Japan’s SoftBank Group and Nvidia are in advanced talks to invest in Skild AI in a funding round worth more than $1 billion, a move that could value the maker of foundation models for robots at about $14 billion, according to sources and a term sheet reviewed by Reuters.

If the deal closes on schedule before Christmas, it would nearly triple the company’s valuation from the $4.7 billion it reached in a $500 million Series B round earlier this year, which drew in Nvidia, LG’s venture capital arm, and Samsung, among others, according to PitchBook data.

Founded in 2023 by former Meta AI researchers, Skild AI has emerged as one of the fastest-growing players in the bid to solve one of robotics’ hardest challenges: building general software that can serve as the decision-making core for machines of multiple shapes and uses. Instead of manufacturing hardware, Skild develops foundation models trained on vast datasets so robots can perceive their environment and make decisions with human-like fluidity. The ambition is to overcome a major constraint that has kept general-purpose robots from operating widely in both homes and industrial settings.

The company is backed by Amazon.com and Lightspeed Venture Partners, adding to the momentum around firms building the “brains” for the next generation of robots. Skild AI raised $300 million at a $1.5 billion valuation in its Series A round last year, with investments from Jeff Bezos, SoftBank, and Khosla Ventures.

The latest talks highlight the surge of investor interest in humanoid and general-purpose robotic systems as advances in artificial intelligence make these machines more capable of handling intricate tasks once considered far beyond reach. Heavyweights such as Nvidia, Samsung, AMD, and a rising group of specialized robotics software firms are locked in a race to supply the computational engines and learning systems that will underpin the category.

Even so, experts say the world is still several years away from seeing general-purpose robots deployed at scale. The technical constraints remain steep, including real-time decision-making, fine-grained manipulation, long-duration safety, and the cost of integrating such models into commercially viable machines. The industry is advancing quickly, but not at the pace implied by some of the recent enthusiasm.

SoftBank and Skild AI did not immediately respond to Reuters when asked for comment, while Nvidia declined to comment on the matter. A source familiar with the negotiations said some details could still change because the talks are fluid, though the goal is to complete the deal before Christmas. Another person close to the discussions said SoftBank was particularly impressed by Skild’s technology during pilot projects, which reinforced the company’s belief that software-driven robotics could define the next chapter of automation.

Robotics has become a centerpiece of SoftBank CEO Masayoshi Son’s strategic plans. In October, SoftBank acquired the robotics business of Swiss engineering group ABB in a $5.4 billion deal, adding to the portfolio that Son wants to build as part of a long-term robotics and automation push. The investment discussion around Skild AI fits that larger objective of gaining pole position in what Son expects to be the next major technology wave.

The funding effort comes at a moment when the U.S. government is also accelerating its attention on robotics. Commerce Secretary Howard Lutnick is holding meetings with industry CEOs to speed up development, and the Trump administration is weighing an executive order on robotics next year, according to Politico last week.

Policymakers see robotics as a strategic industry tied to productivity, supply-chain resilience, and national competitiveness, especially as manufacturing transitions toward more automated production architectures.

Skild AI introduced its first general-purpose AI model in July, presenting it as a system that can handle a wide variety of environments and tasks ranging from warehouse logistics to household chores. The company argues that robots powered by such models could become far more versatile, reducing the need for custom engineering approaches that have slowed adoption in many industries.

Investor excitement around Skild mirrors the broader surge in robotics funding this year as companies, governments, and chipmakers position themselves ahead of what they believe will be a transformative decade. Several countries are racing to establish dominance in humanoid robotics, and China recently warned that speculative activity in its own robotics sector had reached levels that risk creating a bubble, with more than 150 companies vying to launch humanoid robots.

The competitive backdrop underscores why companies like SoftBank and Nvidia are moving quickly. Nvidia’s role is especially notable because the company has become the central supplier of the chips and compute infrastructure that power modern AI systems. Its interest in Skild fits its broader campaign to lock in the software and developer ecosystems that will rely on Nvidia hardware as humanoid systems evolve. For SoftBank, Skild represents an opportunity to strengthen its robotics strategy with software that can scale across multiple robot manufacturers and use cases.

If the round closes at the expected valuation, Skild AI would enter 2026 as one of the most valuable robotics software companies in the world, with backing from some of the most influential names in technology and venture capital. The challenge, as always, will be delivering on the promise of general-purpose robotics.

A new study has cast a sharp beam of cold light onto the auto industry’s runaway enthusiasm for artificial intelligence, warning that almost all of today’s aggressive spending will fade long before the end of the decade.

