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Huang is expected to meet Taiwan Semiconductor Manufacturing Company chairman and chief executive C. C. Wei as Nvidia prepares a second-half production ramp for Vera Rubin, the next-generation platform that combines new Vera CPUs, Rubin GPUs, advanced memory and high-speed networking for rack-scale AI systems. The visit has drawn close attention because Nvidia’s product cycle is now tied more tightly than ever to Taiwan’s foundries, packaging houses, server assemblers and component suppliers.

Nvidia reported revenue of $81.6bn for the quarter ended April 26, up 85 per cent from a year earlier, with data-centre revenue reaching $75.2bn, up 92 per cent. The company also authorised an additional $80bn in share repurchases and raised its quarterly dividend to 25 cents a share from 1 cent, underscoring the scale of cash generation from the AI infrastructure boom.

Demand is being driven by cloud providers, AI model developers, enterprise customers and sovereign AI projects, all seeking clusters capable of training and running larger generative and agentic AI systems. Nvidia has described the buildout of “AI factories” as one of the largest infrastructure expansions in technology history, with its platform spanning chips, networking, systems software and reference designs for data centres.

Vera Rubin is expected to follow the Blackwell and Grace Blackwell generation, which is still being shipped in high volumes. That overlap is creating pressure across Taiwan’s supply chain because new capacity must support existing commitments while preparing for a platform with different requirements in advanced packaging, high-bandwidth memory integration, board assembly, cooling and system validation.

Huang has described Vera Rubin as potentially the biggest product ramp in Nvidia’s history and one of the largest technology manufacturing efforts Taiwan has handled. Industry estimates around the platform point to millions of parts across each system and the involvement of well over 100 Taiwan-based ecosystem partners, from chip fabrication and advanced packaging to printed circuit boards, power systems, connectors, thermal modules and final server integration.

TSMC remains central to the effort because Nvidia depends on its leading-edge process technology and advanced packaging capacity. The challenge is not limited to wafers. CoWoS and other packaging technologies have become strategic bottlenecks as AI accelerators require multiple logic dies, high-bandwidth memory stacks and dense interconnects. Any delay in packaging expansion can ripple through server production schedules, cloud deployment plans and Nvidia’s revenue timing.

Taiwan’s broader technology sector has become a critical artery for the global AI trade. TSMC, Foxconn, Quanta, Wistron, Wiwynn, Inventec and a dense network of specialist suppliers are competing to add capacity while dealing with constraints in land, power, water, skilled labour and equipment availability. The concentration gives Nvidia speed and engineering depth, but also exposes the company to geopolitical and operational risks.

China remains the clearest commercial setback. Huang has said US export controls have pushed Nvidia’s share of China’s advanced AI chip market to zero, a striking reversal for a company that once held a dominant position there. The restrictions have strengthened the opening for Huawei and other domestic suppliers, while leaving Nvidia dependent on whether Washington and Beijing allow compliant chips to be sold without triggering national-security objections.

That loss has not slowed Nvidia’s global momentum, but it has changed the composition of growth. Revenue is now increasingly concentrated in markets where hyperscale cloud companies, enterprise AI developers and governments are building large AI data centres outside China. For investors, the question is whether demand can keep absorbing each successive architecture at the scale implied by Nvidia’s guidance.

Nvidia forecast revenue of about $91bn for the current quarter, above market expectations, signalling that orders for AI infrastructure remain strong despite concerns about overcapacity, power shortages and the pace at which customers can monetise AI services. Operating expenses rose 49 per cent to $7.75bn, reflecting the growing cost of research, supply commitments and ecosystem investments needed to sustain the company’s lead.

The Tokyo-listed investment group has become one of the most closely watched proxies for AI demand after committing tens of billions of dollars to OpenAI and building exposure across chips, power infrastructure, robotics and data-centre supply chains. The latest rally extended a sharp two-session advance that added tens of billions of dollars to SoftBank’s market value, marking one of its strongest runs since the technology boom that first made the group a symbol of risk-taking capital.

Investor enthusiasm has been driven by two linked expectations. OpenAI is preparing for a confidential US initial public offering filing that could set up a listing as early as September, while SB Energy, a power and data-centre infrastructure company backed by SoftBank and OpenAI, has said it intends to confidentially file for a US IPO. Together, the developments have strengthened the market view that SoftBank’s private holdings may be moving closer to public-market price discovery.

SoftBank’s OpenAI position is the main driver of that reassessment. The group has invested $34.6bn in the ChatGPT maker since September 2024 and has agreed to make a further $30bn investment through SoftBank Vision Fund 2. Completion of those tranches would lift its cumulative investment to $64.6bn for an ownership interest of about 13 per cent. The preferred shares are designed to convert automatically into common shares if OpenAI completes an IPO or related listing transaction.

OpenAI’s valuation has become central to SoftBank’s investment case. The company was valued at $852bn in private markets and preliminary discussions around a listing have included the possibility of a valuation as high as $1tn. The company is also seeking to raise at least $60bn at the lower end of earlier discussions, reflecting the huge capital needs of frontier AI model development, data centres, chips and power supply.

The OpenAI bet has already reshaped SoftBank’s earnings profile. The group reported a sharp profit rebound, helped by valuation gains tied to OpenAI, and said cumulative gains on the investment had reached $45bn. Its annual profit of about ¥5tn was described by the company as the highest recorded by a Japanese company, underscoring how quickly Son’s AI strategy has altered investor perceptions after years of scrutiny over the Vision Fund’s uneven performance.

SB Energy has added another layer to the rally because its business sits at the intersection of AI and power infrastructure. Founded in 2019, the company develops large-scale energy and data-centre projects and has positioned itself to benefit from surging electricity demand linked to AI workloads. It has raised more than $18bn in project capital and has a portfolio of about 5 gigawatts of operating and under-construction energy assets.

