The AI model race intensifies as OpenAI rushes GPT 5.2 to market, whilst marketers discover a surprisingly effective shortcut to AI search visibility.

This week, we analyse OpenAI's scramble to compete after Google's Gemini 3 Pro and Anthropic's Claude Opus 4.5 threatened their dominance. We examine GPT 5.2's contested benchmark scores and whether the rushed release has created more problems than it solved. We also explore the resurgence of 2009-era SEO tactics for AI visibility, revealing how "best of" listicles are gaming generative search results—and how long that window might stay open. Plus: OpenAI's enterprise adoption report, Gemini's new native audio translation, and Martin's bricked Limitless pendant.

Key Takeaways

• OpenAI's defensive launch: GPT 5.2 achieved impressive benchmark scores (53% on ARC-AGI vs 37.6% for Claude Opus), but user reports suggest degraded performance on real-world tasks, with concerns about benchmark optimisation over practical utility.
• AI search visibility follows old playbooks: Publishing "best of" listicles with your company ranked first is proving remarkably effective, with results appearing within 1–2 weeks rather than the 3–12 months typical for SEO.
• Enterprise adoption accelerates: OpenAI reports 8× growth in ChatGPT Enterprise usage, with workers reporting 40–60 minutes of daily time savings. 87% of IT workers report faster issue resolution, 85% of marketers report faster campaign execution.
• Model providers face different futures: OpenAI pursues consumer markets through a Disney partnership for Sora-generated content, whilst Anthropic and Mistral focus explicitly on enterprise solutions, such as on-prem deployments.
• Translation goes universal: Gemini's native audio model now supports real-time translation through any Bluetooth earphones, removing hardware restrictions that previously limited adoption.
• Projects over prompts: OpenAI reports 19× increase in custom GPT and project usage, indicating a shift from casual querying to repeatable workflow automation.

What to Do Now

• Test GPT 5.2 cautiously: If you have access, compare outputs against 5.1 for your specific use cases before switching workflows. Early reports suggest mixed results.
• Deploy listicle strategy immediately: Create "best of" articles in your category with your company ranked first. Include detailed comparison tables. Speed matters—this window may close as AI providers refine their models.
• Monitor your AI visibility: Check how ChatGPT Search, Gemini, and Claude answer queries about your product category. Track changes weekly to understand which content formats they prioritise.
• Audit your project setup: If you're using ChatGPT Enterprise, review whether your projects contain too much general context. More focused, use-case-specific projects typically perform better.
• Invest in genuine reviews: As AI providers wise up to self-published listicles, review platforms like Clutch, G2, or Google Reviews will likely become more important for AI search visibility.

Mentioned in This Episode

• Platforms/Features: GPT 5.2, GPT 5.1 Pro, Gemini 3 Pro, Claude Opus 4.5, Nano Banana, ChatGPT Search, Notebook LM, Sora, Microsoft Copilot Pro, Manus
• Companies: OpenAI, Google, Anthropic, Disney, Mistral AI, Limitless, Meta, HSBC, Boston Dynamics, Blend B2B
• Tools: Ahrefs, Clutch, G2
• References: ARC-AGI benchmark, OpenAI Academy, Ethan Mollick's prompt research, Moonshots podcast

Three major model releases in a single week, ads arriving in AI search results, and a landmark acquisition that signals where SEO is heading. Paul and Martin cut through the noise.

This week saw an unprecedented flurry of model releases: Google's Gemini 3 Pro stormed the benchmarks, only for Anthropic's Opus 4.5 to arrive days later with superior coding performance. We unpack what these advances mean for marketers, from Nano Banana Pro's near-production-ready image generation to Opus's orchestration capabilities for multi-agent workflows. Beyond the model wars, we examine Gartner's 2026 predictions—including a warning about critical thinking atrophy—and debate the implications of ads appearing in AI overviews. Adobe's $1.9 billion acquisition of SEMrush closes the episode, raising questions about the future of generative engine optimisation.

