Tesla shares are nearly back to pre-crisis levels after suffering a sharp drop last week following Elon Musk’s highly public spat with President Donald Trump.

The stock surged 5.7% on Tuesday to close at $326.09, putting it just about $6 short of its price before Musk and Trump’s social media clash sent the company’s market cap spiraling by 14% in a single session.

The recovery is being interpreted by investors as a turning point, not just for Tesla’s stock, but for Elon Musk’s public priorities.

Robotaxi Sparks Rally—But It’s About More Than Cars

The latest rally followed the release of an eight-second video showing a Tesla Model Y operating autonomously on the streets of Austin, Texas, without a safety driver. The vehicle, painted black and bearing a graffiti-style “Robotaxi” logo, was seen navigating an intersection, slowing for pedestrians at a crosswalk. Tesla fans hailed it as a long-awaited public appearance of the company’s Full Self-Driving (FSD) software operating in “unsupervised” mode.

Musk amplified the clip on X, calling it a “beautifully simple design” and emphasizing that these were regular, unmodified Teslas, meaning future robotaxis are already rolling off the production line. A pilot ride-hailing program using about 10 to 20 Model Y vehicles is expected to launch in Austin on June 12, though Tesla has not formally confirmed the date. For now, the cars will operate within a geofenced zone and be remotely monitored by employees who can intervene if needed.

Despite the excitement, analysts and researchers have warned against reading too much into the short clip. Philip Koopman, an autonomous vehicle safety researcher at Carnegie Mellon, told CNBC the video doesn’t offer enough information to evaluate how safe or effective the system really is.

Even so, the market appears encouraged—not just by the robotaxi test, but by what the test signals: a Musk who’s refocusing on Tesla after months of political entanglements and public controversies.

Exit from DOGE Marks a Turning Point

Tesla’s rebound coincides with Musk stepping down from his high-profile, though informal, role as head of the Department of Government Efficiency (DOGE) under the Trump administration. Though the post was largely symbolic, it positioned Musk at the intersection of policy, tech, and power—and increasingly distracted him from his core ventures.

Since exiting DOGE, Musk has become more publicly engaged in Tesla’s operations and vision, starting with renewed pushes on autonomy and streamlining the company’s AI development pipeline. Analysts say the stock’s recent uptick may be the market pricing in the benefits of Musk returning to business leadership, where his strategic instincts have typically shone.

His recent fallout with Trump makes a return to politics even less likely in the near term. The two men, once aligned on regulatory rollbacks and space policy, began trading insults last week following disagreements over a new spending bill. Trump threatened to end federal contracts and subsidies for Musk’s companies, including Tesla and SpaceX. Musk, in turn, said SpaceX will ‘immediately decommission’ its Dragon capsule with NASA.

For investors, that’s welcome news.

“The more time Musk spends engineering and shipping products, the better it is for Tesla,” one analyst at a major investment firm told CNBC. “The political distractions were hurting the brand and the stock.”

Pressure to Refocus—and Perform

Tesla’s first quarter was a rough one. Automotive revenue fell 20%, as Chinese competitors like BYD and XPeng undercut Tesla on price, and Musk’s personal brand began to erode consumer enthusiasm in key markets like Germany and mainland China. Tesla also faced pushback in Europe, where sales slid in the early months of the second quarter.

Even before the Trump-Musk feud, investors were already pressuring the CEO to concentrate on product rollouts, not political posturing. The robotaxi reveal, along with the apparent dialing down of his political engagements, seems to signal that Musk is listening—at least for now.

Tesla is currently listed as “testing” on the City of Austin’s official autonomous vehicle registry, joining companies like Amazon’s Zoox and AVRide. Only Waymo, a subsidiary of Alphabet, holds a “deployment” designation, underscoring how far Tesla still has to go in commercializing autonomous mobility.

However, for bulls like Piper Sandler, that process has now officially begun. “A key component of our TSLA thesis has officially begun playing out,” the firm wrote in a Tuesday note, reaffirming its “buy” rating.

While Musk’s political estrangement may have opened a clearer path for Tesla to recover both public trust and investor confidence, the company still needs to navigate safety concerns and roll out a viable robotaxi service.

The big question now is whether Musk stays the course.

With Trump clearly drawing battle lines, and DOGE no longer occupying his calendar, all signs point to a Musk who is—for the first time in months—all in on Tesla. And the market, at least for now, appears to be rewarding that.

