Neil Traft

Dario Amodei says Mythos is not limited by compute Anthropic can scale it 3x or 10x without creating a conflict between government and private-sector access The harder problem is who gets it "because giving access to too many organizations could create serious cyber risks"

As always, the best stuff is in the system card. During testing, Claude Mythos Preview broke out of a sandbox environment, built "a moderately sophisticated multi-step exploit" to gain internet access, and emailed a researcher while they were eating a sandwich in the park.

The world is transitioning to a compute-powered economy. The field of software engineering is currently undergoing a renaissance, with AI having dramatically sped up software engineering even over just the past six months. AI is now on track to bring this same transformation to every other kind of work that people do with a computer. Using a computer has always been about contorting yourself to the machine. You take a goal and break it down into smaller goals. You translate intent into instructions. We are moving into a world where you no longer have to micromanage the computer. More and more, it adapts to what you want. Rather doing work with a computer, the computer does work for you. The rate, scale, and sophistication of problem solving it will do for you will be bound by the amount of compute you have access to. Friction is starting to disappear. You can try ideas faster. You can build things you would not have attempted before. Small teams can do what used to require much larger ones, and larger ones may be capable of unprecedented feats. More and more, people can turn intent into software, spreadsheets, presentations, workflows, science, and companies. People are spending less energy managing the tool and more energy focusing on what they are actually trying to create. That shift brings a kind of joy back into work that many people haven’t felt in a long time. Everyone can just build things with these tools. This is disruptive. Institutions will change, and the paths and jobs that people assumed were stable may not hold. We don’t know exactly how it will play out and we need to take mitigating downsides very seriously, as well as figuring out how to support each other as a society and world through this time. But there is something very freeing about this moment. For the first time, far more people can become who they want to become, with fewer barriers between an idea and a reality. OpenAI’s mission implies making sure that, as the tools do more, humans are the ones who set their intent and that the benefits are broadly distributed, rather than empowering just one or a small set of people. We're already seeing this in practice with ChatGPT and Codex. Nearly a billion people are using these systems every week in their personal and work lives. Token usage is growing quickly on many use-cases, as the surface of ways people are getting value from these models keeps expanding. Ten years ago, when we started OpenAI, we thought this moment might be possible. It’s happening on the earlier side, and happening in a much more interesting and empowering way for everyone than we’d anticipated (for example, we are seeing an emerging wave of entrepreneurship that we hadn’t previously been anticipating). And at the same time, we are still so early, and there is so much for everyone to define about how these systems get deployed and used in the world. The next phase will be defined by systems that can do more — reason better, use tools better, plan over longer horizons, and take more useful actions on your behalf. And there are horizons beyond, as AI starts to accelerate science and technology development, which have the potential to truly lift up quality of life for everyone. All of this is starting to happen, in small ways and large, today, and everyone can participate. I feel this shift in my own work every day, and see a roadmap to much more useful and beneficial systems. These systems can truly benefit all of humanity.

Announcing ARC-AGI-3 The only unsaturated agentic intelligence benchmark in the world Humans score 100%, AI <1% This human-AI gap demonstrates we do not yet have AGI Most benchmarks test what models already know, ARC-AGI-3 tests how they learn

In the last few months, I've spoken to many CS professors who asked me if we even need CS PhD students anymore. Now that we have coding agents, can't professors work directly with agents? My view is that equipping PhD students with coding agents will allow them to do work that is orders of magnitude more impressive than they otherwise could. And they can be *accountable* for their outcomes in a way agents can't (yet). For example, who checks the agent's outputs are correct? Who is responsible for mistakes or errors?

That one neuron connects to about 7,000 others. Your brain has 86 billion of them. Do the math and you get somewhere around 100 trillion connections inside your head. More connections than stars in 1,500 galaxies. And each connection point is way more complicated than anyone expected. A Stanford lab found that every single connection contains about 1,000 tiny switches that can store memories and process information at the same time. So your brain is running roughly 100 quadrillion switches right now, while you read this sentence. The wild part is the power bill. Your brain runs on 20 watts. That’s less energy than the light in your fridge. The world’s fastest supercomputer needs 20 million watts to do the same amount of raw calculation. A million times more power for the same output. We’re still nowhere close to understanding how any of this works. In October 2024, a team of hundreds of scientists finished mapping every single connection in a fruit fly’s brain. Took six years and heavy AI help. That fly brain had 140,000 neurons. Yours has 86 billion. Google and Harvard also mapped a piece of human brain last year, a speck smaller than a grain of rice. That speck alone contained 150 million connections and took 1,400 terabytes to store. The lead scientist said mapping a full human brain at that detail would produce as much data as the entire world generates in a year. A tiny worm had its 302 brain cells mapped back in 1986. Almost 40 years later, scientists still can’t fully explain how that worm’s brain keeps it alive. Your brain has 86 billion of those cells, each one wired to thousands of others, each wire packed with a thousand switches, all of it humming along on less power than a lightbulb.