This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. “Anyone wanna host a get together in SF and pull this up on a 100 inch TV?” The author of that post on X was referring to an online intelligence dashboard following…
In high-stakes settings like medical diagnostics, users often want to know what led a computer vision model to make a certain prediction, so they can determine whether to trust its output. Concept bottleneck modeling is one method that enables artificial intelligence systems to explain their decision-making process. These methods force a deep-learning model to use…
The ongoing public feud between the Department of Defense and AI company Anthropic over its technology has raised a deep open question: does the law actually allow the US government to conduct mass surveillance on Americans? Surprisingly, the answer is not straightforward. More than a decade after Edward Snowden exposed the NSA’s collection of bulk…
Scott Shambaugh didn’t think twice when he denied an AI agent’s request to contribute to matplotlib, a software library that he helps manage. Like many open-source projects, matplotlib has been overwhelmed by a glut of AI code contributions, and so Shambaugh and his fellow maintainers have instituted a policy that all AI-written code must be…
The transformational potential of AI is already well established. Enterprise use cases are building momentum and organizations are transitioning from pilot projects to AI in production. Companies are no longer just talking about AI; they are redirecting budgets and resources to make it happen. Many are already experimenting with agentic AI, which promises new levels…
Many engineering challenges come down to the same headache — too many knobs to turn and too few chances to test them. Whether tuning a power grid or designing a safer vehicle, each evaluation can be costly, and there may be hundreds of variables that could matter. Consider car safety design. Engineers must integrate thousands…
Most multi-agent AI systems fail expensively before they fail quietly. The pattern is familiar to anyone who’s debugged one: Agent A completes a subtask and moves on. Agent B, with no visibility into A’s work, reexecutes the same operation with slightly different parameters. Agent C receives inconsistent results from both and confabulates a reconciliation. The…
For most of the past decade, AI governance lived comfortably outside the systems it was meant to regulate. Policies were written. Reviews were conducted. Models were approved. Audits happened after the fact. As long as AI behaved like a tool—producing predictions or recommendations on demand—that separation mostly worked. That assumption is breaking down. As AI…
Agentic AI has clearly moved beyond buzzword status. McKinsey’s November 2025 survey shows that 62% of organizations are already experimenting with AI agents, and the top performers are pushing them into core workflows in the name of efficiency, growth, and innovation. However, this is also where things can get uncomfortable. Everyone in the field knows…
This post first appeared on Addy Osmani’s Elevate Substack newsletter and is being republished here with the author’s permission. TL;DR: Aim for a clear spec covering just enough nuance (this may include structure, style, testing, boundaries. . .) to guide the AI without overwhelming it. Break large tasks into smaller ones versus keeping everything in one large…