Daily AI Briefing — June 25, 2026

TechCrunch reports OpenAI unveiled its first custom processor, Jalapeno, built with Broadcom and tailored for the specific demands of OpenAI inference systems. It marks OpenAI entry into bespoke AI silicon.

Qualcomm agreed to acquire AI chip software startup Modular for nearly 4 billion dollars, expanding from edge devices into data center infrastructure. Modular built one of the most prominent AI compiler and runtime stacks of the era.

Blackstone committed 30 billion dollars to AI data center development in Japan as competition for AI infrastructure in the country intensifies. The plan joins a wave of multibillion-dollar data center deals.

Revisits data repetition in the Chinchilla scaling era using Compute-Equivalent Gain/Loss, showing repetition damage is systematic and that repeating a moderate subset many times harms performance more than repeating a large subset few times. Important for data-constrained pretraining.

Introduces Autodata, a method to train an agentic data scientist that creates high-quality synthetic training/eval data, with a meta-optimization that improves the agent itself, yielding gains over classical synthetic data methods. From a strong Meta FAIR-style author team.

Provides evidence that thinking tokens do not always improve safety: across frontier open-weight reasoning models, refusal/compliance outcomes are highly predictable from the first token's hidden representation, before any deliberation. Challenges the assumption that deliberation improves alignment.

Evaluates four production real-time voice systems (GPT Realtime 2, Gemini 3.1 Flash Live, Qwen3.5 Omni Plus/Flash) on tasks where vocal delivery conveys meaning, finding they act on words and ignore distress, fear, or sarcasm. Notably the failure is in acting not perceiving, since systems can identify the emotion when asked directly.

iLLaDA is an 8B masked diffusion language model trained fully from scratch with bidirectional attention, scaling pretraining to 12T tokens and improving substantially over the prior LLaDA on general, math, and code benchmarks. It demonstrates continued progress for diffusion-based alternatives to autoregressive LLMs.

John Carmack reflects on early-career mistakes during Quake development, including over-ambition, pushing teams too hard at startup intensity, and poor founder stock arrangements.

Brockman introduces Jalapeño, an inference chip built from scratch over nine months and accelerated by OpenAI's own models, touting strong performance per watt.

Nathan Lambert shares a prerequisites lecture for his book covering language model basics, post-training masks, cross-entropy, KL divergence, and RL framing, built with GLM 5.2.

Continuing the discussion from yesterday, Karpathy explains his org-level harness is a genuinely different, enterprise-grade way of working, deeply integrated and multiplayer, where the system writes most code and everyone becomes a manager, distinct from Slack bots or OpenClaw.