David Bigelow 🔳 retweeted
BREAKING: @GoogleDeepMind just announced Gemini 4 Argon, its most powerful model yet! Google says it matches or beats leading OpenAI and Anthropic models across coding and cybersecurity benchmarks, with strong results in science, maths and professional work.
The focus is on complex tasks that require sustained reasoning: working through large codebases, debugging, and handling extended workflows in finance and law. Google is already using Argon internally for coding, quantum computing research and data centre optimisation. This is the kind of capability jump I've been waiting for from Gemini.
Access is initially limited to selected companies and governments through Google's "Fairwind" programme, focused on finding and fixing cybersecurity vulnerabilities. No public release date yet. Now I want to get my hands on it!
David Bigelow 🔳 retweeted
People laughing at the 2 years in stealth have absolutely no brain cells.
The timing was truly impeccable.
The internet is breaking right now as it did when ChatGPT launched.
I have never been so bullish.
Jev is really what so many wanted but instead they gave us bloated models.
I am willing to bet Diogo woke up many days prior saying today might be the day but chose to wait, knowing a more perfect day would come.
I don't think this could have been executed more perfectly.
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x cheaper (w/ output tokens free)
• Frontier composable intelligence optimized for decisions
AFAICT the shortest path to AI-based economic revolution
Last month I wrote about how we can build a positive and safe future for everyone: meta.com/thefutureisforevery…
Every lab has the responsibility and incentive to move at the pace required to train its models safely, and the ability to take its own actions to ensure that happens.
The reality is:
- People won't want to use agents that are misaligned with them and that don't do what they ask, so labs have a strong natural incentive to make their models more aligned.
There is a lot of debate about slowing progress on capabilities until alignment catches up. My view is that trust and alignment are quickly becoming the most important capabilities that will differentiate agents and models. Any lab that doesn't focus on alignment will fall behind.
- Labs face significant liability if their models cause harm, so they have a strong incentive to prevent this as well.
Meta delayed shipping Muse for several months to focus on safety and security. We didn't call for everyone else to do this before we would. We just did it as part of our day-to-day work because it was clearly the right thing for people and for us. I'm proud of the security foundations we've built.
- Engaging independent evaluators and advisors is industry best practice. MSL already does this today in several areas because it helps produce better work. Other labs can just do this too. In general, it would be helpful for there to be a larger and more diverse ecosystem of evaluators.
- Committing the significant majority of compute towards serving people rather than racing towards recursive self-improvement is one of the best ways to ensure we develop this technology safely. Meta has made this commitment and other labs can do this as well.
I believe the key to building a positive future for everyone is maintaining the right balance of power. This is within our power to do.











