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Navigating AI: AI Media Buying, AI Job Disruption and Putting AGI to the Test

March 31, 2025

In this edition of the newsletter, we explore the growing role of AI in replacing human effort. While many see AI as a powerful tool to boost productivity, others view it as a threat to their livelihoods. AI has already begun to take over a range of tasks traditionally handled by junior marketers, product managers, developers, and professionals across various industries. However, its ability to take on more complex responsibilities remains a subject of intense debate.

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From Weeks to Hours: How AI is Revolutionizing Digital Marketing

Jellyfish, a Brandtech Group agency, is revolutionizing digital advertising with AI-powered media buying. Adweek reports that by automating campaign setup and optimization across platforms like Google, Meta, and Amazon, their AI agents have dramatically reduced campaign launch times and operational costs. While AI handles routine tasks, human oversight remains essential for strategic decisions, signaling a shift in the skills needed in modern marketing teams. This transformation highlights the growing role of AI in streamlining ad operations while still requiring human expertise for high-level strategy.

  • AI Agents Revolutionize Media Buying
    Jellyfish's AI agents automate campaign setup and optimization across Google, Meta, and Amazon, slashing launch times by 65%. These AI-driven tools replicate the decision-making of junior media buyers, handling budget adjustments and reporting dashboards in real time. This automation significantly reduces the need for large teams, cutting costs and improving efficiency.

  • Faster, Smarter Ad Campaigns
    AI agents have transformed performance marketing by completing weeks-long tasks in hours. Brands can now pivot and refine strategies instantly, with real-time updates every 15 minutes to prevent errors. Jellyfish’s AI investment has boosted campaign performance by 30% while reducing infrastructure costs by 22%.

  • The Rise of Digital Employees in Marketing
    Jellyfish has integrated AI agents as "digital employees," replacing large teams of junior marketers. These agents learn campaign setup and optimization tasks through APIs and automated frameworks. Companies can now manage massive ad spend with minimal human oversight, optimizing workflows and reducing operational complexity.

  • Balancing AI Automation with Human Oversight
    Each AI agent operates within brand-specific KPIs, autonomously adjusting budgets and optimizing ads. However, major strategic decisions, such as shifting platform investments or testing new audiences, still require human approval. The rise of AI-driven marketing is also creating new roles, like multi-modal strategists, who bridge AI insights with creative and media strategies.

AI Trailblazer Takeaways: Jellyfish is ahead of the curve. There is little doubt that many marketing tasks will be affected by AI agents enabling marketing orgs to move fast and increase productivity while giving rise to new marketing roles.

AI Job Disruption: A Slow Burn or Sudden Inferno?

AI is rapidly transforming the workforce, but the full impact on jobs remains uncertain. VentureBeat argues that while automation and AI-driven decision-making are becoming more common, widespread job displacement has yet to occur. However, historical patterns suggest that technological shifts often happen gradually—until they suddenly accelerate. With growing AI adoption and economic pressures on the horizon, 2025 could be the tipping point when AI stops merely assisting workers and starts replacing them at scale.

  • AI Job Displacement: A Slow Build-Up or Sudden Shock?
    While AI is transforming workplaces, large-scale job displacement hasn’t yet materialized. Predictions suggest this may change soon, with 40% of employers expecting workforce reductions between 2025 and 2030. Historical trends indicate technological change often occurs gradually before accelerating suddenly.

  • The Rise of AI Adoption: Businesses Embrace Automation
    AI adoption is growing rapidly, with 78% of companies now using AI in at least one business function. Executives increasingly trust AI for decision-making, with 44% deferring to AI over their own insights. However, full enterprise integration remains in its early stages, leaving room for a potential shift in workforce dynamics.

  • Software Developers: The First Domino to Fall?
    AI-driven coding tools are advancing at an unprecedented rate, with some experts predicting AI will generate nearly all code within a year. Startups are already relying on AI for 95% of their development work, reducing the need for human programmers. As AI models like GPT-4.5 continue improving, software engineering may be one of the first white-collar professions to experience large-scale automation.

  • Will an Economic Downturn Force AI-Driven Job Cuts?
    Economic recessions historically accelerate technological adoption, and the next downturn could push companies to automate rapidly. If a recession hits in 2025 or 2026, businesses may turn to AI to cut costs and boost efficiency. This shift could mark the tipping point where AI moves from augmenting jobs to replacing them on a large scale.

AI Trailblazer Takeaways: The question of everyone’s mind is how much job displacement will be caused by AI adoption. The answer varies with estimates varying from 85 million to 300 million jobs globally. The truth is no one knows, but everyone should prepare for it by learning and adopting AI tech used in their respective fields.

AI Models Stumble on ‘Easy’ Test—Humans Win!

New Scientist reports that a new benchmark, ARC-AGI-2, is challenging the world’s most advanced AI models in unexpected ways, revealing their struggles with tasks that humans solve with ease. Unlike previous tests that focused solely on intelligence, this one also measures efficiency, raising concerns about the rising computational costs of AI. While some see these benchmarks as meaningful progress toward artificial general intelligence (AGI), others argue they simply test AI’s ability to follow prompts rather than exhibit true intelligence. As AI companies race to improve performance, the debate over what defines AGI—and whether we’re truly approaching it—continues.

  • AI Models Struggle with New AGI Test
    A new benchmark, ARC-AGI-2, reveals that even the most advanced AI models fail at tasks that humans solve easily. Unlike previous tests, this one measures adaptability and efficiency rather than just raw intelligence. No AI system scored higher than single digits out of 100, despite every question being solved by at least two humans in under two attempts.

  • Beyond Computing Power: The Cost Factor in AI Testing
    The ARC-AGI-2 test introduces efficiency as a crucial metric, factoring in the cost of running AI models. OpenAI’s o3-low model, for example, costs $200 per task, while human testers were paid only $17. This shift highlights concerns about AI models becoming too energy-intensive and unsustainable in their pursuit of better performance.

  • AI’s Weak Spot: Simple Tasks Require Complex Thinking
    Unlike deep-learning-based benchmarks, ARC-AGI-2 focuses on symbolic interpretation and simple pattern recognition. While AI models excel in complex tasks like graduate-level reasoning, they struggle with seemingly basic problems requiring adaptability. OpenAI’s o3-low model scored 75.7% on the previous test but only 4% on this new challenge.

  • Is AGI Progress Real or Just a Benchmark Chase?
    Some experts argue that passing these tests doesn’t indicate real artificial general intelligence. Instead, they claim AI is just responding well to specific prompts, not demonstrating broad cognitive abilities. As AI benchmarks evolve, the debate over true AGI remains far from settled.

AI Trailblazer Takeaways: We all know that AI LLMs are in their infancy and will get better over time. Imagine how much improved the scores will be a year from now or two years from now.

Quote of the Week

“This is our space race.”  

- Andrew Bosworth, CTO of Meta

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