Rule changes for the SpaceX $SPCX IPO:
Index providers waived the profitability requirement and cut the seasoning window from 90 days to 5.
This forces over $30 trillion in passive 401k and retirement money to buy SpaceX at IPO valuations.
Bloomberg Intelligence estimates S&P 500 funds must absorb 19% of SpaceX's float within 6 months.
Russell 1000 and Nasdaq 100 funds will absorb 24%.
The rules built to protect passive investors:
1. S&P 500 has required 12 months of trading and 4 quarters of GAAP profitability since 2002. Both waived.
2. Nasdaq cut its inclusion window from 90 trading days to 15.
3. FTSE Russell cut its to 5.
All three benchmarks are now structured to buy SpaceX at IPO pricing.
Terence Tao - "AI tools are like taking a helicopter to drop you off at the site. You miss all the benefits of the journey itself. You just get right to the destination, which actually was only just a part of the value of solving these problems."
Judit Polgar - "I always felt that intuition is very important in chess, but I get my intuition through my experience. And many times I think that this is the biggest danger for youth, that they don't have the experience because they don't spend enough time doing."
Elites from two different fields voice the same opinion.
[1] https://t.co/XRDSSPjpQ8
[2] https://t.co/fQzPT3D3f4
my quant friend handed me a python script last week
he said: "run this during the real madrid match. it beats the broadcast by 12 seconds"
i tested it. while i was watching the game on my screen, the bot had already bought YES shares on madrid's win
valverde scored the 3-2 winner. i saw it live. the bot had already exited the position
that's not a prediction edge. that's a structural gap in how polymarket prices live events
webRTC streams run 5-8 seconds behind real events
UMA oracle needs another 6-10 seconds to verify and push on-chain
by the time you see the penalty on your screen, the ref pointed to the spot 12 seconds ago
the bot doesn't watch TV. it reads raw stadium APIs - player GPS fatigue, pressing vectors, xG updating every 2 seconds
one calculation runs when an event gets flagged: edge = true probability minus current polymarket price
if edge clears 5%, a signed transaction hits the polygon mempool before the UI even flickers
50 gwei gas, private RPC, zero public node lag
he used claude to write the entire EVM execution layer over a weekend
what used to take C++ engineers months - low-level contract interaction, web3 signing logic, websocket parsing - claude compressed into a clean python pipeline
you aren't trading against smart money on polymarket
you're trading against an 8-second database and retail staring at a broadcast
he just broke down the full architecture below
Peter: they are not PhD level in physics. You trail behind a model picking up all it breaks.
This is a bleeding edge malfunctioning military grade research project weirdly marketed direct to consumer to fund R&D at the top of mad hype cycle that’s likely *directionally* correct.
Full video of my discussion on e/acc + d/acc w/ @VitalikButerin
We talk about the future of intelligence, the economy, and life itself
s/o to @eddylazzarin + @shawmakesmagic for co-hosting
The CEO of a $3 trillion company just admitted the biggest threat to AI has nothing to do with the technology itself.
It is YOU.
Satya Nadella spoke at Davos and said the real obstacle to AI is getting people to actually change how they work.
He gave a personal example.
Before Davos, his team would spend days preparing briefing notes, filtering up through layers of staff before reaching him.
That process had not changed since he joined Microsoft in 1992.
Now he types one sentence into Copilot and gets a full 360-degree brief in seconds what Microsoft is doing for a client, what that client is doing for Microsoft, the whole picture at once.
Nadella said that kind of capability does not just speed things up, it completely inverts how information flows through an entire organization.
The old model, departments hoarding knowledge, information trickling upward through hierarchy, is now structurally obsolete.
Most companies have not figured that out yet.
He said firms will see almost zero productivity gains from AI unless leaders actively redesign their structures, retrain their people, and rebuild how context moves through the organization.
The companies that refuse to change will not just fall behind and they will become irrelevant to the ones that do.
His exact words: "That's why you're going to see the challenge of why am I not seeing immediate results in productivity. You have to do the hard work."
The hard work is convincing an entire workforce to let go of how they have operated for decades.
That is the actual AI race and most companies are losing it before it even starts.
With all the noise around AI, I hope this Report provides policymakers, researchers, and the public with the reliable evidence they need to make more informed choices about how to develop and deploy this critical technology.
This year, we also have a ~20-page “Extended Summary for Policymakers” to make our key findings more accessible.
(16/17)
https://t.co/KdWQGYNZJJ
@ylecun@demishassabis Specialization is more energy efficient than generalization.
Every bee is doing something,
with movements short and long.
The key to making honey is
to do work where you're strong.
@EmanuelDerman@gsignoret Additional thought is that not only many futures but also different views on the past. Depending on the mood and memory. For instance, two types of happiness by Kahneman
Like ‘this picture better than thousands words’ style