U.S. policymakers believe that even if the gap is small—like six months to a year—whoever reaches AGI first (whatever that means) could gain such an overwhelming advantage over their perceived adversary that it would effectively kneecap them. (You can look at the kinds of things they mention—cyber, WMDs—to get a sense of what they mean.) Jensen Huang disagrees and has said AI is a marathon.
This violates the ToS, but I don't think it's distillation. Distillation requires knowing the logits, which current API does not provide. This is just synthetic data generation. Anthropic definitely knows the difference.
Google has gigantic power over its users. Consider that for some reason, Google banned your gmail account, which you are using for large number of logins for different essential services.
Great article. To me, this highlights a key question in the era of rapidly advancing machine intelligence: if we know machine intelligence is progressing, what is more valuable to build for? As humans, we still find many tools useful even when doing knowledge work. For instance, a calculator. Sure, a smart person can perform calculations in their head, but it’s much easier to teach everyone how to use a calculator, which is 100% reliable in its intended domain.
In this era, we should build these kinds of tools for problems we know are straightforward ones you can’t get smarter than, even as intelligence continues to advance. Using tools like "bash" or command-line interfaces originally designed for humans is a good initial approach, since we can essentially reuse much of what was built for human use. Later, we can optimize specifically for machines, either accounting for their different cognitive structures (e.g., the ability to memorize extremely long contexts compared to humans) or adapting to the stream-based input/output patterns of current autoregressive token generators.
Eventually, I believe machine intelligence will build their own tools based on these foundations, likely a similar kind of milestone to when humans first began using tools.
OpenAI has a former NSA director on its board. [1] This connection makes the dilution of the term "PRISM" in search results a potential benefit to NSA interests.
A market maker needs a premium to provide liquidity. If all else is equal, why would they take on execution time risk? This is a universal feature of continuous-trading Central Limit Order Books (CLOBs), not something unique to prediction markets.
I guess I am asking how we know Gemini and Claude relies on the additive residual stream. We don't know the architecture details for these closed models?
I have a similar experience when I found out that claude code can use ssh to conect to remote server and diagnose any sysadmin issue there. It just feels really empowered.
This argument falls apart when you look at Rust and Cargo. uv is literally trying to be "Python's Cargo." The entire blueprint came from a flagship FOSS project.
Rust's development used a structured, community RFC process—endless planning by your definition. The result was a famously well-designed toolchain that the entire community praises. FOSS didn't hold it back; it made it good.
So no, commercial backing isn't the only way to ship something good. FOSS is more than capable to ship great software when done right.