Microsoft has sharpened the timeline in the global quantum computing race, unveiling a new quantum chip developed with the aid of artificial intelligence and declaring that commercially useful quantum computers could arrive by 2029.

The announcement places Microsoft alongside rivals racing to turn quantum computing from a scientific experiment into a commercially viable technology. The company now expects practical quantum machines to emerge within three years, matching a timetable recently outlined by IBM and intensifying competition with Alphabet, Amazon, and a growing number of Chinese quantum research initiatives.

At stake is a technology that could fundamentally alter computing. Quantum systems are expected to solve problems involving molecular simulations, drug discovery, advanced materials, logistics optimization, and cryptography at speeds unattainable by conventional computers. Governments and corporations view quantum computing as a strategic technology with implications for economic competitiveness and national security.

The centerpiece of Microsoft’s latest push is Majorana 2, a successor to the company’s first Majorana chip introduced last year. Unlike many competitors that rely on aluminum-based superconducting materials, Microsoft’s new chip uses lead, a material the company says delivers dramatic performance improvements.

According to Microsoft executive vice president Jason Zander, AI-powered materials science tools played a critical role in identifying and engineering the new approach. The company says the redesign generated a thousand-fold improvement in certain performance metrics compared with the previous generation.

“The reason why people don’t use it to build chips is it requires an incredibly specialized process to be able to go figure that out. And we figured it out,” Zander said.

Currently, AI is increasingly being used not only to develop software but also to accelerate scientific discovery and engineering. Technology companies are investing heavily in AI-driven materials research, hoping to shorten development cycles for semiconductors, batteries, pharmaceuticals, and next-generation computing systems.

For Microsoft, the development also strengthens a broader strategy that combines leadership in artificial intelligence with ambitions in quantum computing. If successful, the company could eventually integrate quantum capabilities with its cloud and AI platforms, creating a powerful competitive advantage in enterprise computing.

Yet Microsoft’s announcement also revives long-running scientific disputes surrounding its quantum research. The company’s quantum architecture relies on exotic quasiparticles known as Majoranas. Microsoft has long argued that these particles could enable more stable quantum computers that are less prone to errors than competing approaches.

However, the existence and practical implementation of Majorana-based quantum systems have remained subjects of intense debate within the scientific community. Microsoft previously faced scrutiny over research related to Majorana particles, and some physicists continue to question whether the company has provided sufficient evidence to support its claims.

Henry Legg, a lecturer in quantum physics at the University of St. Andrews, challenged the company’s latest announcement.

“Microsoft can use as much lead as they like – it is not going to shield them from the basic scientific principle that your results need to be reproducible,” he said.

Critics argue that independent researchers need access to more experimental data to verify Microsoft’s findings. Some have pointed to concerns surrounding earlier studies and say similar questions remain unresolved.

Microsoft counters that much of the underlying data cannot be publicly released because of intellectual property concerns. Company executives say detailed information has been shared with the U.S. Defense Advanced Research Projects Agency (DARPA), which is evaluating multiple quantum computing approaches.

“We’ve done enough of the physics to really have great data,” Zander said. “Believe me, I would not spend the money on the engineering if I felt like we were still off on the physics.”

As billions of dollars flow into quantum research, companies face pressure not only to demonstrate scientific progress but also to convince investors, customers, and governments that practical systems are within reach.

Just weeks ago, IBM announced plans to invest $10 billion in quantum computing and outlined its own roadmap toward fault-tolerant systems. Meanwhile, China continues to pour resources into quantum research as part of its drive to achieve technological self-sufficiency and reduce dependence on Western technologies.

OpenAI rival Perplexity CEO Aravind Srinivas has zeroed in on what he believes will ultimately determine the winners in the artificial intelligence race: the ability to deliver maximum economic value from the energy consumed by AI systems.

In a CNBC interview on Wednesday, Srinivas argued that the companies best able to maximize “token value per watt per user”, essentially delivering the highest useful output per unit of energy and per user, will command the highest valuations in the long term.

A token is the basic unit of data that an AI model processes when handling a query or task. Each token requires computational power and, by extension, electricity.

