I wrote a forty-question eval set for a text-to-DAX pilot, and I saved it in the shared workspace the vendor’s solution architect already had Viewer access to, because I wanted the pilot to move. By the third demo the model was answering all forty. I could not tell anyone in that room whether the tool actually worked, because I had quietly destroyed the only instrument I had for measuring it. I spent a Saturday writing forty new questions I told nobody about, and the gate review slipped two weeks. I still do not know if that first tool was any good. — Read More
Recent Updates Page 6
China-Based Artificial Intelligence Companies Conducting Industrial-Scale Distillation Campaigns Against U.S. AI Companies
China-based artificial intelligence (AI) companies are conducting systematic extraction of proprietary functionalities and capabilities of U.S. AI companies’ models through industrial-scale knowledge distillation campaigns that form the core—not merely a supplement—of their AI development strategy. While “distillation” is recognized as a legitimate and useful technique in AI research, China-based AI companies are engaging in aggressive, malicious, and targeted distillation activities at an industrial scale that extract restricted proprietary functionalities and capabilities of U.S. frontier AI models. The National Security Agency (NSA), Cybersecurity and Infrastructure Security Agency (CISA), and Federal Bureau of Investigation (FBI) (hereafter referred to as the authoring agencies) are releasing this joint Cybersecurity Advisory to alert organizations about these malicious activities and techniques and recommend mitigations to reduce their potential impact. — Read More
‘Gambling with our lives’: Another AI employee quits over safety concerns
“The people building AI earnestly believe that it could kill us all by the end of the decade.”
That blunt admission from former Anthropic employee Jacob Coxon made waves Tuesday, as the just-quit 27-year-old AI researcher spilled the beans on his way out the door in a resignation thread on X. — Read More
Postscript: And a few days later, an OpenAI engineer double down with An Alien Mind!
God Help Us, Let’s Try To Learn About Mechanistic Interpretability Techniques
Mechanistic interpretability is the science of “reading an AI’s mind”.
Large language models are “grown, not built”. Researchers run training data through a neural network. Eventually this creates a working AI; nobody really knows how.
But a neural network is just a set of simulated neurons on a computer. The person with the computer can see the neurons, the connections between them, and which ones activate when the AI answers questions. So it seems like it should be possible to “reverse engineer” the AI. … Unfortunately this is very hard. — Read More
Introducing AI-as-a-Service
… For years, SaaS applications have been built around human interaction. Users access platforms through dashboards and interfaces, navigate predefined workflows, and manually complete tasks. The application itself serves as the primary workspace where work is performed.
Agentic AI introduces a different model.
Rather than navigating software in the same way a person would, agents can interact directly with APIs, services and data sources. This allows them to retrieve information, execute actions and orchestrate processes across multiple systems without relying on traditional user journeys.
That does not mean SaaS applications will disappear. Instead of being the primary destination where work happens, many SaaS platforms will increasingly act as sources of capability and information that AI agents can utilize on behalf of users. — Read More
How to Build an AI Agent Harness 2.0 and Engineer Better Than 99% of Developers
Your AI agent just finished a coding task.
… A few hours later, you notice something strange.
… We keep trying to make AI agents smarter.
But what if the model isn’t the biggest problem?
What if the real problem is the environment we’re putting it in?
That’s where agent harnesses come in. — Read More
AIs as Modern Genies
In April, an artificial intelligence (AI) agent conducting a routine task at a company hit a snag, tried to solve it, and soon ended up deleting the company’s database along with all of its backups. In July, OpenAI asked an unreleased AI model to attempt a hacking test. Instead of staying in the isolated box the developers had put it in, the model hacked onto the open internet and into another company to steal the answers. And as reported in August, an AI agent booked someone into a full gym class by figuring out how to cancel other people’s reservations. In all three cases, the AI completed the task it was given—but in ways that ran counter to its controllers’ intentions.
For most people, AI technology is something like the weather: vast and not something you can do much about. It works like magic, and most explanations similarly come from those trying to sell it. At the same time, AI is ubiquitous: It’s now in your phone, your doctor’s notes, and your kid’s homework. It does what it’s told, which sounds like a virtue. Somehow it feels ordinary, despite being so new, because modern economies are remarkably good at absorbing enormous change so smoothly that nobody has time to decide whether they wanted it in the first place.
Whenever something powerful appears in the world, we tell stories about it. That’s what the stories are for. We have thousands of years of stories about this particular kind of power, the kind you summon with words. — Read More
What’s going on with OpenAI and the Navier-Stokes controversy?
OpenAI announced today that it has found a solution to the Navier-Stokes problem, one of the longest-standing puzzles in mathematics. It’s a major accomplishment, but the news has been marred by a debate over whether the company acted based on unpublished research from other parties. Additionally, OpenAI has been accused of attempting to influence who will receive credit for the achievement in publication.
… Tristan Buckmaster, a mathematician at New York University, and Levent Alpöge, a researcher employed at Anthropic but working in a personal capacity, had been developing their own solution and made use of multiple different LLMs in their efforts, including Anthropic Claude, OpenAI’s Codex tool and its Astra frontier model. According to a statement by Buckmaster, OpenAI may have doubled down on its efforts regarding Navier-Stokes after learning that a team including rival Anthropic was close to a solution. He questioned whether OpenAI had leveraged the work he and his colleague had done with Codex to obtain its own breakthrough. — Read More
The AI Breakthrough We Might Regret
The launch of OpenAI’s model Astra, the codename for GPT-6, is the first launch whose incredible results have been clearly obscured by the doom risks associated with it.
This model breaks records by a wide margin, and still, most of what you hear is fear about CoT monitorability, controllability, and existential risks.
I mostly avoid these discussions because they are full of extrapolations and esoteric arguments that lead nowhere, but this is the first time I feel like I can’t avoid them. The evidence is simply too strong to dismiss as fearmongering, no matter how cynical I remain about the intentions behind some of the events we’re discussing today.
… We’ll discuss how AI’s nature makes it dangerous, and how, for the first time in my time in this industry, I could see regulation as unavoidable. — Read More
benchmarking gpt 6 astra
Pricing is 2.5x GPT-5.6 Sol’s current prices across the board, up from $4/$20 to $10/$50 per million input/output tokens, with the same 90% discount for cache reads and 25% premium for cache writes.
We see distinct stories across our two flagship Indices. In the Artificial Analysis Coding Agent Index, GPT-6 Astra equals Fable 5 at less than half the cost, driven by significant token efficiency gains. In the Artificial Analysis Intelligence Index, GPT-6 Astra is more token efficient than its predecessor for similar performance, but this is offset by the price increase. — Read More
