ChatGPT Ads, Thinking Machines Drama, and STEM: Shaking Up AI Video Marketing

By VEONIB | 2026-07-16

Quick Answer

OpenAI’s move to test ads in ChatGPT signals a monetisation shift that will reshape how ecommerce brands pay for AI-powered customer interactions, while the Thinking Machines talent war and the STEM scaling paper each hint at faster, cheaper model development for AI video generation.

TL;DR

Table of Contents

According to the LWiAI Podcast #232 - ChatGPT Ads, Thinking Machines Drama, STEM published by Last Week in AI on 2026-01-28 (recorded 2026-01-23), the AI industry is undergoing three shifts with direct implications for ecommerce video marketing: OpenAI’s advertising model, internal startup turmoil, and a breakthrough in transformer scaling. For merchants using AI-generated product videos, these changes influence everything from API pricing to the quality and speed of video outputs. This article unpacks each story through the lens of AI-powered ecommerce video production and offers actionable guidance for Shopify sellers, Amazon vendors, and TikTok Shop operators.

Hero Image
Alt Text: Abstract illustration of ChatGPT ad placement between product AI video creation steps, with a glowing transformer chip in background
Caption: OpenAI's ad trial, startup talent wars, and transformer scaling – three forces reshaping AI video for ecommerce
OG Image Title: ChatGPT Ads Impact on AI Video Marketing
Suggested Visual: A split-screen graphic showing a ChatGPT conversation with a subtle ad card on the left, a Thinking Machines logo on the right, and a “STEM” diagram in the center, all overlaid on a product video storyboard.

OpenAI Ads in ChatGPT: What It Means for Ecommerce AI Video

OpenAI is testing advertisements within ChatGPT, a move fueled by the company’s need to monetise a product that reportedly burns through billions of dollars in operating costs. According to the Ars Technica report cited in the podcast, these ads could appear as sponsored prompts or recommended products within the chat interface. For ecommerce merchants, this development is a double-edged sword.

On one hand, ads inside ChatGPT could create a new channel for product discovery – brands might pay to appear in relevant AI conversations. On the other hand, if OpenAI begins to rely on advertising revenue, it may shift focus away from pure API performance toward ad-driven features. That could affect the reliability and pricing of the GPT-powered tools that underpin many AI video script and storyboard generators, including VEONIB.

Potential Impact on API Costs and Video Tools

OpenAI’s API pricing has already fluctuated. If the advertising model succeeds, the company may subsidise API usage with ad revenue, making it cheaper for developers to integrate AI into video workflows. Conversely, if ads fail to generate sufficient income, OpenAI might raise API fees to cover losses – a scenario that would directly increase the cost of generating product videos.

VEONIB Insight

Why this matters: ChatGPT ads represent a fundamental shift in how AI platforms monetise. For merchants relying on GPT for video script generation, prompt engineering, or A/B copy suggestions, any change in API pricing or feature prioritisation will hit the bottom line.

What it means for AI video generation: If the ad model becomes pervasive, we may see sponsored product placements injected into AI-generated video scripts. That could be a new revenue stream for creators but also a compliance headache for brands that need ad-free product demonstrations.

What it means for ecommerce: Shopify and Amazon sellers who use AI video tools should monitor OpenAI’s ad rollout closely. If the ad tier offers cheaper or even free API access, it could dramatically lower the cost of video production. However, brands must ensure that their product videos remain brand-safe and not appear alongside competitor ads.

Should businesses adopt? Not yet. The test is limited, and the full impact on API pricing is uncertain. Merchants should continue using OpenAI’s standard API for video scripts but prepare for potential pricing changes by evaluating alternative models (Claude, Gemini, open-source LLMs).

Recommended scenarios: Use GPT-powered video script generation now while pricing is stable. If OpenAI introduces a subsidised ad-supported API, consider migrating to that tier for high-volume, non-sensitive content like social media ads.

Practical advice: Diversify your AI model stack. Relying solely on OpenAI is risky; test Claude and Gemini for video script generation to retain flexibility.

