This post is part of my Weekly AI Signals series — a curated look at the moments that matter once the noise fades.

Introduction

Five Signals That Mattered

Hello, dear reader! Welcome to the last week of December 2025. I hope you are enjoying the holidays and have had a moment to look back on what has been an extraordinary year for AI.

This is not a complete account of everything that happened in AI this week. Instead, it is a small, curated set of signals that felt meaningful once the noise settled — moments where limits became visible, incentives shifted, or assumptions quietly changed.

If 2025 was the year we kept asking “what can we build?”, this past week felt like the moment the industry started asking a more useful question: “what actually works?”

The five signals below come from very different places — creative industries, hardware, developer practice, security, and physical systems — but together they point to the same thing. AI is moving out of its novelty phase and into an engineering one.

Here is what stood out, and why it may matter longer than this week’s headlines.

1. Hollywood Discovers That Creativity Cannot Be Automated (Yet)

Hollywood's AI Experiment in 2025: Hype, Scandals, and a Flood of Low-Quality Content

As 2025 draws to a close, retrospectives on the entertainment industry’s use of generative AI reveal a consistent problem: scale without quality.

Despite massive investments — including Disney’s widely reported partnership with OpenAI — studios struggled to deliver compelling results. This month’s most visible failure was Amazon’s AI-dubbed anime releases, which were quietly removed after audiences criticised their robotic delivery and lack of emotional nuance.

What’s revealing is not that AI struggled, but where it struggled. Generative systems can produce video quickly and cheaply, yet they still fail to capture intent: cultural context, emotional timing, and deliberate storytelling choices.

In consumer-facing AI, novelty fades fast. Speed alone does not create value. Quality, taste, and human judgment remain the differentiators.

Interestingly, while creativity hit its limits, something very different was happening lower in the stack.

2. Nvidia and Groq: The $20 Billion Hardware Handshake

Nvidia to license AI chip challenger Groq’s tech and hire its CEO

On December 24th, Nvidia announced a strategic licensing deal with Groq, reportedly valued at $20 billion, marking one of the most significant AI hardware collaborations of the year.

Groq has focused on ultra-fast inference through its Language Processing Unit (LPU), while Nvidia continues to dominate large-scale model training. Rather than competing across the entire pipeline, this deal acknowledges a reality developers already feel: training and inference have different optimisation needs.

Groq’s leadership and engineers will support Nvidia’s efforts to scale low-latency inference, while Groq remains independent — a rare example of cooperation in a fiercely competitive space.

This is a strong signal that real-time AI applications will become cheaper and more accessible in 2026. Faster inference unlocks practical use cases that previously felt out of reach.

But faster models alone are not enough. We also need to communicate with them better.

3. Context Engineering: The New Frontier in AI Coding

An Empirical Study of Developer-Provided Context for AI Coding Assistants in Open-Source Projects

A research paper published on December 21st analysed 401 open-source repositories and surfaced a pattern many developers will recognise: AI coding tools perform best when given explicit structural context.

Rather than endlessly refining prompts, teams are adding context files that explain architecture, style, and constraints. The insight is simple but powerful:

An AI coding assistant is only as good as the context it can read.

A practical example is adding a CONTEXT.md file at the root of your repository:

# CONTEXT.md
- Language: Python 3.12
- Style: small pure functions, no globals
- Architecture: service layer + repository pattern
- Tests: pytest, no mocks unless unavoidable

Something to try: Add this file today. It improves AI output and makes expectations clearer for human collaborators.

4. OpenAI’s Admission: Prompt Injection Is a Long-Term Risk

OpenAI admits prompt injection is here to stay as enterprises lag on defenses

On December 22nd, OpenAI publicly acknowledged that prompt injection attacks are unlikely to ever be fully eliminated.

This framing is important. It treats AI security the same way we treat web security issues like SQL injection: not as a bug to fix once, but as an ongoing risk to manage.

Rule of thumb: Never let an LLM be the final authority on decisions that matter.

Application-level validation, monitoring, and layered defences remain essential. Models can assist — but they cannot be your security boundary.

5. Waymo and Conversational AI Beyond the Screen

Waymo is testing Gemini as an in-car AI assistant in its robotaxis

This week also confirmed that Waymo is testing Google’s Gemini model as an in-car conversational assistant.

The AI does not drive. Instead, it acts as a passenger-facing interface — answering route questions, adjusting the environment, or explaining vehicle behaviour.

AI is moving off screens and into physical spaces. In 2026, the challenge will be context awareness — understanding not just language, but environment, timing, and human expectations.

Closing Thoughts

This week felt like a quiet turning point. The “magic” phase of AI — where novelty carried everything — is fading. The engineering phase is taking its place.

That is good news.

It means fewer demos and more systems. Fewer promises and more constraints. And ultimately, more reliable tools that earn trust through behaviour rather than spectacle.

As we head into 2026, I’d love to know: which part of AI feels most “real” in your work right now — the models, the tooling, or the constraints?

I hope you have a wonderful weekend, and happy building!