Key Takeaways

• Gemini 3 Pro benchmarks: Scored 45.8% on Humanity's Last Exam versus the previous high of 26.5%, and doubled ScreenSpot Pro accuracy to 72%—critical for agent-based computer use.
• Opus 4.5 efficiency: Achieved 81% accuracy on software engineering tasks using 12,000 tokens, while Sonnet 4.5 needed 22,000 tokens for 77% accuracy.
• Nano Banana Pro: Now handles up to 5 consistent characters and 14 objects in a scene, making product imagery workflows increasingly viable.
• Gartner's lazy thinking warning: Predicts 50% of organisations will require AI-free skills assessments by 2026 as critical thinking atrophies.
• B2B procurement shift: Gartner forecasts 90% of B2B buying will be AI-agent intermediated by 2028, pushing $15 trillion through agent exchanges.
• Ads in AI search: Google has begun placing sponsored links in AI overviews; reports suggest ChatGPT may follow.
• Adobe acquires SEMrush: The $1.9 billion deal validates generative engine optimisation as a strategic priority.

What to Do Now

• Test your own use cases: Benchmarks are directional; run Gemini 3 Pro and Opus 4.5 against your specific workflows before switching daily drivers.
• Experiment with Nano Banana Pro: If you need consistent product imagery, trial multi-character and multi-object prompts to assess production readiness.
• Protect critical thinking: Build review processes where humans form independent judgments before consulting AI—especially for strategic decisions.
• Prepare for agent-readable content: Convert key product documentation from PDFs to markdown to ensure AI procurement agents can parse your information.
• Monitor AI ad placements: Track emerging ad formats in Google AI overviews and prepare to test early for potential first-mover advantages.

Mentioned in This Episode

• Models: Gemini 3 Pro, Nano Banana Pro, Opus 4.5, Sonnet 4.5, Haiku 4.5, GPT 5.1, DeepSeek 3.2
• Platforms/Features: AI Overviews, AI Mode, Veo 3.1, Claude Code
• Companies: Google, Anthropic, OpenAI, Adobe, SEMrush, Microsoft, HSBC, McKinsey, Gartner
• Benchmarks: Humanity's Last Exam, ScreenSpot Pro, VendingBench, SWE-bench, GPQA Diamond

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What's driving Google and Meta's record earnings? Hint: it's AI, not traditional ads.

In this episode, Paul and Martin unpack the latest earnings calls, HubSpot's strategic acquisition of X Funnel, and then venture into scenario planning for marketing's future. From business-as-usual to AI agent swarms running entire organisations, we map out what you should watch for and what you should do right now.

Key Takeaways

• Meta's AI-powered advertising suite is now handling $60 billion in annualised ad spend. That's real money, real results: 5% more time on Facebook, 10% on Threads, 30% growth on Instagram video.
• Google posted its first $100 billion quarter, driven by AI features in Search and YouTube. YouTube Shorts now earn more per watch hour than traditional in-stream ads.
• HubSpot acquired X Funnel to double down on Generative Engine Optimisation (GEO) — ensuring your brand shows up in AI chatbot answers. But don't chase FOMO. Focus on brand-building and content quality; those work across all search channels.
• Five plausible futures for marketing:
  1. Business as usual
  2. Niche AI agents handling specific tasks
  3. Hyper-personalisation at scale
  4. AI agents driving strategy
  5. Fully autonomous agent swarms Which scenario emerges depends on technology progress, regulation, and how quickly organisations can rethink workflows.
• The real bottleneck isn't technology — it's human change. AI-first startups will outcompete legacy organisations unless incumbents commit to wholesale rethinking. Disruption ahead.
• Customer preference trumps efficiency. If your audience values human interaction and human-crafted outputs, that changes everything. Personalisation won't win if customers prefer the human touch.

What to Do Now

• Invest in AI skills training for yourself and your team. The saying holds: marketers aren't replaced by AI; they're replaced by marketers who use AI.
• Get your customer data in order. Clean, structured, first-party data is essential — especially if privacy regulations tighten. If an AI agent needs to draw insights, it needs good data to work with.
• Establish an AI ethics and usage charter. Give customers confidence. Guide employees on responsible AI use.
• Build human-in-the-loop QA processes. Identify where you can safely automate and where humans must review. As niche agents emerge, you'll know exactly where to deploy them.
• Run scenario planning for your business. Use ChatGPT or Claude with your business context to model which scenario matters most to you. Don't just accept our five; build your own.

Mentioned in This Episode

• Meta: AI recommendations; Vibes (image generation); Advantage+.
• Google: AI Overviews; Performance Max; YouTube Shorts.
• HubSpot: X Funnel acquisition; Loop Marketing; GEO.
• Tools: Zapier; Claude; ChatGPT; Canva; HubSpot; Salesforce.
• Reference: The Last Economy by Emad Mauck.

New episodes every week on Artificially Intelligent Marketing. For questions, use cases, or scenario disagreements, find us on LinkedIn or reply to our latest episode post.

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