The Thinking Machines Drama: Talent Wars and Model Development

The New York Times reported on the internal turmoil at Thinking Machines, a high-profile AI startup that saw several key employees resign and return to OpenAI. The podcast episode described the drama as “riveting Silicon Valley,” highlighting how top AI talent often circles back to established incumbents.

For ecommerce video generation, this matters because model development velocity directly impacts the features available to merchants. When a startup loses its core research team, its roadmap for video generation, fine-tuning, or multimodal understanding may stall. Thinking Machines was reportedly working on advanced reasoning models that could have powered more sophisticated product description generation and video storyboarding. Their departure leaves a gap that may be filled by OpenAI or other labs.

Comparison of Talent Stability and Impact on AI Video Tools

Startup/Company Talent Retention Likely Impact on AI Video Features Recommendation for Merchants
Thinking Machines Low – multiple returns to OpenAI Delays in reasoning-model improvements for video script generation Avoid building exclusive workflows around unproven startup models
OpenAI High – strong brand and resources Steady improvements in GPT vision and reasoning, benefiting video tools Continue using OpenAI for core video generation tasks
Anthropic (Claude) Medium – competitive retention Steady improvement in safety and reasoning, but slower video multi-modal support Use Claude for script refinement and compliance checks
Black Forest Labs Stable (FLUX.2 release) Compact flow models enable real-time, on-device video generation Consider FLUX.2 for low-cost, quick-turnaround product thumbnails

VEONIV Insight

Why this matters: Talent churn directly affects the pace of innovation. When startups lose researchers, new AI video capabilities – like real-time script editing, better character consistency, or lower latency – are delayed.

What it means for AI video generation: Merchants should not anchor their entire video production pipeline to a single startup or model. The Thinking Machines case proves that even well-funded startups can suddenly lose momentum. Diversifying across multiple model providers (OpenAI, Anthropic, Google) is prudent.

What it means for ecommerce: For DTC brands that rely on bespoke AI video tools built on a startup’s API, a model stall could mean missing out on competitor features. Stay flexible by using modular platforms like VEONIB that support multiple model backends.

Should businesses adopt? Not applicable – this is an industry signal, not a product. Use it as a reminder to build platform-agnostic video workflows.

Practical advice: When choosing an AI video generation platform, ask whether it supports model switching. Platforms that lock you into one model create vendor risk.

STEM Research: Faster, Cheaper Transformer Scaling for Video

The podcast discussed a research paper titled STEM: Scaling Transformers with Embedding Modules (arXiv: 2601.10639). While the audio snippet is brief, the core idea is that embedding modules can reduce the computational cost of scaling transformer-based models. For AI video generation, this is a significant development.

Current large-scale video models require enormous GPU clusters and hours of training. If STEM allows the same performance with fewer FLOPs, it could democratise high-quality video model training and inference. For ecommerce, that means faster generation of product videos, lower API costs, and potential for on-device video creation.

How STEM Could Benefit AI Video Generation

Scenario Current Bottleneck STEM Potential Likely Timeline
Product video generation via cloud API High compute cost per 10-second clip 30-50% cost reduction 6-12 months (research to deployment)
Real-time video editing for shoppable live streams Latency too high for instant feedback Near-real-time transformer inference 12-18 months
On-device product video creation (mobile apps) Models too large for phones Compact embedding modules enable phone-based generation 18-24 months

VEONIB Insight

Why this matters: The STEM paper directly targets the largest barrier to AI video adoption: compute cost. For merchants generating thousands of product videos, a reduction in per-video cost can transform the ROI of AI video production.

What it means for AI video generation: If STEM is adopted by major platforms (OpenAI, Google, open-source), we can expect cheaper video APIs and faster turnaround times. This makes AI video generation viable for high-volume, low-margin products (e.g., grocery items, accessories).

What it means for ecommerce: Shopify sellers running dynamic product ads could generate fresh videos daily instead of weekly. Amazon sellers could A/B test multiple video variations without budget constraints. TikTok Shop brands could produce UGC-style videos at scale.

