gen 2 vehicles and, of course, R2.” He did not specify a month or quarter for the rollout, and Rivian has not publicly demonstrated the point-to-point system in an uncontrolled environment.

The comparison to Tesla is deliberate but architecturally inexact. Tesla’s FSD relies exclusively on cameras, while Rivian’s platform integrates 10 external cameras, five radar units, 12 ultrasonic sensors, and a high-precision GPS receiver. Rivian began delivering R2 SUVs earlier this month, and future R2 models will add a roof-mounted LiDAR sensor and the company’s custom RAP1 processor, a 5nm chip delivering up to 1,600 trillion operations per second.

The pricing undercut is sharper than the technology comparison. Rivian’s Autonomy+ package costs $2,500 as a one-time purchase or $49.99 per month, compared with Tesla’s FSD at $8,000 or $99 per month. Whether the lower price reflects a competitive strategy or a difference in capability remains to be seen, given that Rivian’s point-to-point system does not yet exist as a shipping product.

Rivian’s autonomy software is built around what the company calls a Large Driving Model, a foundational AI system trained end-to-end through reinforcement learning. The LDM maps raw sensor input directly to vehicle trajectory, analysing multiple driving paths and selecting the optimal one using a technique called Group-Relative Policy Optimization. The approach mirrors the end-to-end neural network philosophy Tesla adopted with FSD v12, though Rivian’s multi-sensor hardware gives the model a wider range of input data to work with.

The 2027 eyes-off milestone is where the roadmap becomes commercially consequential. Supervised driving, regardless of how capable, still requires a human to watch the road. Tesla has been promising unsupervised FSD for years and has pushed the timeline repeatedly, most recently to Q4 2026 at the earliest. Scaringe has said Rivian targets Level 3 autonomy by 2028 and Level 4 by 2030, timelines that no autonomous driving company has consistently met.

The commercial centrepiece of the roadmap is the $1.25 billion deal with Uber announced in March. Uber committed an initial $300 million investment, with the remainder contingent on Rivian hitting autonomous performance milestones through 2031. The deal calls for Uber or its fleet partners to purchase 10,000 fully autonomous R2 robotaxis, with an option for up to 40,000 more beginning in 2030. Commercial deployment is planned for San Francisco and Miami in 2028, expanding to 25 cities by 2031.

Those targets depend on Rivian achieving something it has not yet demonstrated: a vehicle that can drive itself without human supervision. The company’s Gen 3 autonomy platform, which will power the robotaxi programme, is still undergoing validation. The initial R2 production run launched without the Gen 3 hardware, meaning the robotaxi-grade vehicles are at least one hardware generation away from production.

Scaringe framed the self-driving push as essential to Rivian’s long-term economics. The company posted a net loss of $3.63 billion in 2025 despite achieving its first full-year positive gross profit at $144 million. Autonomy, if it works, transforms the revenue model from selling cars to operating a transportation platform. But the gap between announcing a roadmap at a conference and shipping a reliable autonomous system is where most self-driving timelines have historically broken down.



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India debates sovereign AI after the US forced Anthropic to kill Fable 5, with proposals for a $5B fund and calls to embrace open-source models.

When the US government ordered Anthropic to shut down Fable 5 and Mythos 5 on 12 June, the export control directive was aimed at restricting foreign nationals from accessing America’s most capable AI. In India, Anthropic’s second-largest market, it landed as a warning shot about what happens when your AI infrastructure runs on someone else’s politics.

The suspension cut off Indian developers and enterprises from Claude’s most advanced models overnight. India’s Claude run-rate revenue had doubled since October 2025, and Tata Consultancy Services had announced a partnership just one day earlier, on 11 June, to train 50,000 employees on Claude and build a dedicated Anthropic business unit. That deal is now in limbo.

The timing has turned what was already a simmering debate about AI sovereignty into a full strategic reckoning. Proposals that sounded ambitious a week ago now sound urgent.