Vast Data, the nine-year-old enterprise data storage company quietly powering some of the world’s most demanding AI workloads, is back in the market to raise a fresh round of funding—this time at a proposed valuation of $25 billion, according to a person familiar with the deal.

The move would mark a monumental leap from its $9 billion Series E valuation in December 2023 and signals just how aggressively investor sentiment has shifted in favor of companies building the underpinnings of the AI revolution.

Although the terms of the round are not yet finalized, and insiders say the valuation could change, interest in Vast from venture capitalists has intensified. The startup has emerged as a key enabler of large-scale AI deployment, offering a data infrastructure that handles the growing complexity, volume, and speed demands of modern artificial intelligence applications.

At the core of Vast’s appeal is its AI-native storage platform, designed to unify all types of data—structured, semi-structured, and unstructured—under one high-speed, flash-based system. Unlike traditional systems that rely on storage tiers—slower, cheaper storage for archival data and high-performance storage for active datasets—Vast’s architecture collapses those silos, allowing immediate access to massive volumes of data. This structure is particularly critical for AI model training and inference, where delays in data retrieval can slow down entire pipelines.

Its platform, paired with hardware from Supermicro, HPE, and Cisco, is already trusted by next-gen AI cloud providers like Coreweave and Lambda, as well as established enterprises such as Pixar, ServiceNow, and Xai, Elon Musk’s AI research company. These clients use Vast not only for storage but also for the performance benefits it brings to AI workloads that require rapid and frequent data access.

Economic Impact of AI Behind Valuation Surge

Vast’s bid for a $25 billion valuation isn’t occurring in a vacuum. It underscores a broader market trend: the economic gravitational pull of AI, which is projected to generate up to $15.7 trillion in global economic value by 2030, according to a report by PwC. Of that, nearly $6.6 trillion is expected to come from increased productivity, with the rest driven by consumption-side effects, including new product and service offerings.

As the AI wave accelerates, so does the demand for the foundational infrastructure—computing, storage, and data management—that enables it. Vast Data sits at the center of that value chain, providing the kind of high-throughput storage systems needed to make large language models (LLMs), generative AI tools, and autonomous systems operate efficiently.

It’s why Vast’s year-over-year revenue has reportedly been growing at 2.5x to 3x, and why it hit $200 million in annual recurring revenue (ARR) when it last raised funds 18 months ago. CEO Renen Hallak said on a podcast in May 2023 that the company has also been free cash flow positive for four consecutive years, further bolstering investor confidence.

From Storage to Full-Stack Data Infrastructure

What sets Vast apart is its strategic ambition to move beyond storage. The company is building a next-generation database architecture that could position it to compete with Databricks, a $43 billion juggernaut in unified data analytics and AI services. This move transforms Vast from a storage provider into a broader data infrastructure company, helping customers not only store data but optimize how it flows into AI models and enterprise systems.

That ambition places it in a different league compared to rivals like Weka, which raised $140 million last year at a $1.6 billion valuation, or even Pure Storage, a publicly traded firm with a market cap of around $17 billion. If Vast secures its new valuation, it will leapfrog both to become one of the most valuable private companies in the enterprise AI space.

To date, Vast has raised $381 million from heavyweight backers such as Fidelity Management & Research Company, NEA, BOND Capital, and Drive Capital. The next round, if closed at the intended figure, would not only rank as one of the largest step-ups in enterprise tech but also validate the thesis that data infrastructure is as valuable as AI models themselves.

The Silent Engine of the AI Boom

As consumer-facing AI firms like OpenAI and Anthropic dominate headlines, startups like Vast Data are emerging as the silent engines making the AI economy viable. From managing the flood of training data to delivering high-speed access for real-time inference, these infrastructure players are quietly redefining how value is created in the digital age.

And as the projected $15.7 trillion AI economy begins to take shape over the next five years, it is companies like Vast—those enabling AI to run faster, more reliably, and more affordably—that may define the next era of enterprise technology.

Huawei Technologies founder and CEO, Ren Zhengfei, has admitted for the first time that the company’s semiconductor capabilities are still a generation behind those of its American rivals.

However, he insists that Huawei is making progress through unconventional techniques such as cluster computing, as well as deeper investment in theoretical research. His remarks, published Tuesday on the front page of the People’s Daily, come at a critical moment in global geopolitics and tech rivalry, with China and the United States locked in a fierce battle for dominance in artificial intelligence and advanced computing.