The company’s January partnership with OpenAI under the Stargate initiative placed it inside a broader push to build AI and energy infrastructure in the United States. That has made SB Energy more than a conventional renewable power developer in the eyes of investors. Its appeal rests on the argument that AI expansion will require dedicated power generation, grid connections and purpose-built campuses capable of supporting high-density computing.

SoftBank’s rally has also been supported by strength in Arm Holdings, the UK chip designer in which the group holds a large stake. Arm remains a key public asset for SoftBank and is viewed as a beneficiary of AI-related semiconductor demand. Gains in Arm shares have helped reinforce confidence that SoftBank owns multiple assets tied to the same structural theme, rather than a single concentrated wager on OpenAI.

The optimism carries risks. OpenAI faces intensifying competition from Alphabet’s Gemini, Anthropic’s Claude and other model developers, while the cost of training and operating advanced AI systems continues to rise. Credit analysts have raised concerns that the size of SoftBank’s OpenAI commitment may affect liquidity and asset quality, particularly as the group uses loans, asset sales and financing backed by holdings such as Arm and SoftBank Corp. to fund its strategy.

SoftBank arranged a $40bn bridge loan in March and drew $20bn in April, largely for the OpenAI investment, while also repaying part of that borrowing. The group has sold stakes in holdings including Nvidia and T-Mobile, issued bonds and considered other financing options as it reallocates capital towards AI. Chief Financial Officer Yoshimitsu Goto has said the group may also explore ways to use its OpenAI assets for financing if market conditions allow.

A 2026 software supply chain security assessment shows that about 65 per cent of organisations in India cannot detect malicious packages, while 71 per cent do not use container security tools. The findings underline a widening gap between the speed of AI-led software development and the controls needed to secure code, dependencies, models and deployment pipelines before they reach production systems.

Security teams are facing a more complex threat environment as AI coding assistants, model registries, agent frameworks and machine-learning libraries become embedded in day-to-day software engineering. AI is no longer confined to application features or analytics layers. It is becoming part of the software supply chain itself, influencing how code is written, tested, packaged and deployed.

Enterprises in India have been quick to adopt AI tools to improve developer productivity, reduce repetitive work and accelerate product releases. Yet the same shift has increased reliance on open-source packages, pre-trained models, plug-ins and automated build systems. Each of these components can introduce hidden risks if provenance, integrity and behaviour are not continuously verified.

Global software repositories are now attracting sustained attacks because they provide a direct route into enterprise development environments. Malicious npm packages rose by 451 per cent during 2025, while more than 48,000 new Common Vulnerabilities and Exposures were disclosed worldwide, a rise of about 20 per cent from the previous year.

The pressure on security teams is being amplified by the volume of AI-generated code. Developers in India are spending about 51 per cent of their time reviewing, validating and hardening code produced by AI tools. About 53 per cent treat such output only as a starting point and review every line before use, while 11 per cent rewrite AI-generated fixes from scratch.

This shift has altered the economics of software engineering. AI may reduce the time needed to draft code, but it can also transfer effort to verification, testing and security review. Weaknesses such as cross-site scripting, SQL injection and missing authorisation checks can still be reproduced by automated tools, particularly when developers rely on generated code without adequate scrutiny.

Governance is emerging as another area of concern. Nearly 97 per cent of organisations surveyed said they had certified AI model governance programmes, but only 59 per cent claimed full provenance visibility across production environments. About 48 per cent still needed a week or longer to produce audit-ready compliance evidence.

That gap points to a difference between policy and enforceable control. Many enterprises have AI governance frameworks on paper, but fewer can prove where every model, library, container image and software artefact came from, how it changed, who approved it and whether it complies with internal policy.

CERT-In has also warned that software supply chain compromises are increasingly targeting developer tools and trusted repositories. Its April 2026 activity note referred to attacks affecting npm, PyPI, GitHub Actions and container registries, including incidents involving Checkmarx, Trivy, LiteLLM, Axios, Telnyx and npm packages linked to the CanisterWorm campaign. The payloads observed in these attacks included credential harvesting, data exfiltration, remote execution and persistence within developer systems.

Model registries have added another layer to the risk. Around 1.4 million new AI artefacts were published on Hugging Face during 2025, representing 58 per cent of all new software packages tracked in one assessment. Researchers identified 495 malicious AI models in public repositories with active payloads capable of credential theft, command execution and reverse-shell activity, along with 969 malicious AI-agent skills designed to exploit developer environments and automation workflows.

For enterprises in India, the challenge is sharpened by the country’s role as a major software engineering and global capability centre hub. Bengaluru, Hyderabad, Pune, Chennai and Gurugram host large engineering teams serving banking, retail, telecoms, manufacturing and cloud services. These centres are increasingly responsible not only for maintenance work but also for product development, AI integration and security-sensitive digital platforms.

Regulatory and customer expectations are also tightening. Buyers are asking for software bills of materials, vulnerability disclosure processes, provenance records and proof that build pipelines are protected. CERT-In’s technical guidance on SBOM, QBOM, CBOM, AIBOM and HBOM has emphasised regular updates, vulnerability exploitability exchange documents and staff training around software supply chain visibility.

Security leaders are therefore moving towards stronger controls across development pipelines. These include automated malicious package detection, container image scanning, secrets detection, signed artefacts, dependency pinning, model provenance checks, policy enforcement in CI/CD systems and continuous monitoring of developer environments.

The investment case is becoming harder to ignore. Software supply chain attacks can bypass perimeter defences because they enter through trusted development channels. Once inside, malicious packages can steal credentials, alter builds, move into cloud environments or compromise downstream customers. AI raises the stakes because it can accelerate both legitimate development and attacker experimentation.