CMOs today need to master tech, AI, storytelling, and strategy—all while dodging blame when things go south.

But hey, at least we still get to make TikToks.

Follow us here for more marketing insights (and occasional existential crises). 🚀

5️⃣ Stakeholder Management
CMOs don’t just market—they sell marketing to the board, CFO, and CEO. Bonus points if you can explain attribution without their eyes glazing over.

4️⃣ Demand Gen Has Evolved
Lead gen is out. Full-funnel engagement is in. CMOs need to know who their buyers are, how they engage, and what drives revenue.

3️⃣ Brand vs. Revenue
Customers want a brand they trust. CEOs want marketing to drive measurable growth. CMOs must balance both.

2️⃣ AI is Changing Everything
New AI tools emerge daily (Operator, DeepSeek, and more). CMOs must adapt fast to stay ahead.

1️⃣ Tech Overload
Marketing now has 11,000+ tools to choose from. CMOs must build a seamless tech stack, align with sales, and prove ROI on every investment.

Final thought: This feels like one of those moments we'll look back on as an inflection point. AI is about to become a lot more accessible, and a lot less expensive. The question isn't if this will disrupt the current players, but how fast.
What do you think?
Let’s discuss 👇
#IA #News #nvidia

The AI world is about to become far more democratized. Nvidia may bounce back, but this moment is a clear signal:
In AI, as in life, sometimes it’s not about how much power you have—it’s about how efficiently you use it.
DeepSeek just proved that with $6 million.

For AI startups and small developers, this is a golden era.
AI is becoming cheaper and more accessible.
You no longer need billions or partnerships with hyperscalers like Microsoft to compete.
For Big Tech? This changes everything.

Nvidia’s response? A 17% drop in stock value—a $500 billion loss.
Their business thrives on selling high-margin GPUs, but DeepSeek’s approach makes those GPUs less essential. Now, you could train AI with a $3,000 gaming rig.
This could destabilize their entire business model.

DeepSeek’s innovations slashed the cost of training AI models by 95%.
Here’s the scary part for Nvidia:
GPUs required for AI: From 100,000 to 2,000 (-98%).
Training cost: From $100M to $5M.
Oh, and DeepSeek made it open-source. 💥

2: Multi-token processing
Why process word-by-word? DeepSeek processed whole phrases at once, doubling speed while staying accurate.
3: Specialized modules
Instead of one giant AI, they used smaller, focused systems. More efficient, less power-hungry.

How did they pull this off? By being smart, not extravagant:
1: 8-bit quantization
Instead of super-precise 32-bit calculations, they trained with 8 bits. Less precision, but good enough—and saved 75% memory.

Enter DeepSeek, a small Chinese AI startup that did the unthinkable.
While Nvidia’s GPUs dominated AI research with astronomical costs (training GPT-4 cost ~$100 million!), DeepSeek flipped the script:
Total cost? $6 million.
Time? 2 months.
And their model? Almost as good as GPT-4.

At CES, Nvidia hit a record high valuation of $3.66 trillion—making them the second most valuable company on Earth, right behind Apple.
Investors couldn’t get enough. Nvidia wasn’t just selling chips; they were selling the future of AI.
But just one week later… chaos.

January 6, 2025, CES in Las Vegas. Nvidia’s CEO, Jensen Huang, delivered a jaw-dropping presentation.
Their vision? Revolutionize robotics, gaming, and AI with groundbreaking chips, “Cosmos” generative models, and new GPUs ranging from $549 to $1,999.
It was supposed to be their year.