The report, released Monday by technology research firm Gartner, says only a handful of manufacturers have the structure, leadership, and long-term discipline needed to keep pushing deep into AI through 2029. It challenges the optimism that has fueled boardroom strategies, investor narratives, and headline-grabbing claims about self-driving ambitions, in-car intelligence, and automated factories.

According to Gartner, over 95% of automakers today describe themselves as being in a phase of strong AI investment growth. By 2029, that number collapses to just 5%.

The research is part of Gartner’s predictions for 2026 and suggests that the industry’s current surge of spending is not built on stable foundations. The firm concludes that only companies with strong software cores, tech-oriented leadership structures, and a clear long-term commitment to AI will keep moving forward. Everyone else risks slipping into stagnation.

This widening divide reflects a basic structural problem within the industry. Traditional carmakers such as Volkswagen were built on engineering muscle, decades of mechanical innovation, and manufacturing discipline. They grew into sprawling organizations optimized for hardware, supply-chain mastery, and incremental upgrades to combustion engines. That foundation is now a disadvantage in a world where intelligence, code, and automation increasingly determine who wins.

Gartner’s report argues that the leadership model inside legacy companies is one of the biggest obstacles. Many of these manufacturers adopted software teams only reluctantly, and too often placed them deep within the hierarchy where they lacked influence. Gartner analyst Pedro Pacheco said many firms are still dealing with internal resistance, slow decision cycles, and outdated cultural habits that treat software as an accessory rather than the engine of competitiveness.

He told Reuters that success requires turning these organizations into digital-first companies and clearing away internal roadblocks that slow innovation. That includes giving software leaders a direct line to the CEO and putting technology at the very top of strategic planning. Without that shift, he said, firms will struggle to compete with players like Tesla and BYD, which were built around software from the start.

“A company that is not great at software … is going inevitably to struggle,” Pacheco said, summing up what has become an increasingly accepted truth across the global auto landscape.

The divide is not just about who writes code well. It is about who can sustain the enormous spending required to build industry-leading AI systems. As automakers roll out advanced driver assistance, predictive maintenance, in-car voice systems, automated production lines, and next-generation battery management, their costs increasingly resemble those of the world’s tech giants. That level of spending is difficult to maintain for firms still carrying legacy manufacturing costs, debt burdens, and the weight of combustion-era supply chains.

The industry’s internal structure is colliding with another challenge: a rapidly cooling investor appetite for speculative AI projects. While AI remains the hottest narrative in global markets, investors are no longer impressed by slogans about “software-defined vehicles” unless companies can produce genuine revenue improvements. Many automakers who rushed into grand AI announcements now face pressure to justify the billions they have already committed.

The risk heading into 2026 is that companies trapped between rising costs, slow cultural change, and shifting investor sentiment may pull back from ambitious projects before they yield results. That could stall unfinished automation systems, slow the rollout of next-generation EV capabilities, and weaken attempts to develop in-house operating systems. Companies that hesitate now will only widen the gap with Tesla, BYD, and the small group of tech-forward newcomers who view AI as their native territory rather than a strategic add-on.

This trend also affects long-term competitiveness. If only 5% of the industry maintains strong AI investment growth by 2029, the rest may find themselves dependent on external suppliers for critical vehicle intelligence. That would push them closer to becoming low-margin hardware assemblers in a market where the value sits inside the software stack. Carmakers who cannot build or control their own AI systems risk losing pricing power, market influence, and brand authority.

These pressures are reshaping the competitive environment faster than expected. Tesla continues to treat software as its core operating system, using continuous over-the-air updates and integrated data loops to make each vehicle smarter over time. BYD is expanding this model at an enormous scale, blending advanced electronics with aggressive production growth in China and beyond. Their momentum amplifies the urgency felt by legacy manufacturers struggling through internal reform.

The “euphoria” described in the Gartner report has driven carmakers to announce sweeping AI ambitions. But the coming years will test whether they can pay for those promises, reorganize their cultures, and compete with companies forged in the language of code. The report’s numbers suggest the vast majority will fall short.

The auto sector enters 2026 with two realities pulling in opposite directions. One is the growing expectation that vehicles will soon operate with meaningful autonomy, predictive intelligence, and self-improving software. The other is the industry’s internal difficulty in transforming itself fast enough to deliver that future. If Gartner’s forecast holds true, the next three years will be decisive—and only a tiny fraction of automakers will emerge with the strength, vision, and long-term discipline to remain competitive in the AI race.

The rest may find themselves watching from the sidelines as a new hierarchy takes shape.