Jeff Crume examines Jev, a new AI approach that makes structured decisions rather than generating text, through a cybersecurity lens. He argues that confidence scores are not evidence, that AI decisions expand the attack surface, and that observability, decomposed reasoning, independent controls, and human oversight are needed before trusting them.
A roundtable of AI power users reviews OpenAI DevDay and asks what actually works in daily use. Topics include personal assistants and their trust, permission, and safety trade-offs; durable agent state and remote execution; the Jev decision model and calibrated classification; model preferences across Anthropic, OpenAI, xAI, Meta, and open models; subagents and context management; and the gap between impressive capabilities and products that hold up.
Jev is a System 1 AI model from TypeSafe that skips text generation entirely, returning calibrated probabilities for fixed-option questions in a single fast request. This explores how it works, how it is trained with reinforcement learning for calibrated decisions, how its probabilities drive software thresholds and guardrails, and how it can complement LLMs in workflows.
Diogo Almeida, founder of TypeSafe AI, joins a16z's Ben Horowitz and Martin Casado to explore why AI is so capable yet so underused for automation. He argues that coding agents make developers faster without making software better, and introduces Jev, a primitive that embeds probabilistic, intent-aware intelligence inside programs. The conversation covers reliability as the key to programmable AI, the revival of probabilistic software, why SaaS companies are well positioned to benefit, and the long-term goal of technology that can reliably "do what I mean."