———- Forwarded message ———
From: ali green <aligreenphd@gmail.com>
Date: Mon, Dec 29, 2025 at 7:31 AM
Subject: HigherEd AI Daily: Dec 16 – Nvidia Nemotron 3 Models, Adobe AI Revenue Growth, and Runway World Model
To: <askme@askthephd.com>, <higheredai@askthephd.com>, Ali Green <uwfhoteldoc@gmail.com>
From: ali green <aligreenphd@gmail.com>
Date: Mon, Dec 29, 2025 at 7:31 AM
Subject: HigherEd AI Daily: Dec 16 – Nvidia Nemotron 3 Models, Adobe AI Revenue Growth, and Runway World Model
To: <askme@askthephd.com>, <higheredai@askthephd.com>, Ali Green <uwfhoteldoc@gmail.com>
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DAILY AI BRIEFING FOR EDUCATORS
HigherEd AI
Tuesday, December 16, 2025
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Today's Focus
Nvidia Releases Open-Source Nemotron 3 Model Family
Nvidia released Nemotron 3 this week, an open-source family of AI models ranging from 30 billion to 500 billion parameters. The Nano version launched immediately with Super and Ultra coming in early 2026. Notably, Nvidia is publishing training data and releasing customization libraries, signaling a strategic shift in how it competes with OpenAI, Google, and Anthropic.
What's interesting here is Nvidia's positioning. As major AI labs develop their own chips instead of relying on Nvidia hardware, Nvidia is pivoting toward providing software and models. By releasing open-source alternatives with published training data, Nvidia maintains relevance in the AI stack even if companies move away from its GPUs.
For universities, this matters because open models with published training data become more auditable and customizable. Your institution could theoretically run Nemotron 3 on your own infrastructure, fine-tune it for specific use cases, and understand exactly how it was trained. This is fundamentally different from closed APIs where you send data to third parties.
Worth considering:
Open models with published training data mean universities can audit AI systems for bias, understand training decisions, and customize models for specific disciplines. Should this be a requirement for institutional AI adoption?
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Platform News
Adobe Reports Record Revenue with AI Integration Driving Growth
Adobe reported record annual revenue of $23.77 billion in 2025, an 11% year-over-year increase. The company attributes most of this growth to integration of generative AI across its product suite. Firefly, Generative Fill, and AI-assisted workflows are now core to Adobe's value proposition.
For creative disciplines in higher education, this signals confidence in AI tools. Adobe is investing heavily because the market is buying. Students learning design, video, photography, and visual communication are increasingly expected to know AI-assisted workflows. If you're teaching these subjects, incorporating Adobe's AI tools into curriculum is no longer optional.
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Research Update
Runway World Model Accelerates Simulation-Based Learning
Runway released its World Model this week, enabling AI to generate playable 3D environments from text descriptions. Early reports show studios achieving 4x faster development speeds using this technology. This means interactive scenarios, educational simulations, and training environments can be created dramatically faster.
For STEM education, business simulations, and professional training, this is significant. Imagine creating complex scenarios for students to navigate—surgical simulations, business crisis management, engineering design challenges—without months of development. Instructors could generate new scenarios on demand based on current events or specific learning objectives.
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A Final Reflection
When students can generate interactive 3D environments and open-source models are available on your servers, what becomes the limiting factor in AI adoption at your institution?
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This newsletter synthesizes developments from TLDR AI, TLDR Design, and primary source documentation. Each edition is curated specifically for higher education professionals.
Visit AskThePhD.com for more resources, daily tool tests, and tutorials for educators.
Dr. Ali Green
Professor & AI in Education Specialist
From the AskThePhD team at HigherEdAI
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