31–40 of 84 posts
(1999) - "Spending on Amazon warehouses Is at Epic Levels. Will It Ever Pay Off?"
I'd suggest a better analogy would be telecommunications fiber[1]. [1] https://internethistory.org/wp-content/uploads/2020/01/OSA_B...
Even the smallest and poorest countries in the world invested in their fiber networks.
Only China and the US have money to create models.
I look forward to the cheap compute flooding the market when the music stops.
Are we still getting AGI in 2026, per OpenAI? Based on AGI 2026, they convinced US Government to block high-end GPU sales to China. They said, we only needed 1-2 more years to hold them off. Then AGI and OpenAI/US rules the world. Is this still the plan? /s If AGI does not materialize in 2026, I think there might be trouble, as China develops alternative GPUs and NVIDIA loses that market.
(1999) - "Spending on Amazon warehouses Is at Epic Levels. Will It Ever Pay Off?"
Are we still getting AGI in 2026, per OpenAI? Based on AGI 2026, they convinced US Government to block high-end GPU sales to China. They said, we only needed 1-2 more years to hold them off. Then AGI and OpenAI/US rules the world. Is this still the plan? /s If AGI does not materialize in 2026, I think there might be trouble, as China develops alternative GPUs and NVIDIA loses that market.
Altman says in a few years Chat GPT 8 will solve quantum physics
insert Rick & Morty "Show me what you got" gif here
Earlier quoted context omitted.
Amazon weren't spending a single digit percentage of GDP on GPUs with a shelf life measured in just a few years though.
but we collectively there was a single digit spend on things like fiber that ended up paying off for the public later
Earlier quoted context omitted.
How are you getting these results? Even with grounding in sources, careful context engineering and whatever technique comes to your mind we are just getting sloppy junk out of all models we have tried. The sketchy part is that LLMs are super good at faking confidence and expertise all while randomly injected subtle but critical hallucinations. This ruins basically all significant output. Double-checking and babysitti…
> Its not like there is zero benefit to it, but I am genuinely curious how you get consistently correct output for a "complicated subject matter like insurance". Most likely by trying to get a promotion or bonus now and getting the hell out of Dodge before anyone notices those subtle landmines left behind :-)
Just like tech debt, there's a time for rushing. And if you're really getting good results from LLMs, that's fabulous.
I don't have a final position on LLM's but it has only been two days since I worked with a colleague who definitely had no idea how to proceed when they were off the "happy path" of LLM use, so I'm sure there are plenty of people getting left behind.
Earlier quoted context omitted.
How are you getting these results? Even with grounding in sources, careful context engineering and whatever technique comes to your mind we are just getting sloppy junk out of all models we have tried. The sketchy part is that LLMs are super good at faking confidence and expertise all while randomly injected subtle but critical hallucinations. This ruins basically all significant output. Double-checking and babysitti…
Could you give an example of a prompt?
It’s not like anyone is going in debt to pay for gpu’s though. So it’s probably ok. Now if banks start selling 30 year mortgages for gpu’s, I might get a little worried.
People act like big tech didn't have a mountain of cash they didn't know what to do with. Each of the big players has around 100 billion that just sitting there doing nothing.
Earlier quoted context omitted.
I'd suggest a better analogy would be telecommunications fiber[1]. [1] https://internethistory.org/wp-content/uploads/2020/01/OSA_B...
Is not similar at all. Even the smallest and poorest countries in the world invested in their fiber networks. Only China and the US have money to create models.
Few companies and very few countries have the bleeding edge frontier capabilities. A few more have "good enough to be useful in some niches" capabilities. The rest of the world has to get and use what they make - or do without, which isn't a real option.