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Mohandas Pai, former Infosys CFO and one of India’s most prominent tech investors, has called for a ₹50,000 crore (roughly $5 billion) annual sovereign AI fund. He has also proposed a ₹2 lakh crore (approximately $21 billion) credit guarantee to finance cloud infrastructure, hardware procurement, and semiconductor development. The figures dwarf the government’s existing commitment.

India approved its IndiaAI Mission in March 2024 with a budget of ₹10,372 crore, approximately $1.25 billion. The programme has deployed around 38,000 GPUs so far. Pai’s proposal would quadruple annual spending and add a credit backstop an order of magnitude larger.

Sridhar Vembu, the founder of Zoho, has gone further. He argued that India should embrace smaller and open-source models, including Chinese ones, rather than depend on American frontier systems that can be switched off by executive order. “Technology is the ultimate weapon,” Vembu said. “Globalization is dead and Bharat must find her own way ahead.

The argument has teeth because the suspension demonstrated exactly the vulnerability Vembu is describing. Amazon’s CEO reportedly triggered the government crackdown by telling Treasury Secretary Scott Bessent that researchers had used Fable 5 to obtain information that could be used in cyberattacks. Anthropic called the action disproportionate, but compliance was immediate and global.

Policy expert Prasanto Roy put it bluntly: “American AI models are bound to American geopolitics.” For Indian enterprises that had built workflows around Claude, the lesson was that access to frontier AI is a privilege that can be revoked without notice, without consultation, and without regard for the commercial relationships it disrupts.

The Indian startup ecosystem is already adapting. Sarvam, a Bengaluru-based AI company, released 30-billion and 105-billion parameter open-source models at the India AI Impact Summit in 2026. Krutrim, founded by Ola’s Bhavish Aggarwal, has pivoted from building foundational models to providing cloud and AI infrastructure services, reporting ₹3 billion in revenue for fiscal year 2026.

Neither company is close to matching the capabilities of Fable 5 or Mythos 5. But the argument for sovereign AI was never about matching frontier performance immediately. It is about ensuring that the floor does not fall out when Washington makes a unilateral decision about who gets to use which models.

Aakrit Vaish, founder of the AI startup Activate, said the suspension “completely changes things” for the sovereign AI debate. Vijay Rayapati, CEO of Atomicwork, raised concerns about what the precedent means for Indian companies with multi-country teams that depend on American AI providers. If the US can shut off model access to enforce export controls, any country that relies on American AI is one policy decision away from disruption.

Not everyone agrees that India needs to build its own frontier models. Hemant Mohapatra, a partner at Lightspeed Venture Partners, argued that talent and compute access matter more than capital for building competitive AI. India has the engineering workforce, but the compute gap is significant, and closing it requires either massive domestic investment or continued access to foreign cloud infrastructure.

Anthropic opened a Bengaluru office as part of its India expansion, and the TCS partnership was designed to be a cornerstone of its enterprise strategy in the country. Whether those plans survive the suspension intact depends on how quickly Anthropic can restore access and whether Indian enterprises still trust a provider whose most capable models can vanish overnight.

The broader pattern is unmistakable. The US has spent four years tightening controls on AI technology, from chip export restrictions to model-level interventions. Each escalation pushes more countries toward the conclusion that dependence on American AI infrastructure carries political risk. India, with its 1.4 billion people and rapidly growing technology sector, is now asking whether it can afford that risk, and what it would cost to eliminate it.

The Opendoor layoffs in June 2026, which shut the company’s India office and affected roughly 250 employees, added another dimension. CEO Kaz Nejatian cited AI-native teams as the reason, suggesting that some US companies are using AI to reduce their reliance on Indian engineering talent at the same time that India is debating its reliance on American AI. The relationship is becoming less complementary and more competitive.

For now, the sovereign AI proposals remain proposals. Pai’s fund has no legislative vehicle, Vembu’s call for open-source adoption has no coordinated policy framework, and the IndiaAI Mission’s GPU deployment is still in early stages.

But the Anthropic suspension has done something that years of policy papers and conference speeches could not: it has given the sovereign AI movement a concrete, recent, and viscerally felt example of why dependence on foreign AI is a strategic liability. The debate is no longer theoretical.



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