Jensen Huang skips Trump’s China business delegation



The Nvidia CEO is not on the list of US executives travelling to Beijing for the Trump-Xi summit. Apple’s Tim Cook and Tesla’s Elon Musk are among those attending; the trip’s focus is reportedly agriculture, manufacturing, and aviation.


NVIDIA CEO Jensen Huang will not join President Donald Trump’s business delegation to China this week, Reuters reported on Monday, citing a source familiar with the planning.

Apple CEO Tim Cook and Tesla CEO Elon Musk are among more than a dozen US executives travelling with the president, who is scheduled to arrive in Beijing on 13 May for formal state meetings on 14 and 15 May.

Trump will meet Chinese President Xi Jinping during the visit. The White House has reportedly steered the agenda toward agriculture, manufacturing, and commercial aviation, including potential Boeing aircraft orders, rather than the AI chip-export disputes that have dominated US-China technology policy for the past three years.

Huang has been an unusually visible AI-industry counterpart for the Trump administration over the past twelve months.

NVIDIA’s CEO has appeared alongside the president at multiple events and travelled with Trump on prior overseas trips, including to the Gulf. NVIDIA‘s exclusion from the China delegation is therefore being read as a deliberate signal rather than an oversight.

The chip-export question remains the central friction in US-China technology policy. NVIDIA’s most advanced GPUs are restricted under US export controls for sale into China; the company has developed regulator-compliant variants but has continued to lobby the administration for tighter integration between national-security objectives and commercial freedom. Trump and Huang met in March to discuss export limits.

Huang has not commented publicly on the delegation list. NVIDIA’s communications team declined to comment when asked by Reuters.

Several Wall Street analysts told the wire service that Huang’s exclusion reduces the probability of any meaningful US-China announcement on AI chip access during the trip itself.

NVIDIA shares were modestly higher in pre-market US trading on Monday, with broader semiconductor indices outperforming. Analysts split on the strategic read: some viewed the exclusion as positive for Nvidia’s separation from the political process, others as a setback for the company’s quieter lobbying push to ease export restrictions.

China is a critical market for Nvidia despite the export controls. The company’s data-centre revenue from the country has continued to grow under the H20-class regulated parts; the introduction of more recent regulated variants is expected to support continued shipments.

The Bloomberg sourcing on the China-tier policy framework remains unsettled; a meaningful agreement on chip access would require either a softening of export controls or a structural concession from Beijing on associated trade issues.

Cook and Musk’s inclusion reflects the administration’s preferred mix of large US consumer-electronics and electric-vehicle exposure with Chinese manufacturing and consumption.

Apple’s iPhone supply chain remains heavily China-anchored despite recent diversification into India and Vietnam; Tesla operates its Shanghai gigafactory at scale and has been navigating the EV-incentive policies that the Chinese government has rolled back in recent quarters.

Boeing, also represented in the delegation, is reportedly the focus of a potential commercial-aircraft order announcement during the visit. China’s domestic aircraft programmes have continued to expand, but Chinese carriers retain significant demand for Boeing widebody aircraft.

Trump’s delegation flies out on Tuesday. The China trip is scheduled to last four days.



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ZDNET’s key takeaways

  • Trusted quality data is the backbone of agentic AI.
  • Identifying high-impact workflows to assign to AI agents is key to scaling adoption.
  • Scaling agentic AI starts with rethinking how work gets done. 

Gartner forecasts that worldwide AI spending will total $2.5 trillion in 2026, a 44% year-over-year increase. Spending on AI platforms for data science and machine learning will reach $31 billion, and spending on AI data will reach $3 billion.

The global agentic AI market will reach $8.5 billion by the end of 2026 and nearly $40 billion by 2030, per Deloitte Digital. Organizations are rapidly accelerating their adoption of AI agents, with the current average utilization standing at 12 agents per organization, according to MuleSoft 2026 research. This rate is projected to increase by 67% over the next two years, reaching an average of 20 AI agents. 

