For thirty years we built data systems for human analysts, who supply the context, judgment, and skepticism to work around data that’s incomplete or wrong. Autonomous agents supply none of that. They act on whatever they’re handed, confidently. For data to be AI-ready we need to build a series of layers: a data foundation that makes data trusted, a context layer to apply proper meaning, and an access layer that supports and controls how agents operate on that data. While doing this we need continuous attention to observability that ensures the data is properly governed and we have an auditable trace of its use in decision-making. — Read More
Daily Archives: August 31, 2026
Mark Zuckerberg had a bold plan to replace Meta staff with AI. Here’s how it imploded.
In January, Meta CEO Mark Zuckerberg and his top lieutenants gathered for their annual leadership retreat at his Hawaii compound. There they hatched a radical plan to reimagine work at the social-media giant in the age of artificial intelligence.
… But on the night of May 19, just hours before the first layoff wave, Zuckerberg blinked. By then, Meta employees were in open revolt, convinced that the company’s AI transformation initiatives were partly aimed at replacing them. [T]he social-media giant attempted to position itself at the forefront of an AI-driven workplace overhaul, only to stumble in the execution. — Read More
Base Models Stopped Being the Bottleneck
… Base models embed a lot of raw knowledge inside them, and this knowledge scales with the number of parameters, but we can use that base model and prune it in order to steer it through post-training to be good at specific tasks, even with a reduction of their core parameters. We model the raw knowledge to become performant in the tasks we are interested in (model is becoming a loaded word).
I don’t know about you, but with how things are evolving, I am seeing closer and closer the day where we can run these models “cheaply” at home (or in the cloud, for those with a bigger risk appetite), and we can have plug and play AI. We are getting closer to “the solar panel moment.” — Read More
Automated researchers can reliably mitigate alignment failures
As AI begins to build itself, automating alignment research becomes increasingly important to let safety research keep pace. Although measuring the success of alignment research is enormously challenging, researchers (at Anthropic and elsewhere) have developed benchmarks and automated auditing tools, such as Petri, that quantify common alignment failures, like deception, sycophancy, and jailbreaks.
In one of our earlier experiments, we tasked Claude with finding effective ways to use weak AI models as “teachers” to supervise the training of stronger models (in this case, the “student” model). Now, we’re releasing a new report that builds on this idea. We had Claude autonomously train models to improve their performance on several public benchmarks that measure each of 10 categories of alignment failure. — Read More
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