“Whoever is able to maximize this particular objective really will, by balancing accuracy, latency, cost, privacy and intelligence all together, they’re going to win. That’s what’s going to win long term,” Srinivas told CNBC’s Elaine Yu.

He acknowledged that current AI spending patterns raise legitimate questions. Many companies are pouring billions into infrastructure with limited visibility into returns, creating significant waste.

“You hear companies saying, I am spending a ton of money on AI. And I know some great stuff is happening, but I know there’s a ton of waste. How long do I have to wait for it to really show up in revenue, and how long do I have to wait to really get the costs under control?” he asked.

Srinivas views this as a temporary phase. He believes the industry will quickly improve efficiency, but the companies that solve the energy-to-value equation first will build durable advantages.

The Shift Toward Agentic AI and Orchestration

Perplexity is positioning itself at the center of this efficiency drive through a strong focus on agentic AI — systems capable of handling complex, multi-step tasks autonomously rather than simple prompt-and-response interactions. In February, the company launched Perplexity Computer, an agent designed to execute extended workflows.

A key innovation is the newly introduced Personal Computer tool, which Perplexity calls an “orchestrator.” This system intelligently decides which AI model to use for a given task, how different agents should collaborate, and whether processing should occur locally on a device or in the cloud.

On Wednesday, Perplexity announced that Personal Computer is now available on Microsoft’s Windows operating system, allowing integration with apps like Word and Outlook, in addition to its existing availability on Apple’s Mac platform.

Srinivas emphasized the importance of this layer, saying: “The data center is coming to your laptop… this is an orchestration problem. We believe that by solving that, we’ll be building a pretty valuable company that has endurable, long-term advantage.”

By acting as a neutral orchestrator that works across different models (including those from Anthropic), chips, operating systems, and hardware providers, Perplexity aims to create a versatile “AI operating system” that optimizes for multiple objectives simultaneously — cost, speed, privacy, and performance.

Platform-Agnostic Strategy in a Crowded Field

Perplexity, last valued at around $20 billion, trails far behind larger rivals such as Anthropic (nearing $1 trillion) and OpenAI (over $850 billion). Anthropic confidentially filed for an IPO this week, highlighting intense investor appetite for AI companies.

The competitive landscape is intensifying. OpenAI, Anthropic, and Google are all ramping up agentic capabilities, while Microsoft and Apple are building their own AI agents and assistants. Microsoft unveiled new coding and reasoning models on Tuesday, and Apple is updating Siri using Google’s AI technology.

Srinivas remains confident in Perplexity’s differentiated approach.

“I think they absolutely will try to build their own AI systems, but we believe we’re building the most versatile operating system by making it work across different models, across different chips, across different traditional operating systems, different hardware providers, different laptops. That hybrid neutral orchestration layer is what we are doing, and that allows us to balance all the different objectives simultaneously,” he said.

He noted that Perplexity has tripled its annualized revenue since the beginning of the year, largely “thanks to model advances that have been made by Anthropic,” whose models are integrated into Perplexity’s platform. This highlights a key strength of the orchestration strategy: the company benefits from rapid improvements by frontier model providers without bearing the full cost of developing them in-house.

Why Efficiency Will Define the Next Phase of AI

Srinivas’s emphasis on energy efficiency and orchestration addresses a growing tension in the AI industry. Hyperscalers are spending hundreds of billions on data centers and chips, yet real-world utilization rates remain low in many cases, and returns on investment are still being proven at scale. The focus on edge computing, running AI locally on devices like laptops and phones, could dramatically reduce power consumption, improve speed and privacy, and lower latency.

This shift from cloud-only to hybrid cloud-edge architectures represents a potential inflection point. Companies that master orchestration are considered to be better positioned to deliver cost-effective, responsive AI experiences while minimizing environmental impact and energy costs — factors increasingly important to both regulators and corporate buyers.

For Perplexity, this strategy aims to create a sustainable moat in a field where raw model performance alone may not guarantee long-term leadership. The company hopes to carve out a valuable niche as the “AI operating system” layer by focusing on intelligent routing and system-level optimization rather than solely chasing the largest models.

