Introduction
In a recent LinkedIn post, Pat Gelsinger — electrical engineering expert and former Intel CEO — congratulated NVIDIA and its CEO Jensen Huang on another impressive earnings cycle.
But more interestingly, Gelsinger drew attention to four surprising and positive developments from NVIDIA’s recent quarter that have far-reaching implications for the future of AI and enterprise innovation.
Let’s unpack these insights and what they mean for the industry, investors, and society at large.
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The Four Emerging AI Trends Highlighted
According to Pat Gelsinger, these were the four positive surprises that emerged from NVIDIA latest quarter:
1. Growing Demand for Inference Reasoning AI
2. The AI Diffusion Rule Being Rescinded
3. Enterprise AI and More Successful Demonstrations of Agentic AI
4. The Rise of Industrial AI (Every Factory Becoming an AI Factory)
Inference AI: The New Workhorse of Intelligence
Why it matters:
While AI training grabs headlines, inference—the process where AI applies its learning to make decisions—is where real value happens. From voice assistants to predictive maintenance, inference runs the world.
Gelsinger’s take:
He believes efficient inference is the key to practical deployment. It allows AI to respond in real-time, power enterprise tools, and drive automation at the edge (in phones, cars, and factories). This shift is also central to his firm Playground Global’s investment strategy.
Data Point:
According to Gartner, inference workloads will represent 60% of AI processing by 2026, up from 40% in 2023. Efficient inference reduces cloud costs, latency, and energy use—key for scaling AI globally.
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Rescinding the “AI Diffusion Rule”: A Regulatory Tailwind
Why it matters:
Gelsinger hinted that certain AI restrictions—what he calls the “diffusion rule”—are being lifted. This could refer to relaxed export rules, data-sharing policies, or company-level AI governance changes.
Impact:
The rollback of such barriers allows faster and broader AI adoption, especially in sensitive sectors like healthcare, defense, and industrial automation. It signals that policymakers and companies alike are ready to embrace AI deployment, not just experimentation.
Example:
In May 2025, the U.S. Department of Commerce eased AI model sharing guidelines, allowing more open-source and commercial AI tools to flow between research labs and startups.
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Enterprise Agents: The Rise of AI Co-Workers
Why it matters:
Agentic AI systems—tools that can complete tasks, make decisions, and interact autonomously—are transforming how businesses operate. These systems now go beyond chatbots to manage entire workflows.
Gelsinger’s view:
He sees enterprise AI in 2025 like the internet in 1995—about to explode in usage. Models like GPT-4, Claude, and open-source alternatives are being embedded directly into enterprise platforms, driving everything from contract analysis to logistics.
Real-world use case:
Companies like SAP and Salesforce now offer AI copilots to assist with customer queries, product suggestions, and even business forecasting. AI agents are already saving enterprises up to 30% in operational costs, according to a McKinsey 2024 study
techovedas.com/openai-launches-swarm-a-new-framework-for-multi-agent-ai-systems
AI Factories: Industrial AI Goes Mainstream
Why it matters:
Gelsinger envisions a world where every factory becomes an AI factory. This includes digital twins, predictive analytics, and AI-managed supply chains.
Impact:
This shift boosts productivity and reduces downtime. It also aligns with national efforts in the U.S., Taiwan, and India to build AI-native fabs and strengthen manufacturing independence.
Example:
Foxconn’s AI-integrated production lines in India saw a 22% improvement in yield after implementing NVIDIA-powered systems in early 2025.
Why Gelsinger Focuses on Inference and Enterprise AI
Among the four trends, Gelsinger singles out Inference AI and Enterprise AI as the most significant and sustainable.
These, he argues, are not just technological wins — they are strategic doorways to future innovation.
Inference unlocks the practicality of AI across devices and use cases, while Enterprise AI introduces durable business value.
Additionally the two together make AI a long-term foundational layer in the global economy — not just a hype cycle.
techovedas.com/4-big-wins-from-nvidia-ceo-jensen-huangs-homecoming-speech-in-taiwan/
Gloo and the Ethics of AI Alignment
Gelsinger concludes with a nod to his work at Gloo, an organization focused on ensuring AI systems are aligned with human values.
In a world previously shaped by social networks’ “click models,” he warns that we cannot afford to replicate the same profit-maximizing algorithms in next-gen AI systems.
Gloo’s work aims to develop AI systems that go beyond engagement metrics — prioritizing human alignment, societal good, and long-term values.
With new announcements on the horizon, Gelsinger teases that Gloo may soon introduce tools or frameworks to address these ethical challenges in real-world deployments.
Conclusion
What stands out in Pat Gelsinger commentary is not just enthusiasm for Nvidia AI, but a clear-eyed view of which trends matter most and why. The shift from training to inference, such as the rise of enterprise agents, and the embedding of AI into industrial systems signal a maturation of the field.
And yet, with great power comes the responsibility of alignment. Gelsinger’s parting thoughts on Gloo remind us that as AI capabilities grow, so must our commitment to deploying them with purpose and care.
The next wave of AI isn’t just about capability — it’s about where, why, and how we apply it.
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