INSANE AI News: GPT-RED, Kimi K3, Gemini 3.5 Pro and Anthropic's "END GAME"

2026-07-16 · Source: Wes Roth · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Intermediate, extended

Summary

Recent AI developments include Moonshot AI's Kimmy K3, rumored for a July 15th release with 2.5 trillion parameters and a 1 million context window, showing early impressions "on par with Fable 5." Ex-OpenAI researcher Mera Miati's Thinking Machines launched Inkling, an open-weight model finetunable on their Tinker platform, performing just under GPT 5.6 Soul. This reflects a strategy, also seen at Microsoft AI, of offering models freely and selling customization services. Google's Gemini 3.5 Pro faces internal delays and reported weaknesses, prompting co-founder Sergey Brin to lead an AI strike team focused on agentic coding. Meanwhile, Anthropic is strategically hiring for Recursive Self-Improvement (RSI), emphasizing compute resources with a Google agreement for over 1 million TPUs. OpenAI introduced GPT Red, an automated red teaming model using self-play reinforcement learning, achieving 84% attack success rates against vulnerabilities, significantly outperforming human red teamers.

Key takeaway

For Directors of AI/ML evaluating model deployment strategies, you should recognize the emerging viability of business models centered on providing open-weight models for free, coupled with paid, specialized fine-tuning services. This approach, exemplified by Thinking Machines and Microsoft AI, allows you to maintain data privacy and potentially reduce costs associated with chasing leaderboard-topping generalist models. Consider investing in internal capabilities for reinforcement learning and customization to leverage these offerings effectively, rather than solely relying on black-box API access.

Key insights

Recursive Self-Improvement (RSI) and AI-driven safety mechanisms are rapidly advancing, shifting AI development paradigms.

Principles

• AI models can self-improve for both capabilities and safety.
• Open-weight models with customization services offer a viable business model.
• Compute availability is a critical bottleneck for frontier AI development.

Method

OpenAI's GPT Red trains an automated red teaming model using self-play reinforcement learning, rewarding it for eliciting valid failures against defender LLMs, which simultaneously learn to defend.

In practice

• Utilize automated red teaming models like GPT Red for scalable vulnerability discovery.
• Explore open-weight models for fine-tuning with proprietary data for specialized applications.

Topics

• Recursive Self-Improvement
• AI Safety
• Automated Red Teaming
• Model Fine-tuning
• Open-Weight Models
• Compute Infrastructure

Best for: CTO, VP of Engineering/Data, AI Architect, AI Scientist, Director of AI/ML, Research Scientist

Related on AIssential

• The Sequence Radar #897: Last Week in AI: China, Compression and the Open-Model Race — AI's future hinges on distributed access and governance, fragmenting the intelligence frontier beyond raw capability.
• Kimi K3's Victory Over Anthropic Examined — Chinese AI labs are rapidly advancing large open-weight models, challenging established Western AI companies and regulatory frameworks.
• You Are Being Told Contradictory Things About AI — Contradictory narratives dominate AI's future, spanning job displacement, AGI paths, and the necessity of recursive self-improvement.
• Open-Weight AI Models — Open-weight AI models offer direct control, customization, and cost-effectiveness for production workloads.
• Murati's 975B Model Fine-Tuned Itself on Launch Day — and Cracked the Top US Open-Weight Spot — Inkling, a 975B-parameter model, demonstrated autonomous self-fine-tuning to adapt its behavior post-training.
• Thinking Machines Unveils New AI Technology — AI innovation focuses on specialized models, security, integration, and strategic compute infrastructure.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Wes Roth.