What people don't realize is that if AI works it grows GDP like population growth, but since there are no new humans it also grows GDP per capita by an equal amount. This is why this time is different.
Economically productive AI must accumulate knowledge like a human during work. Even the best hire in the world is fairly useless on their first day, which is roughly where we're at with AI.
If AI works it will grow the economy like population growth not productivity growth. The days of 2.5% annual GDP growth will be over and a sci fi universe will be real within our lifetime.
The best part about this rotation from software into industrialization
Is that venture capital dollars and companies will finally be transforming the physical world
The 2010s deployed billions of dollars, but if you stepped out on the street, the world looked the exact same...
@IterIntellectus Harnessing even a millionth of the Sun’s power, which is extremely difficult, results in an economic value far more than a million times than that of Earth’s current entire economy
many scientists think humans are nothing more than meat machines. AI is here to show us what a machine really is. you will realize that we are not a machine. we are so much more
We’ve agreed to a partnership with @SpaceX that will substantially increase our compute capacity.
This, along with our other recent compute deals, means that we’ve been able to increase our usage limits for Claude Code and the Claude API.
most people think ideas come from:
- insight
- intelligence
- taste
- reading
- vibes
but in practice they actually come from:
- building the wrong thing
- hitting a constraint
- getting embarrassed by users
- realizing the obvious thing you missed
- noticing the second order effect you couldn’t see from the couch
a really great idea is the *output* of the work, not the input.
256 Tb/s data rates over 200 km distance have been demonstrated on single mode fiber optic, which works out to 32 GB of data in flight, “stored” in the fiber, with 32 TB/s bandwidth. Neural network inference and training can have deterministic weight reference patterns, so it is amusing to consider a system with no DRAM, and weights continuously streamed into an L2 cache by a recycling fiber loop. The modern equivalent of the ancient mercury echo tube memories. You would need to pipeline a bunch of them to implement modern trillion parameter models, but fiber transmission may have a better growth trajectory than DRAM does today, so it might someday become viable.
Much more practically, you should be able to gang cheap flash memory together to provide almost any read bandwidth you require, as long as it is done a page at a time and pipelined well ahead. That should be viable for inference serving today if flash and accelerator vendors could agree on a high speed interface.