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
Monthly Archives: August 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
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
Dylan Patel – Anthropic & OpenAI will have most of the world’s compute by 2028
Had a lot of fun chatting again with my twin brother Dylan Patel.
We went through lab economics over the next few years – the shift from inference to training as RSI draws near; and how Anthropic and OpenAI are on track to control most of the world’s usable FLOPs within the next few years (because they can monetize compute better and thus outbid everyone).
And then we discuss whether the >$10T of total AI capex we’ll see by the end of the decade will cause a sovereign debt crisis, where hyperscaler debt raises interest rates, drives non-AI exposed countries into bankruptcy, and crashes non-AI equities.
One question we weren’t able to resolve is whether there’s anything that can counter all the forces barrelling towards centralization in this industry – the economies of scale in training, the scarcity of compute, and eventually continual learning and RSI. — Read More
The turbulent AI era is here. The choices we make now are critical.
… AI will either be the greatest equalizer ever invented, or the worst source of injustice. The challenge is monumental. Even under the best circumstances, the transition to this new AI era will be one of the most turbulent times in human history. How will we use this technology to make the world a fairer place and keep it from widening the divide between rich and poor? How will we protect the people who are most vulnerable to the harms caused by artificial intelligence, including those who lose their livelihoods and the sense that they are in control of their future?
I believe that answering these questions and acting on the answers should be the world’s top priority. If the world takes the right steps AI will be a force for good and leave everyone better off. — Read More
Strategy in the age of AI – Eight points beyond the obvious
What happens when AI changes not only how companies compete, but the very game they are competing in?
[V]iewing AI through the traditional mechanisation/automation lens traps us in looking for operational benefits (much of the discourse today), when the real advantage lies in understanding its impact on the rules of competition. It changes where scarcity lies, how industries are organized, and what constitutes a defensible advantage.
As AI makes intelligence within individual modules abundant, advantage increasingly moves to those who can connect activities, learn across their boundaries, and improve the whole system together.
Vertical integration may therefore be returning, but in a new form. The prize is no longer necessarily ownership of every asset. It is ownership of the feedback loops that connect them. — Read More
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