👋 Good morning! The future of innovation is getting hands, or rather, robotic arms. From Lila Sciences’ AI-driven “super-labs” that run experiments around the clock, to cybersecurity fortresses built to defend AI itself, this week’s edition dives into how automation is scaling discovery, defense, and creativity all at once. You’ll also pick up a science-backed prompt-writing method to sharpen your AI results instantly. Ready to see where intelligence meets ingenuity? Let’s dig in.

Rethinking how science is done with “AI super-labs”

Lila Sciences, founded in 2023 and recently backed by investors including Nvidia’s venture arm, has secured a $115 million extension on its Series A funding, pushing its valuation past $1.3 billion.

The company’s bold vision: build “AI Science Factories”, robotic laboratories controlled by specialized AI models that conduct continuous experiments across chemistry, materials science and life-sciences domains.

Rather than training generic large-language models on internet data, Lila emphasises generating proprietary scientific data via automated experiments, positioning itself as a platform that other companies can tap into for innovation in energy, semiconductors and drug-development.

With its latest capital, the startup is leasing a 235,500-sq-ft facility in Cambridge (MA), signaling its intentions to scale dramatically.

In essence: Lila Sciences shows that the next frontier for AI may not be just smarter text generators, but autonomous laboratories that push the boundaries of discovery — turning science into a high-throughput, data-driven enterprise.

⚙️ In Focus: Palo Alto Networks Prisma AIRS 2.0 & Xage-NVIDIA Security Integration  

Cybersecurity’s AI revolution isn’t just about smarter detections; it’s evolving into a fortress built for the AI era. Palo Alto Networks’ Prisma AIRS 2.0 offers a comprehensive, end-to-end shield, inspecting AI models deeply and defending AI agents in real time. This level of protection is critical as enterprises race to deploy AI while facing threats like indirect prompt injection attacks that can sneak secret commands into AI systems.  

Adding muscle to the security stack, Xage Security and NVIDIA have teamed up to deliver lightning-fast, zero-trust security controls tailored for AI-driven critical infrastructure. Their integrated platform offers granular, identity-based access controls with rock-solid resilience, a must for environments where a security breach could cause cascading failures.  

Why it matters: As AI becomes the backbone of business operations and industrial control, defending these AI layers is no longer optional but strategic. Enterprises embracing AI will need layered, autonomous defenses and orchestration between security teams and AI tools to prevent tomorrow’s sophisticated cyber intrusions today.  

🧜AI tips and tricks: How to Write Better Prompts: The “Role → Task → Specifics → Context → Examples → Notes” Method

Most people throw random instructions at ChatGPT and hope for magic. But if you want reliable, high-quality outputs, there’s a structure that actually works, and it’s backed by research.

Role prompting means assigning ChatGPT a clear identity.
When the model knows who it is supposed to be, its accuracy and creativity skyrocket.

Example:

“You are a highly skilled and creative short-form content script writer who crafts engaging, informative, and concise videos.”

Research:

✅ Takeaway: Choose a role that gives an advantage for the task (e.g., “math teacher” for math problems) and enrich it with strong traits.

This is what you actually want done — written as a clear, action-oriented instruction.

Always start with a verb (generate, write, analyze, summarize).

Example:

Generate engaging and casual outreach messages for users promoting their services in the dental industry. Focus on how AI can help them scale their business.

This section is your “cheat sheet” for execution details, written as bullet points.

Example Specifics:

👉 Keep this list short and practical. “Less is more.”

Context tells the model why it’s doing the task — and it makes a huge difference.

It helps the model act with more purpose, empathy, and relevance.

Example:

Our company provides AI-powered solutions to businesses. You’re classifying incoming client emails so our sales team can respond faster. Your work directly impacts company growth and customer satisfaction.

Add context about*:*

This is Few-Shot Prompting — showing the model a few examples before asking it to perform the task.

Why it works:
Adding just 3–5 examples can drastically improve results .
Accuracy scales with more examples (up to ~32), but most gains come early.

Step 6: Notes

This is your final checklist — format rules, tone reminders, and “don’t do this” notes.

Example Notes:

Want access to the full template?

Download here👇

💡Quick Hits & Numbers  

- 63% of cybersecurity pros report AI-driven cyberattacks hitting their organizations in 2025, a surge demanding multi-layered AI defenses.  

- AI models like Emu3.5 and Lumina-DiMOO push multimodal reasoning, supporting complex, cross-modal content synthesis and problem-solving.   

- 80% of enterprises are using generativ Ai in some way.  

🧩Closing Thought  

AI is no longer just a tool in the toolbox, it’s becoming the architect drafting the blueprint. Whether it’s generating epic video stories, safeguarding digital realms, orchestrating autonomous workflows, or sparking emotional bonds, AI is reshaping how business and society create, defend, and relate. The trick will be designing systems that serve human goals without becoming inscrutable black boxes or emotional hazards.  

Because in the end, AI isn’t replacing humans; it’s replacing human excuses.