Today, we’re bringing you the latest in AI-powered marketing and business strategies. Here’s what’s inside:

🚨 AI Top Story: ‘Big Tech isn’t slowing down on AI spending—despite rising costs and new challengers like DeepSeek

📊 Top Free AI Resources: Discover Operator use cases that are ACTUALLY useful

🎯 AI Case Study Of The Week: More sales, less waste—Volkswagen’s AI ad formula

🌟 Creator Spotlight: Not sure which paid media channel is right for you? Marc Jordan Waldeck breaks down the pros and cons to help you decide

Who’s Really Winning the AI Race?

DeepSeek Cut AI Costs—So Why Is Big Tech Still Spending?

AI spending isn’t slowing down—it’s accelerating. Despite rising costs and growing competition, Microsoft and Meta are investing billions into AI, betting that the companies with the biggest infrastructure will dominate the future of the industry.

Microsoft’s AI-driven cloud services are now a $13 billion business, reinforcing AI’s growing role in enterprise infrastructure. Meanwhile, Meta is seeing AI transform its ad business. Its AI-powered Advantage+ ad tools are fuelling stronger-than-expected earnings, showing that AI isn’t just streamlining operations—it’s directly driving revenue by making ad targeting more efficient and effective.

What stands out most isn’t just how much they’re making—it’s how much they’re spending. AI at scale is expensive, and companies are betting that massive infrastructure investments now will give them the edge later.

Meta’s Chief AI Scientist, Yann LeCun, dismissed fears that DeepSeek’s low-cost AI model could shake up the market, arguing that as AI systems take on more complex, real-world tasks, their computing demands—and costs—will inevitably go up.

For businesses, the challenge isn’t just about adopting AI—it’s about making informed choices on which AI tools align best with their needs. While large-scale AI models push the boundaries of capability, companies must also consider efficiency, sustainability, and reliability in their decision-making.

The long-term impact of AI won’t just be shaped by Big Tech’s investments, but by which technologies businesses collectively choose to integrate into their workflows.

As new AI challengers emerge and Big Tech continues to invest at scale, the landscape is shifting fast. Is DeepSeek just the beginning of a wave of disruptors? And in a race where cost, speed, and dominance all compete—does anyone truly win?

⚠️ DeepSeek hit with ‘large scale’ cyber-attack after AI chatbot tops app stores - What happened, and what does it mean for AI security?

🚀 Postman Unveils An API-First AI Agent Builder - Here’s how it could change the way businesses build and automate APIs

🤖 Is your marketing team AI-ready? 8 steps to strategic AI adoption - Build a strategy that makes AI work for you

Trapica

If you're running paid ads, you know how tricky it can be to get the right message in front of the right people. Trapica takes the guesswork out of ad targeting and optimisation by using AI to refine audience selection, automate bidding, and adjust budgets across multiple platforms—including Meta’s Advantage+ tools. Whether you’re scaling campaigns or just trying to get better ROI, Trapica helps make your ad spend more efficient.

Key AI Features

AI-Powered Targeting – Automatically finds and refines your ideal audience to improve campaign performance.

Smart Ad Bidding – Uses AI to adjust bidding strategies in real time for better results.

Cross-Platform Optimisation – Tweaks budgets and creatives across Meta, Google, and other ad channels.

Marketing Intelligence – Provides insights into audience behaviour and campaign trends.

Take it for a spin! Click here to Book a Demo on their website.

MARC JORDAN WALDECK - Discover what paid media channel is best suited to your needs!

Discover Operator use cases that are ACTUALLY useful

Volkswagen’s AI-Driven Ad Buying Strategy

The Overview

Volkswagen redefined its advertising strategy by using AI-powered predictive analytics to optimise media spend and improve ad targeting. Instead of relying on traditional ad-buying methods, they turned to data-driven insights to forecast the impact of their campaigns and allocate budgets more effectively.

The result was less wasted ad spend, more strategic placements, and a measurable increase in dealership sales—all without increasing overall marketing costs.

Key AI Components

Predictive Analytics for Smarter Ad Buying
Volkswagen used AI to analyse fuel prices, competitor promotions, and past sales data to:

• Make data-driven media buying decisions rather than relying on assumptions.

• Forecast which ad placements would drive the most sales.

AI-Powered Budget Optimisation
The AI system dynamically allocated advertising budgets to the most effective channels, resulting in:

• Reduced unnecessary ad expenditures.

• Enhanced cost efficiency through automated bidding strategies.

Impact Forecasting with Machine Learning

By leveraging machine learning, Volkswagen predicted which advertisements would drive the highest conversions by analysing:

• Customer behaviour patterns.

• Past campaign performances.

Replicate It

To implement a similar AI-driven advertising strategy:

Select AI-Powered Ad Optimisation Tools
Consider platforms like Albert.ai or Trapica to automate media buying and optimise ad placements.

Leverage Machine Learning Models
Utilise frameworks such as TensorFlow or Scikit-Learn to analyse customer data and forecast ad performance.

Implement Automated Budget Reallocation
Employ AI-driven platforms like Smartly.io to dynamically adjust advertising budgets across various channels for optimal performance.

CHINA’S AURA IS THROUGH THE ROOF RIGHT NOW 🇨🇳