Chinese artificial intelligence startup DeepSeek is preparing to raise about 50 billion yuan ($7.4 billion) in its first external funding round, a deal that could value the company at as much as 400 billion yuan ($59 billion) and further cement its position as the centerpiece of China’s AI ambitions.

The proposed fundraising wields enormous weight not only because of its size, but also because of the coalition of investors gathering behind the company. Technology giant Tencent Holdings, battery leader CATL, state-backed investment vehicles, and several of China’s largest internet firms are expected to participate, underscoring a coordinated effort to strengthen domestic AI capabilities amid intensifying technological competition with the United States.

If completed on the reported terms, the financing would rank among the largest private AI funding rounds globally and would place DeepSeek among the world’s most valuable privately held technology companies.

China’s AI Champion Attracts Strategic Backers

According to people familiar with the discussions cited by Reuters, DeepSeek founder Liang Wenfeng is expected to contribute 20 billion yuan of his own capital, demonstrating an unusually large founder commitment.

Tencent is reportedly considering an investment of 10 billion yuan, while CATL could contribute approximately 5 billion yuan. Other potential investors include NetEase, JD.com, the state-backed China National AI Fund, investment firm IDG Capital, and Monolith Capital.

The investor list illustrates how AI is increasingly becoming a national strategic priority rather than merely a venture capital opportunity.

Unlike earlier technology cycles that were driven primarily by internet platforms or consumer applications, AI development requires enormous investments in computing infrastructure, semiconductors, electricity, data centers, and advanced software engineering talent. As a result, the companies backing DeepSeek span multiple sectors critical to the AI value chain.

To some analysts, the involvement of CATL rings a bell. Best known as the world’s largest electric vehicle battery manufacturer, CATL has increasingly expanded into energy storage systems and power infrastructure. Its interest in DeepSeek is thus seen as a reflection of a growing recognition that electricity and computing capacity are becoming inseparable components of AI development.

As AI models grow larger and more computationally demanding, access to reliable power infrastructure is emerging as a strategic advantage.

Industry analysts describe the AI race as a competition involving not only algorithms and chips but also electricity generation, grid capacity, and energy storage. CATL’s participation suggests China’s corporate sector is taking a position to capture opportunities across the entire AI ecosystem.

However, Tencent’s interest carries a different strategic rationale. While the company has developed its own Hunyuan large language model, it has struggled to establish the same level of market momentum achieved by DeepSeek and rivals such as ByteDance’s Doubao and Alibaba’s Qwen ecosystem.

A closer relationship with DeepSeek is expected to provide Tencent with greater exposure to one of China’s fastest-growing AI platforms while strengthening its position against domestic competitors.

DeepSeek’s Rise Altered Global AI Assumptions

DeepSeek emerged as a major force in the AI industry after its V3 and R1 models attracted widespread attention from researchers and technology executives around the world.

The company’s progress challenged a long-standing assumption in parts of Silicon Valley that U.S. firms would maintain a substantial lead over Chinese competitors due to export restrictions on advanced chips and broader technology controls.

DeepSeek has demonstrated that Chinese developers could remain highly competitive even under significant hardware constraints. Its models triggered renewed debate about whether algorithmic efficiency and software innovation could partially offset limitations in access to cutting-edge semiconductors.

The company quickly became a symbol of China’s ability to develop frontier AI technologies despite restrictions imposed by Washington on advanced computing exports.

Over the past several years, U.S. export controls have restricted Chinese access to advanced AI processors from companies such as Nvidia and Advanced Micro Devices. Those restrictions have accelerated domestic investment in Chinese alternatives spanning chips, cloud infrastructure, software frameworks, and AI models.

The expected DeepSeek financing illustrates how China is responding by mobilizing both private capital and state-backed resources to support national technology champions. Rather than relying solely on government funding, the approach involves aligning major corporations, investment funds, and industrial partners behind strategically important companies.

Valuation Raises New Questions

While DeepSeek’s growth trajectory has impressed investors, the proposed valuation of $52 billion to $59 billion will inevitably invite comparisons with AI leaders in the United States. Although the funding would place DeepSeek among the world’s most highly valued AI startups, it’s still below recently reported valuations for firms such as OpenAI and Anthropic.

