The Kalshi commercial, broadcast during the NBA Finals, was a landmark in media history: the first nationally aired advertisement created almost entirely using generative AI. This experiment provided a real-world test of AI’s potential in video production, revealing both its immediate benefits and the significant challenges that remain for brands, creators, and the broader industry.

The Benefits of AI in the Kalshi Commercial: Cost, Crew, Time, and Process

  • Dramatic Cost Reduction: The ad was produced for just $2,000—a fraction of the tens or hundreds of thousands typically spent on traditional TV spots. This 95% cost reduction demonstrated how AI can democratize access to high-profile media placements.

  • Minimal Crew Required: Instead of a large production team, a single creator (PJ Accetturo) handled everything, from prompt writing to editing, using tools like Google Veo 3, Gemini, and CapCut. This shift from team-based to solo production is a game-changer for small brands and independent creators.

  • Faster Turnaround: The workflow was compressed from weeks or months to just days. Hundreds of AI-generated video clips were created and curated, with the final 30-second spot assembled in a fraction of the time a traditional shoot would require.

  • Radically Different Process: The process replaced storyboarding, filming, and post-production with AI prompt engineering, rapid clip generation, and digital assembly. Instead of managing locations, actors, and equipment, the creator curated and edited AI outputs.

The Reality: Why This Approach Isn’t Scalable—Yet

Despite these clear advantages, the Kalshi commercial also exposed the limits of today’s AI video workflows:

  • Heavy Manual Curation: The creator had to sift through 300–400 AI-generated clips to find just 15 that were usable, then manually edit them into a coherent sequence. This hands-on process is time-consuming and does not scale for organizations seeking to produce content at volume.

  • LLM Hallucinations and Inconsistencies: AI models often generate hallucinations—content that is off-brand, incoherent, or simply incorrect. Each output requires human review and correction, adding hidden costs and bottlenecks.

  • Consumer Expectations vs. Reality: Many expect AI video to be as instant, automated, and affordable as AI-generated images or text. The Kalshi case shows that even “cheap” AI video requires significant manual effort and expertise, making it more expensive and complex than anticipated.

Teaching Moments from the First Broadcasted AI Commercial

  • AI is Not Yet Push-Button: Even with advanced tools, skilled human intervention is needed for quality control, editing, and prompt engineering.

  • Quality Control is the Bottleneck: The volume of AI-generated material means that selecting, editing, and correcting outputs is a major resource drain.

  • Workflow Automation is Essential: Without automation, the process cannot scale to meet the needs of brands, publishers, or agencies aiming for high-frequency, high-quality video output.

The Prescriptive Solution: Application Layer Platforms Like Aeon

To bridge the gap between AI’s promise and the reality of scalable, cost-effective video production, application layer solutions such as Aeon are essential.

Solve for Scalability and Quality Challenges

  • Automated Workflow from Start to Finish: Aeon activates multiple AI solutions to analyze written content, create storyboards, source assets, generate voiceovers, and produce professional-grade videos with minimal human input.

  • Brand Safety and Editorial Control: The platform allows users to embed brand guidelines and editorial standards directly into the workflow, ensuring every video meets quality and compliance requirements.

  • AI-Powered Error Reduction: Aeon’s intelligent validation and refinement tools catch and correct hallucinations or inconsistencies before they reach the final edit, dramatically reducing the need for manual review and post-editing.

  • Scalable Content Creation: With features like text-to-video conversion, automated voiceovers, captioning, and asset management, Aeon enables teams to produce hundreds of videos at scale—without a proportional increase in cost or labor.

  • Optimized for Every Platform: Aeon automatically formats videos for social media, e-commerce, and editorial use, ensuring maximum reach and engagement with minimal effort.

  • Data-Driven Insights: The Manifest feature details time saved, assets used, and provides actionable insights to further refine and optimize production workflows.

Key Benefits of Using Applications on top of LLMs

  • Reduces production costs below current AI benchmarks by automating curation, editing, and quality control

  • Ensures brand consistency and compliance across all video content

  • Enables true scalability, turning a labor-intensive process into a streamlined, repeatable workflow

  • Empowers publishers and marketers to focus on creativity and strategy, not technical hurdles

Conclusion: The Future of AI Video Production

The Kalshi commercial was a landmark, proving that AI can revolutionize video production. But it also highlighted the current limits—manual curation, error correction, and a gap between expectations and reality. The lesson is clear: to unlock the full potential of AI video, the industry must embrace application layer platforms like Aeon. 

These tools automate the hardest parts of the workflow, reduce errors, and make high-quality, scalable video production accessible to everyone. That’s how AI video will truly scale—and how brands and creators can finally deliver on the promise of the AI-powered media revolution