Should businesses adopt? Not yet – it’s research, not product. But start planning: if costs drop, how will you scale video production? Prepare content databases, templates, and product feeds today.

Practical advice: Subscribe to VEONIB or similar platforms that will integrate cost-efficient models as they become available. Monitor arXiv for STEM-based implementations.

Other Notable News in the AI Ecosystem

The podcast covered several other stories worth noting for ecommerce video creators:

VEONIB Insight

Why this matters: These side stories reinforce that the AI video landscape is becoming more accessible (via open models) and more regulated (via legislation). Merchants must balance innovation with compliance.

What it means for ecommerce: Open models like Molmo2 can reduce dependency on proprietary APIs. Use them for internal video analysis (e.g., checking that every product is properly labelled). The Defiance Act makes it critical to use only rights-cleared images and video – never use generative AI to create deceptive product imagery.

How These Changes Affect AI Video Generation Workflows

For merchants using a platform like VEONIB, each of these developments influences different stages of the product-to-video pipeline:

  1. Product URL → Product Analysis: ChatGPT ads have no direct impact yet, but if OpenAI’s API pricing changes, the cost of this step could vary. The Thinking Machines case suggests relying on multiple LLMs for analysis is wise.
  2. Script Generation: STEM’s efficiency gains could make GPU-based script generation faster. FLUX.2 klein could enable real-time script-to-image generation.
  3. Storyboard → Image Prompt → Video Prompt: FLUX.2 and Molmo2 improve image and video model speed. The adoption of embedding modules like STEM could reduce generation latency.
  4. AI Video → Voice → Subtitle → Publishing: Open-source models and reduced inference costs will make this pipeline scalable for high-volume ecommerce.

VEONIB Insight

VEONIB’s modular architecture is future-proof: Because VEONIB supports multiple AI models behind the scenes, merchants automatically benefit from whichever model becomes cheapest or fastest. The STEM paper, if commercialised, will be integrated into the backend to lower costs without merchants changing their workflow. The Thinking Machines drama reinforces why VEONIB does not depend on a single model vendor.

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FAQ

Will ChatGPT ads affect the API cost for AI video generation?
Possibly. If ads generate significant revenue, OpenAI may subsidise API costs. Conversely, if ads fail, they may raise prices. Monitor OpenAI’s announcements in Q2 2026.

Should I stop using Thinking Machines’ technology because of the talent exodus?
If your video workflow depends on a Thinking Machines API, yes – consider migrating to a more stable provider. Their model roadmap is now uncertain.

How soon will the STEM paper impact my video generation costs?
Likely 6–12 months before integration into commercial APIs. Plan for cost reductions but don’t delay current video production.

Can I use open-source models like Molmo2 for ecommerce video analysis?
Yes, Molmo2 is a good choice for internal video tagging and product detection. It requires technical integration; for plug-and-play, use VEONIB.

Is it safe to use AI-generated product videos after the Defiance Act?
Yes, as long as your videos don’t contain explicit, non-consensual content. Stick to product imagery you own or have licensed.

What is the best AI model for script generation in 2026?
There is no single best. Use GPT for creative copy, Claude for safety-compliant scripts, and Gemini for multimodal descriptions. VEONIB lets you switch between them.

References

Sources

Try VEONIB

VEONIB converts a product URL into a full video production pipeline: product analysis, script, storyboard, image prompts, video prompts, and AI-generated marketing videos. Visit VEONIB to see how multi-model support keeps your video generation agile and cost-effective.

Credibility Assessment

Information about the ChatGPT ad test, Thinking Machines departures, and the STEM paper comes directly from the podcast hosts (Andrey Kurenkov and Jeremie Harris), who cited external news outlets (Ars Technica, NYT, arXiv). VEONIB has analysed potential implications for ecommerce AI video based on general industry patterns. The timeline for STEM adoption is speculative; actual product integration depends on platform providers. The article’s recommendations are grounded in practical risk management, not insider knowledge. All external references are to official or widely recognised sources.