The challenge for DeepSeek, as for many AI companies globally, will be translating technological leadership into sustainable commercial returns. AI model development requires continuous spending on computing power, research talent, and infrastructure. Investors are increasingly scrutinizing whether today’s AI valuations can ultimately be justified by future revenue and profitability.

Nevertheless, the willingness of major Chinese corporations to commit billions of dollars suggests confidence that DeepSeek will play a central role in China’s long-term AI strategy.

Alphabet’s decision to raise $80 billion through a massive share sale to finance its artificial intelligence expansion is being viewed as a watershed moment for global capital markets.

The announcement comes as AI infrastructure spending accelerates across the technology sector, forcing even the world’s largest and most profitable companies to seek unprecedented amounts of capital to build data centers, secure computing power, and maintain their competitive positions in a rapidly evolving industry.

Speaking to CNBC, Goldman Sachs International co-Chief Executive Officer Anthony Gutman described the transaction as something markets have never encountered before.

“Let’s start by saying this is unprecedented territory, so we all enter it with a degree of humility and caution, and the right balance of focus,” Gutman said. “The Alphabet issuance yesterday augurs well for the pipeline. That was just a record level of issuance on any level.”

The fundraising package includes a $10 billion allocation to Berkshire Hathaway, which Alphabet said will help support investments in the company’s expanding AI computing infrastructure as it seeks to meet surging customer demand for AI services. The offering is being managed by a syndicate led by  Goldman Sachs, JPMorgan Chase, and Morgan Stanley, highlighting the scale and complexity of the transaction.

For years, major technology firms largely relied on cash flow and debt markets to fund growth. The AI boom has altered that equation. Building and operating advanced AI systems requires vast investments in graphics processors, networking equipment, power infrastructure, and data centers, creating capital requirements that are unprecedented even by Silicon Valley standards.

Industry analysts note that AI infrastructure spending is increasingly resembling the buildout of national utilities rather than traditional software development. Companies are investing tens of billions of dollars annually in computing capacity, with returns expected over many years rather than quarters.

Despite the sheer size of Alphabet’s offering, Gutman argued that investor appetite remains strong.

“There is a lot of demand out there” for significant equity issuance, he said, adding that, viewed as a percentage of overall global market capitalization, the fundraising appears “very manageable.”

His comments suggest Wall Street believes investors remain willing to finance the next phase of AI expansion, particularly when the issuers are dominant technology firms with proven business models and strong cash generation.

The transaction could also serve as a critical test case for a growing pipeline of technology companies seeking access to public markets.

Capital markets are already preparing for a series of potentially historic listings. Most closely watched is the planned initial public offering of SpaceX, which is expected to debut on Nasdaq on June 12 with a targeted valuation of approximately $1.75 trillion. If achieved, it would become the largest IPO ever recorded.

Meanwhile, AI leaders OpenAI and Anthropic have also indicated plans to pursue public listings later this year, potentially creating another wave of mega-cap offerings tied directly to the AI sector.

The prospect of multiple blockbuster deals arriving within months has prompted comparisons to earlier technology booms. Yet many bankers argue the current cycle differs because it is being driven not merely by speculative enthusiasm but by tangible infrastructure requirements. AI companies need enormous amounts of capital to build and maintain computing capacity, creating a financing need that extends far beyond traditional growth funding.

For investors, the key question is whether markets can continue absorbing increasingly large equity offerings without diluting valuations or exhausting demand. So far, sentiment remains constructive.

“We’re excited about it. These are exceptional companies, so they should be able to raise this capital if they navigate the path appropriately,” Gutman said.

The success of Alphabet’s fundraising effort may therefore carry significance beyond the company itself. Some believe that if investors absorb the $80 billion issuance smoothly, it could provide a roadmap for other technology giants seeking capital and reinforce confidence that public markets can support the enormous financial requirements of the AI era.

More broadly, the deal highlights how artificial intelligence is reshaping not only technology but also global finance. As companies race to build the infrastructure underpinning the next generation of computing, capital markets are being asked to fund projects on a scale rarely seen outside government spending programmes or major industrial revolutions.