Also: How to build better AI agents for your business – without creating trust issues

According to IDC, by 2026, 40% of all Global 2000 job roles will involve working with AI agents, redefining long-held traditional entry, mid, and senior level positions. But the journey will not be smooth. By 2027, companies that do not prioritize high-quality, AI-ready data will struggle to scale generative AI and agentic solutions, resulting in a 15% loss in productivity. While 2025 was the year of pilot experiments and small production deployments of agentic AI, 2026 is shaping up to be the year of scaling agentic AI. And to scale agentic AI, according to IDC’s forecast, companies will need trustworthy, accessible, and quality data. 

Scaling agentic AI adoption in business requires a strong data foundation, according to McKinsey research. Businesses can create high-impact workflows by using agents, but to do so, they must modernize their data architecture, improve data quality, and advance their operating models. 

McKinsey found that nearly two-thirds of enterprises worldwide have experimented with agents, but fewer than 10% have scaled them to deliver measurable value. The biggest obstacle to scaling agent adoption is poor data — eight in ten companies cite data limitations as a roadblock to scaling agentic AI. 

Also: AI agents are fast, loose, and out of control, MIT study finds

McKinsey identified the top data limitations as primary constraints that companies face when scaling AI, including: operating model and talent constraints, data limitations, ineffective change management, and tech platform limitations. 

Data is the backbone of agentic AI

Research shows that agentic AI needs a steady flow of high-quality, trusted data to accurately automate complex business workflows. Successful agentic AI also depends on a data architecture that can support autonomy — executing tasks without human intervention. 

Two agentic usage models are emerging: single-agent workflows (one agent using multiple tools) and multi-agent workflows (specialized agents collaborate). In each case, agents will rely on access to high-quality data. Data silos and fragmented data would lead to errors and poor agentic decision-making. 

Four steps for preparing your data 

McKinsey identified four coordinated steps that connect strategy, technology, and people in order to build strong foundational data capabilities. 

Also: Prolonged AI use can be hazardous to your health and work: 4 ways to stay safe

  1. Identify high-impact workflows to ‘agentify’. Focus on highly deterministic, repetitive tasks that deliver value as strong candidates for AI agents. 

  2. Modernize each layer of the data architecture for agents. The focus on modernization should support interoperability, easy access, and governance across systems. The vast majority of business applications do not share data across platforms. According to MuleSoft research, organizations are rapidly adopting autonomous systems. The average enterprise now manages 957 applications — rising to 1,057 for those furthest along in their agentic AI journey. Only 27% of these applications are currently connected, creating a significant challenge for IT leaders aiming to meet their near-term AI implementation goals. 

  3. Ensure that data quality is in place. Businesses must ensure that both structured and unstructured data, as well as agent-generated data, meet consistent standards for accuracy, lineage, and governance. Access to trusted data is a key obstacle. IT teams now spend an average of 36% of their time designing, building, and testing new custom integrations between systems and data. Custom work will not help scale AI adoption. The most significant obstacle to successful AI or AI agent deployment is data quality, cited as the top concern by 25% of organizations. Furthermore, almost all organizations (96%) struggle to use data from across the business for AI initiatives.  

  4. Build an operating and governance model for agentic AI. This is about rethinking how work gets done. Human roles will shift from execution to supervision and orchestration of agent-led workflows. In a hybrid work environment, governance will dictate how agents can operate autonomously in a trustworthy, transparent, and scaled manner. 

The work assigned to AI agents 

McKinsey highlighted the importance of identifying a few critical workflows that would be candidates for AI agents to own. To begin, an end-to-end workflow mapping would help identify opportunities for agentic use. McKinsey found that AI adoption is led by customer service, marketing, knowledge management, and IT. It is important to identify clear metrics that validate impact. Teams should identify the data that can be reused across tasks and workflows.

Also: These companies are actually upskilling their workers for AI – here’s how they do it

McKinsey concludes that having access to high-quality data is a strategic differentiator in the agentic AI era. Because agents will generate enormous amounts of data, data quality, lineage, and standardization will be even more important in the agentic enterprise. And as agentic systems scale, governance becomes the primary level for control. The data foundation will be the competitive advantage in the agentic era. 





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