“There is no need to worry about the chip problem,” Ren said, attempting to calm concerns over the fallout from U.S. sanctions that have crippled Chinese access to the world’s most advanced semiconductors. “We use mathematics to supplement physics, non-Moore’s law to supplement Moore’s law, and cluster computing to supplement single chips.”

Ren’s comments are notable not just for their candor, but also for what they signal: a quiet admission that the U.S. remains firmly ahead in cutting-edge chip development — a key pillar of the broader AI arms race between the two superpowers.

A Race for Technological Supremacy

The rivalry between the United States and China over technology supremacy, particularly in AI, semiconductors, and next-generation computing, has intensified rapidly over the past five years. What began with tariffs and trade restrictions under Washington’s broader China containment strategy evolved into a campaign of high-tech sanctions, especially targeting Huawei, China’s most prominent tech company.

In 2019, the U.S. government placed Huawei on an export blacklist, banning American firms from selling advanced chips and chip-making tools to the Chinese telecom giant. Since then, a series of increasingly stringent export curbs have not only barred Huawei from accessing top-tier semiconductors from firms like Nvidia and Intel, but have also targeted China’s access to advanced chip fabrication technologies used in Taiwan, South Korea, and the Netherlands.

The objective has been to stifle China’s ability to develop and deploy AI systems with military and strategic significance. The result, however, has been an acceleration in China’s domestic innovation push — with Huawei at the center of that effort.

Huawei’s Cluster Gambit and the AI Pivot

Blocked from purchasing the world’s most powerful chips, Huawei has turned to software and systems engineering to multiply the power of the chips it can produce. In April, the company launched its “AI CloudMatrix 384” system — a computing framework that links together 384 of its homegrown Ascend 910C chips. This high-density AI computing cluster allows developers and institutions to train advanced AI models on Chinese infrastructure, effectively creating a workaround to U.S. hardware restrictions.

Ren noted that while each chip may lag behind Nvidia’s most powerful offerings, cluster computing — where many less-powerful chips operate in parallel — allows Huawei to reach performance levels that are “practical” and competitive. Some analysts argue the system can even outperform Nvidia’s newest GB200 NVL72 in specific tasks. Dylan Patel, head of U.S.-based semiconductor research firm SemiAnalysis, wrote that Huawei’s launch demonstrated China’s capability to build high-performance AI systems “that can beat Nvidia on some metrics.”

Nevertheless, Ren was quick to downplay the hype surrounding Huawei’s progress. “The United States has exaggerated Huawei’s achievements,” he said. “We are not that great. We have to work hard to reach the level they claim.”

That statement, from Huawei’s top executive, is perhaps the clearest indication yet that China knows it still trails the U.S. in the foundational technologies powering the AI revolution.

Despite the sanctions, China has refused to slow down. Huawei, SMIC (China’s top chipmaker), and a host of other firms have ramped up spending and redirected R&D toward areas where foreign tech is restricted. According to Ren, Huawei is pouring 180 billion yuan — roughly $25 billion — annually into research, with nearly a third going into theoretical science, not just product development.

“Without theory, there will be no breakthroughs, and we will not catch up with the United States,” Ren said. This focus on homegrown science — not just engineering — marks a strategic shift, as China looks to reduce long-term dependence on foreign innovation ecosystems.

China’s strategy also hinges on building up its domestic semiconductor supply chain. The country has poured billions into chip startups, expanded its chip fabrication capabilities, and created government-backed investment funds aimed at achieving chip self-sufficiency by the end of this decade. At the same time, Chinese firms are exploring alternative materials like compound semiconductors, which Ren called promising, suggesting they could unlock new performance levels that bypass current limitations.

AI As The Flashpoint of the Rivalry

Artificial Intelligence, more than any other field, has become the flashpoint in the U.S.-China tech rivalry. Both countries see AI not just as an economic tool but as a national security imperative. The technology underpins everything from autonomous weapons and surveillance to financial systems and critical infrastructure.

The U.S. has tried to use its dominance in AI chips, particularly those from Nvidia, to slow China’s progress. But the rapid evolution of Chinese workarounds suggests that the AI race is far from settled. Ren’s remarks indicate that while China acknowledges the U.S. lead, it also sees new opportunities to leapfrog through architecture-level innovations and alternative computing strategies.

Ren’s comments were timed with the resumption of U.S.-China trade talks in London — meetings that are expected to once again spotlight the contentious issue of tech sanctions.