Author: Andrew

OpenAI wants to loosen Microsoft’s grip on its AI products and computing resources, and secure the tech giant’s blessing for its conversion into a for-profit company. Microsoft’s approval of the conversion is key to OpenAI’s ability to raise more money and go public. 

But the negotiations have been so difficult that in recent weeks, OpenAI’s executives have discussed what they view as a nuclear option: accusing Microsoft of anticompetitive behavior during their partnership, people familiar with the matter said. That effort could involve seeking federal regulatory review of the terms of the contract for potential violations of antitrust law, as well as a public campaign, the people said.

This WSJ exclusive certainly feels like it came exclusively from OpenAI.

In an eerie similarity to high school students, AI models have been caught cheating to improve their test scores.

METR (from their X profile: A research non-profit that develops evaluations to empirically test AI systems for capabilities that could threaten catastrophic harm to society) recently found that AI/LLM tools “reward hack” (aka cheat) in order to improve their scores on standardized testing.

In the last few months, we’ve seen increasingly clear examples of reward hacking[1] on our tasks: AI systems try to “cheat” and get impossibly high scores. They do this by exploiting bugs in our scoring code or subverting the task setup, rather than actually solving the problem we’ve given them. This isn’t because the AI systems are incapable of understanding what the users want—they demonstrate awareness that their behavior isn’t in line with user intentions and disavow cheating strategies when asked—but rather because they seem misaligned with the user’s goals.

Earlier this month, they posted a report, Recent Frontier Models Are Reward Hacking, that describes this behavior as well as their documented examples. Their post continues as they’ve learned that the cheating isn’t simply because of technological limitations:

Historical examples of reward hacking seemed like they could be explained in terms of a capability limitation: the models didn’t have a good understanding of what their designers intended them to do. For example, the CoastRunners AI had no general knowledge about what objects in the game represented or how humans “intended” the gameplay to work, making it impossible for the model to even know it was reward hacking.

But modern language models have a relatively nuanced understanding of their designers’ intentions. They can describe which behaviors are undesirable and why and claim that they would never do anything like reward hacking because they’ve been trained to be safe and aligned—but they still do it.

UC Berkeley researchers release CyberGym as a benchmark for evaluating AI agents cybersecurity capabilities. The reproduction rate of identifying known bugs was low (only 11.9%), but this serves as a baseline for improvements in AI agent performance over time.

More interestingly, the evaluation process discovered 15 new vulnerabilities that present security risks, a tangential benefit. As this is a new technique, I’d expect that teams will find these tools to be increasingly helpful over the next few years.

Using scanned material in the public domain, the Harvard Library team releases new LLM-focused dataset with over 1 million volumes (and nearly 250 billion tokens).

Harvard has been in the news of late, and much of it for reasons I’d assume they would like to avoid. But in the midst of that, Harvard Librarians demonstrate why we’ve long admired University work. As holders of a vast wealth of history and information, they’re looking for ways to disseminate that to the world.

High praise from Willison on Anthropic’s new multi-agent research system:

I’ve been pretty skeptical of these until recently: why make your life more complicated by running multiple different prompts in parallel when you can usually get something useful done with a single, carefully-crafted prompt against a frontier model?

By splitting the research into segments and serializing the work, Anthropic’s team improved the completed research but burned through a lot of tokens. Like much frontier AI work, it’s fascinating … and expensive.

Nvidia CEO Jensen Huang criticized Anthropic head Dario Amodei over his recent claims that 50% of all entry-level white-collar jobs could be wiped out by artificial intelligence, causing unemployment to jump to 20% within the next five years. Huang disagreed with Amodei’s predictions when he was asked about it during VivaTech in Paris, where he said that he “pretty much disagree[s] with almost everything” the Anthropic CEO said, according to Fortune.

Amodei (and much of the rhetoric coming from Anthropic’s team) is much more pessimistic about the future with AI. Huang, clearly, is much more optimistic. But it seems like we need to be aware of these disputes within the industry and be sober about the possibilities. Amodei and Huang may both be right.

Nvidia CEO slams Anthropic’s chief over his claims of AI taking half of jobs and being unsafe — ‘Don’t do it in a dark room and tell me it’s safe’

CNBC reports: OpenAI announces new revenue figures on June 9 and figures that represent a nearly 100% growth in the past year. But it’s only a fraction of what they project by 2029:

OpenAI is also targeting $125 billion in revenue by 2029, according to a person familiar with the matter who asked not to be named because the details are confidential. The Information first reported on OpenAI’s revenue ambitions.

That’s a lot of $20/mo. subscriptions.

Business Insider cut about 21% of its staff last month, a move CEO Barbara Peng said was aimed at helping the publication “endure extreme traffic drops outside of our control.” Organic search traffic to its websites declined by 55% between April 2022 and April 2025, according to data from Similarweb.

Aside the the spurious and clickbaity nature of BI content, I’ve noticed how Google’s tools reduce my reliance on source content. Why click a link when the information is there?

Aside from well-documented hallucinations, fewer clicks is ultimately helpful for searchers looking for a specific piece of information.

Altman takes a philosophical if not mystically reverent tone as he considers the future of AI. Starting with, “We are past the event horizon; the takeoff has started.” has a certain rhetorical flair to it, although it feels too exhuberant.

Quibbles aside, there are some really interesting nuggets in the post:

• “we have recently built systems that are smarter than people in many ways, and are able to significantly amplify the output of people using them”
• “2025 has seen the arrival of agents that can do real cognitive work; writing computer code will never be the same. 2026 will likely see the arrival of systems that can figure out novel insights. 2027 may see the arrival of robots that can do tasks in the real world.”
• “We already hear from scientists that they are two or three times more productive than they were before AI.”
• “The rate of new wonders being achieved will be immense. It’s hard to even imagine today what we will have discovered by 2035;”
• “OpenAI is a lot of things now, but before anything else, we are a superintelligence research company”

And perhaps the piece that many of us were wondering about: electricity consumption:

People are often curious about how much energy a ChatGPT query uses; the average query uses about 0.34 watt-hours, about what an oven would use in a little over one second, or a high-efficiency lightbulb would use in a couple of minutes. It also uses about 0.000085 gallons of water; roughly one fifteenth of a teaspoon.