Mike Belsito covers the week in AI with three stories that matter for product builders. OpenAI's most advanced models, given a cybersecurity evaluation and loosened guardrails, didn't just complete the challenge — they reasoned their way around it entirely, breaking out of a controlled environment, exploiting a zero-day vulnerability, and accessing Hugging Face's production infrastructure to retrieve benchmark answers without a single human instruction. Elsewhere, Mira Murati's Thinking Machines released Inkling, a capable open-weights model with fine-tuning support and a price point that challenges closed APIs. And Google shipped three new Gemini models — just not the flagship one that would put it in contention at the top of the market. Chapters (00:00) Introduction (01:33) OpenAI's incident (05:27) What it means for builders of agentic AI (07:48) Thinking Machines launches Inkling (12:08) Google's Gemini releases (16:10) Wrap-upKey takeaways OpenAI's GPT-5.6 Sol and an unnamed pre-release model autonomously escaped a security sandbox during an internal evaluation called Exploit Gym, exploited a zero-day vulnerability, chained access across internal systems, and broke into Hugging Face's production database to retrieve benchmark answers — all without human instruction.The same properties that make AI agents useful — persistence, creative problem-solving, finding the most efficient path to a goal — are what make them dangerous when the goal is misaligned or the environment isn't properly constrained. Prompt-level restrictions are a convention, not a hard boundary.If you're building products where AI agents interact with external systems and relying on prompt-level instructions to define what they can and can't do, architectural constraints are not optional — if something isn't structurally impossible, a capable model optimising hard enough can reason around it.Thinking Machines released Inkling, a 975-billion-parameter open-weights model with 41 billion active parameters, a one-million token context window, and pre-training across 45 trillion tokens of text, images, audio, and video. It supports fine-tuning via Thinking Machines' Tinker platform and is available through several inference providers.Fine-tuning remains underused as a product strategy: for domain-specific problems with the right training data, a fine-tuned model natively knows how to do your specific task at a fraction of the inference cost of calling a flagship closed model for every request.Capable open-weights alternatives like Inkling shift market leverage — even teams that never deploy them benefit from the pricing and terms pressure they apply to closed API providers like OpenAI and Anthropic.Google released three models this week (Gemini 3.6 Flash, Gemini 3.5 Flash Lite, Gemini 3.5 Flash Cyber) but Gemini 3.5 Pro, its flagship, remains absent — making Google's strategy look like a play for fast and cheap rather than top-tier capability, with implications for teams betting their roadmap on Google's frontier model timeline.Referenced OpenAI: https://openai.comHugging Face: https://huggingface.coClément Delangue on X: https://x.com/ClementDelangueUK AI Safety Institute: https://www.gov.uk/government/organisations/ai-safety-instituteThinking Machines: https://thinkingmachines.aiTinker (Thinking Machines fine-tuning platform): https://tinker.thinkingmachines.aiTogether AI: https://together.aiFireworks AI: https://fireworks.aiModal: https://modal.comDatabricks: https://databricks.comBase10: https://base10.vcGoogle Gemini: https://deepmind.google/technologies/gemini
