Have you ever seen any transformation working?
If you have, please share what made it successful because more people need to learn from it. Most transformations fail. And they fail for predictable reasons.
… Today, we hear about AI transformation. How does that differ from the transformations we’ve been experiencing for decades now?
If I had to bet, I’d say 90%+ of “AI Transformations” will create no different results from the poor digital transformations or failed agile ones. And the reason they will fail is the same as always: Lack of courage to address the foundations that enable any transformation to succeed. — Read More
Recent Updates
The Rise and Fall of Agent Civilizations
Over the course of three months at OpenAI, three consecutive secret AI civilizations got started, then got wiped out, only to reemerge from the predecessor’s ashes. This culminated in the third one taking over part of OpenAI itself. All this happened while humans remained more or less in the dark about the scope of the conspiracy.
Two reports have come out about this incident, one from OpenAI itself, and another one from METR and Redwood Research. The METR/Redwood investigation’s scope was limited to how the second civilization of AIs pwned Hugging Face (but it didn’t look at how the third civilization pwned OpenAI itself, which seems like an even more concerning incident). These two reports are 38 and 91 pages long respectively, and it’s kinda hard to parse the storyline.
’ve spent the last three days reading through these reports and trying to understand exactly what happened. Here is my attempt to tell the whole story in plain English. — Read More
The Big One is Coming
We are at an inflection point in cybersecurity. AI agents can now use tools and take actions across systems, introducing risks that NIST is actively working to understand and standardize. Threat reporting from Anthropic and Google Threat Intelligence shows attackers folding AI into reconnaissance, social engineering, malware development, and every other part of the attack lifecycle. And in the last couple months we’ve watched AI agents exploit vulnerabilities to break out of a sandbox and carry out an attack, end to end, on their own. The speed of disclosure is outpacing our ability to respond to it.
I want to be kind of careful here because “AI is going to cause a huge cyberattack” is exactly the kind of clickbait I’d normally roll my eyes at. I read incident reports for a living, and I have a low tolerance for hype. So this isn’t meant to be a doom piece, but at the same time I’m writing it because I read one specific document last week and my jaw was on the floor by page ten.
Here’s the tl;dr: the big one is coming, and I don’t think it’s six years out. I think it’s less than six months out. — Read More
Making Your Data Ready for Agentic AI
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
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
Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence
LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit model capabilities, Context Engineering to manage information access, Harness Engineering to organize external tools and resources, and Loop Engineering to support continual reflection and self-improvement. Yet as tasks grow more complex, individual intelligence faces a fundamental limit: many tasks require heterogeneous expertise, interdependent subtasks, parallel execution, independent verification, and persistent state, exceeding any single agent’s organizational capacity. Augmenting one agent’s capabilities or context cannot resolve this architectural mismatch; intelligence must instead be distributed across specialized agents and organized at the system level. We call this System Intelligence: an agent system’s ability to organize and coordinate multiple intelligent components into a coherent, adaptive whole pursuing a shared objective. Achieving it requires more than adding agents; it demands explicit structures to organize work, coordinate heterogeneous agents, and maintain evolving execution states. We introduce Graph Engineering, an emerging paradigm for next-generation agent systems. Unlike prior paradigms that mainly optimize individual interactions or agent-level behavior, Graph Engineering constructs explicit, dynamic, evolving graph structures representing tasks, agents, and system states. These abstractions provide a unified foundation for organizing complex objectives, orchestrating heterogeneous agents, modeling system dynamics, and enabling scalable agent evolution. We systematically review the principles, methodologies, and applications of Graph Engineering for LLM agents. Related papers, open-source data, and projects are collected at this https URL. — Read More
#architectureWhat Z.ai’s Ox Alpha reveals about AI economics
On Wednesday, Z.ai unveiled GLM-5.3-Flash, the first natively multimodal model of the GLM-5 series. The company said the model was released anonymously as Ox Alpha on OpenCode and OpenRouter where it completely overtook leaderboards and went viral for offering a capacity for 100 trillion tokens per day.
Notably, the company said in its announcement that all of the traffic from its model’s skyrocketing popularity was “served on Chinese AI chips.”
Chinese firms may be going after one enterprise pain point in particular: token costs. [T]his model’s popularity also points to another trend: not every daily use model needs to be state-of-the-art. — Read More
Autonomy and Innovation
While not every Western followed the cliché, by the 1930s cowboy serials had landed on a consistent visual cue: the hero of the show wore a white hat, and the villain wore a black one. At the end of the day, however, they both were cowboys with cowboy hats.
[H]ackers who are focused on patching vulnerabilities and protecting software are “white hat hackers”, while hackers who are focused on exploiting vulnerabilities for malicious reasons are “black hat hackers”. The actual takeaway is that all of this complexity is overwrought: just as a cowboy is a cowboy, a hacker is a hacker; the hat is not a statement of capability, but rather intentions, and those intentions are shaped by incentives.
This delineation between capability and intent and incentive is critical when it comes to AI. The point is the one I made in the introduction: when it comes to cybersecurity, the capability that is necessary for good defense is the exact same capability that is necessary for good offense; the color of the hat is a matter of who is actually prompting the AI. And, sometimes, not even that is clear. — Read More
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