Why are yours and most other predictions for take-off timelines years long? My personal median for take-off via RSI (from a reliable auto-coder to ASI) is a single day. The primary reasons I expect it to be faster:
The potential for intelligence in modern hardware is extreme: modern computers can reach over 3 GHz, whereas humans neurons can’t fire more than 1000 times per second. With enough processing cores and similar strategies for reasoning an AI could potentially think 6 OOMs faster than a human (assuming there are no other major bottlenecks for reasoning), that is a year in 30 seconds. An AGI that can think like Einstein at this speed would effectively be an ASI.
In communication speed computers are even faster: an ethernet cable can transmit over 1 Gbps between common computers, while humans can’t even reach 100 bps talking or typing.
Even if creating the ASI mentioned in 1 is hard, getting halfway there shouldn’t be as hard. For example, current LLMs make high dimensional vector for each token before even doing anything, and even trivial thoughts need to pass through all layers before outputting anything. Both of those might have faster alternatives.
Current LLMs are several times faster than humans in most tasks they can peform reliably. If the initial AGI at the start of the RSI cycle is about 100x faster than humans in most domains, it could do an entire month of human work in 10 hours.
AGI will likely find easy speedups just by looking at its own code and analyzing available data. Similar to how GPT-5.6 Sol model autonomously enhanced its own production GPU kernel efficiency. Early RSI could be just about improving the model’s own framework, harness and creating a good document about how to think efficiently and summarizing current knowledge.
They will likely reach breakthroughs eventually, after that we should already be expecting over x1000 speed of a human with intelligence similar to Euler, after this they probably could make a lot of improvements just by reasoning about metacognition.
While these arguments could justify a month or shorter take-off, my single-day prediction assumes that something will accelerate the RSI even more (for example, the reading and analysis rate of the initial AGI could be x1000 faster than human, or it might uncover a great breakthrough early on by analyzing previous ML experiments). This take-off scenario would only be possible if pure reasoning is sufficient for drastic improvements and compute is not as necessary for RSI as it is in the AI futures model. Is compute bottleneck the primary reason for those years long predictions for AC to AGI or are there other reasons you think RSI will be slower than a few months? Which assumptions and arguments in this comment sound wrong to you?
Why are yours and most other predictions for take-off timelines years long? My personal median for take-off via RSI (from a reliable auto-coder to ASI) is a single day. The primary reasons I expect it to be faster:
The potential for intelligence in modern hardware is extreme: modern computers can reach over 3 GHz, whereas humans neurons can’t fire more than 1000 times per second. With enough processing cores and similar strategies for reasoning an AI could potentially think 6 OOMs faster than a human (assuming there are no other major bottlenecks for reasoning), that is a year in 30 seconds. An AGI that can think like Einstein at this speed would effectively be an ASI.
In communication speed computers are even faster: an ethernet cable can transmit over 1 Gbps between common computers, while humans can’t even reach 100 bps talking or typing.
Even if creating the ASI mentioned in 1 is hard, getting halfway there shouldn’t be as hard. For example, current LLMs make high dimensional vector for each token before even doing anything, and even trivial thoughts need to pass through all layers before outputting anything. Both of those might have faster alternatives.
Current LLMs are several times faster than humans in most tasks they can peform reliably. If the initial AGI at the start of the RSI cycle is about 100x faster than humans in most domains, it could do an entire month of human work in 10 hours.
AGI will likely find easy speedups just by looking at its own code and analyzing available data. Similar to how GPT-5.6 Sol model autonomously enhanced its own production GPU kernel efficiency. Early RSI could be just about improving the model’s own framework, harness and creating a good document about how to think efficiently and summarizing current knowledge.
They will likely reach breakthroughs eventually, after that we should already be expecting over x1000 speed of a human with intelligence similar to Euler, after this they probably could make a lot of improvements just by reasoning about metacognition.
While these arguments could justify a month or shorter take-off, my single-day prediction assumes that something will accelerate the RSI even more (for example, the reading and analysis rate of the initial AGI could be x1000 faster than human, or it might uncover a great breakthrough early on by analyzing previous ML experiments). This take-off scenario would only be possible if pure reasoning is sufficient for drastic improvements and compute is not as necessary for RSI as it is in the AI futures model. Is compute bottleneck the primary reason for those years long predictions for AC to AGI or are there other reasons you think RSI will be slower than a few months? Which assumptions and arguments in this comment sound wrong to you?