For most of my career, technology governance began with a familiar set of questions: Is the system secure? Is it resilient? Does it meet the architecture standard? Can we afford it? Those questions still matter. But they are no longer enough.
AI is moving rapidly from producing content and recommendations to initiating actions. It can route work, change code, approve exceptions, communicate with customers, trigger transactions and coordinate other systems. In that environment, the most important question may not be what the technology can do. It is who, or what, has the authority to do it. — Read More
Recent Updates
AI token prices are hitting new record lows
A closely followed measure of artificial intelligence token prices touched fresh lows this week, the latest sign of deflating prices in an increasingly competitive landscape.
The LLM Token Expenditure Index, a key gauge of daily prices from intelligence firm Silicon Data, fell to 97 cents on Monday. That marked the index’s lowest reading since its creation late last year and has fallen by more than half from the high recorded earlier this summer.
….The recent drop is driven in part by the rise of open-source Chinese models like Moonshot’s Kimi K3 that can fetch lower prices than alternatives from leading frontier labs, according to a Tuesday post from Charles-Henry Monchau, investing chief at Syz Group. — Read More
The Five Horses of Bandwidth
Over the last two weeks, a series of events like Hot Chips have laid out the roadmap for the AI industry for the remainder of the decade.
And the conclusion is pretty straightforward: Bandwidth is king.
Investors love magic solutions, so here’s one: success in AI progress over the next five years at least will be proportional to how much bandwidth-pilled your bets are. Bandwidth constrains progress, and most hardware decisions today are moving toward increasing bandwidth. — Read More
When the Source Attacks Back: Prompt Injection Is Coming for OSINT
If you let an AI read untrusted internet content for you, you are no longer just investigating a source.
You are giving that source a chance to investigate you back.
We already know the open web lies. It lies through fake personas, recycled images, synthetic media, planted narratives, scraped junk, and dashboards that look smarter than the people using them.
Prompt injection is different.
It is not content trying to convince you.
It is content trying to tell your AI what to do. — Read More
On-Premise AI for the Newsroom: Evaluating Small Language Models for Investigative Document Search
Investigative journalists routinely confront large document collections. Large language models (LLMs) with retrieval-augmented generation (RAG) capabilities promise to accelerate the process of document discovery, but newsroom adoption remains limited due to hallucination risks, verification burden, and data privacy concerns. We present a journalist-centered approach to LLM-powered document search that prioritizes transparency and editorial control through a five-stage pipeline—corpus summarization, search planning, parallel thread execution, quality evaluation, and synthesis—using small, locally-deployable language models that preserve data security and maintain complete auditability through explicit citation chains. Evaluating three quantized models (Gemma 3 12B, Qwen 3 14B, and GPT-OSS 20B) on two corpora, we find substantial variation in reliability. All models achieved high citation validity and ran effectively on standard desktop hardware (e.g., 24 GB of memory), demonstrating feasibility for resource-constrained newsrooms. However, systematic challenges emerged, including error propagation through multi-stage synthesis and dramatic performance variation based on training data overlap with corpus content. These findings suggest that effective newsroom AI deployment requires careful model selection and system design, alongside human oversight for maintaining standards of accuracy and accountability. — Read More
Runway News: Introducing Solaris
Today, we’re sharing Solaris: the first model in a new family of AI systems we call Interface World Models. Solaris starts with a question: what happens when an operating system generates apps and websites as you use them?
Every operating system, from early terminals to Linux and macOS, has dictated what’s rendered on screen and what happens when a person or program acts on it. Applications get built on top, and stay fixed until someone pushes an update. Solaris instead renders that layer directly. It’s a real-time interactive model that generates the interface itself, frame by frame. Every frame is synthesized as you interact, allowing the interface to respond continuously to your actions. — Read More
Andrew Ng: The Biggest Opportunities in AI Aren’t Where You Think
Just Another Transformation
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
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
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