@pvncher@najibninaba just preordered xreal aura to develop with codex running android debug commands — do you think I’d have a better DX with steam frame?
I have a preference for AR over VR for my use cases
@dumdumfucktard@unusual_whales Also same guy who made the space ship boosters completely reusable. And is mapping speech neurons to speakers for ALS patients
BREAKING: Total consumer credit rose +$8.3 billion in August, to a record $5.19 trillion.
This marks the 14th consecutive monthly increase, totalling +$154 billion.
Since the start of 2022, consumer credit has risen +$684 billion.
Non-revolving credit, covering auto and student loans, rose +$13.1 billion in August, to $3.84 trillion, an all-time high.
Meanwhile, revolving credit, which includes credit cards, fell -$4.8 billion, to $1.35 trillion, its 4th-highest level on record.
US consumers are still borrowing.
@gosuprime022@zerohedge You underestimate the strength of the unreleased models.
I think models capable of solving millennium math problems justifies billions in debt/funding.
Apply it to biology. The bottleneck becomes human regulatory processes not intelligence.
Demand for intelligence is BIG
AI enabled drug discovery names selling off while power scarcity & grid load are jumping — rotation?
“drug dev”: $TWST $TEM $ILMN $NTRA
“power grid”: $EME $STRL $PWR $GEV
Ben Horowitz on what he expects in AI over the next 8 to 12 months.
He thinks the infrastructure for AI is wrong, and it reminds him of the early days of cloud computing. Back then, everyone thought they could put their existing software on the other side of the wire. It didn't work, and everything got rebuilt: storage, networking, operating systems, virtualization, containers.
This is why he expects the same with AI:
1. The workload. A database query is predictable. An AI query could run for a week, and the systems to manage those requests don't exist yet.
2. The chips. Nvidia's GPUs were built for video games. Ben calls them power-hungry and low-yield for what they do today.
3. The models. Training them means sucking down all the data on the internet, and he doubts that is where we end up.
His conclusion: from the model on down, everything is going to change, and there will be a lot of opportunity in that.
Full breakdown here: https://t.co/OvoWHuVAHB
Where does money invested into the AI buildout actually go?
For every $100 flowing into the supply chain:
- $50 to chips
- $20 to power
- $15 to networking
- $15 to cooling, buildings, and land
More charts in State of Markets II: https://t.co/MTaxKUxa2w
You should work backwards from the assumption that robotics will be solved within a decade and material abundance and goods deflation fast follow.
You should then model how you want to spend your discretionary dollars between now and then based on what is likely to become scarce and what is about to plummet in value.
Take that vacation. Rent that hooker. Visit the circus.
🫡