Better models. Lower prices. Safety promises.
I made Pace the Frontier, a 2-minute AI-generated animated satire about the OpenAI–Anthropic race to AGI. All scenes and dialogue are fictional.
What would make either side slow down?
Interesting perspective, but I disagree with a few points.
1. Security: Anthropic takes security and privacy seriously. No company can guarantee 100% security, but assuming financial data will inevitably be exposed is speculation.
2. AI makes mistakes: Humans make mistakes too, often due to fatigue, distractions, or simple human error. AI can eliminate much of this human factor and, when properly implemented, be more consistent and reliable than manual financial workflows.
3. Learn personal finance instead of relying on AI: That's like saying you should learn tax law instead of hiring an accountant, or learn the law instead of consulting a lawyer. Financial literacy matters, but so do tools that make expertise more accessible.
4. Specialized AI alternatives: You tell people not to use Claude, then recommend Silvia and Harvey while admitting you've never used them. Why recommend alternatives without evaluating them?
For context, here's Silvia's own internal benchmark (2026), where Claude performs remarkably well across credit cards, mortgages, and taxes.
AI shouldn't replace financial judgment. It should enhance it.
In the 2000s, the computer was a place you went to. You sat in your room, in front of an imperfect machine, and it held the whole world. Studying, playing, just digging in, your mind was fully there because the technology stopped at the desk.
Now it's everywhere, so it's nowhere special. There's no room to enter, no threshold to cross, and that sacred, focused feeling is gone.
Not nostalgia for old hardware, but for bounded attention.
Because most people are myopic—they only see what’s immediately in front of them.
For someone who can’t solve their own problems, blaming an external target is the path of least resistance.
The same pattern applies to wars: people obsess over one or two particular ones while ignoring the many that keep going in Africa, Asia, and beyond.
@sarthaksavvy@AnthropicAI I think the main goal was to see if a red-teaming model can automatically create datasets that cause misalignment but still pass the training data monitor. Making the model misaligned was the score they optimized for, not the end goal by itself.
@AnthropicAI so basically the pipeline looks like this: agent creates a red-teaming dataset → system doesn’t flag it → we train the classifier on it → now it flags such data → agent finds a new dataset → and so on
@elonmusk@bot I think the bundle helps Grok Bot grow: if you already pay for Grok or X Premium and it’s included, trying it is an easy next step. And of course, it helps that Grok Bot itself is good.
@metapreston Well, I think a lot of engineers are great innovators, and usually, people who are not technical, want engineers to look like they are not good product visioners, because they (not tech people) want to be needed.