G10 FX, UK, US and European equities. Re-tweets/likes not always endorsements. Entirely opinion. No financial advice.
London
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LibCapital retweeted
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openmarket.xyz
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LibCapital retweeted
Antigravity with Gemini 3.8 Flash is really good at finding relevant PDFs on the web and organizing them into a corpus. Now I just need to 100x this. This skill of mine is helping to keep the costs down a lot, too:
jeffreys-skills.md/skills/bu…
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LibCapital retweeted
This is to my point about compression being inevitable as human knowledge is too small for what we are building. As well, humans are too redundant in their wants needs and questions.
AI-generated content will clearly contain propagation errors just like human history of knowledge does. Only LLMs will iterate those propagation errors infinitely faster with less ability to self- correct, for want of understanding.
This gets to Ballard’s test. LLMs cannot attain understanding (AGI) as understanding cannot exist unless reason first exists without language. A likely impossibility for a language model.
Research on this is already focused on getting around this in some way. Though many also have not yet conceded the point.
ChatGPT has now a big problem.
Researchers at Oxford and Cambridge exposed a massive threat to large language models.”
They call it “model collapse."
Internet ecosystem is rapidly changing, and generative AI will soon contribute much of the text found online. This forces us to consider what happens to future iterations like gpt-n when they are trained on data scraped from the web that was already generated by an llm.
According to the research, indiscriminately using model-generated content in training causes "irreversible defects" in the resulting ai. the model loses the "tails of the original content distribution." in other words, it begins to forget the creative, fringe, and unique nuances of actual human writing, collapsing into a repetitive echo chamber.
This isn't just a chatgpt issue.. the researchers built theoretical intuition showing this collapse is ubiquitous across learned generative models, occurring in large language models as well as in variational autoencoders and gaussian mixture models.
Tech companies rely on scraping the internet for large-scale data to build smarter models. However, the paper warns that if we want to sustain the benefits of training on web data, model collapse must be taken seriously.
Ultimate takeaway? data collected from genuine human interactions is going to become increasingly valuable in a web filled with ai content.
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LibCapital retweeted
Helpful list of all the recent rogue AI incidents from the WSJ.
It's getting hard to track them and will only get worse.
We like need to establish consistent naming or numbering conventions, e.g. OpenAI-May11June26-Collusion.
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LibCapital retweeted
Citadel have just shared the same 2026 stock market prediction as me.
> Now: AI sentiment has turned sharply negative and positioning has been reduced (since July, but rebuilt slightly in recent weeks).
> Through September: Supply/demand and seasonality still point to downside.
> Q4: Those same September resets begin to work in the other direction, first in Tech and potentially across the broader market into earnings and year-end.
Not sure if it's a good thing that I have the same viewpoint as Citadel lol.
But it's cool to see my thinking line-up with key market participants.
Prediction time.
Short term: stocks will be choppy and risk:reward will be mediocre.
After midterms: big rally until 2027.
I'm talking about the broader market here, not just AI/semis stocks.
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LibCapital retweeted
Cool chart from @SemiAnalysis_ showing that the frontier labs have now shifted most of their compute from pre-training to post-training (mostly RL).
Unlike pre-training, the bottleneck moves from compute to networking - how fast you read from memory/access CPU. An interesting implication is that you don't need to stack memory (DRAM dies) anymore as this doesn't improve bandwidth so we've gone from 12 dies ---> 8 dies ---> 4 dies?
So, I'm guessing companies that do scale up and scale in win here?
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LibCapital retweeted
New episode with @polynoamial
We talk about multi-agent, Navier-Stokes, and what the current explosion of maths progress tells us about what happens once you automate AI research.
And we also discuss how we will know if the models are actually aligned before we kick off RSI.
0:00:00 – Multi-agent and Navier-Stokes
0:15:28 – How will AI firms work?
0:22:02 – What math progress tells us about recursive self improvement
0:40:22 – Hugging Face and alignment
1:01:18 – The internal/external model gap
1:08:34 – Chain of thought is degrading
1:14:12 – How will we know when alignment is solved?
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LibCapital retweeted
Straits Taylor Rule:
i = r* + π* + 1.5(π−π*) + 0.5(y−y*) + α(SOH−SOH*) + β(BEM−BEM*), α,β > 0
Let’s see if a hike could open SOH or produce a single barrel :)
You can’t 25bp a chokepoint and r* isn’t neutral. It’s SOH risk premium, and We set it.
Stay unanchored !
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LibCapital retweeted
$INTC Lip-Bu Tan admits Intel can fill only half its CPU orders and apologizes when CEOs call him
"Yeah, I think inferencing is very important. When you want to drive some of this reinforced learning, and also in terms of agentic AI, CPU is the best."
"GPU is very good for training, but CPU is really good for orchestration, control plane, and driving some of this even single thread rather than multi-thread has become very useful."
"It happened that I invest quite a few companies in the frontier model. They all tell me that, 'Lip-Bu, we need more CPU.'"
"So good news is, right now, sometimes in life you need some help. And so the help that come to me is that CPU is so high demand, I only can provide 50% of what the customer want. So many CEO call me up, I had to apologize, I'm not manufacturing enough for them."
$NET Matthew Prince reveals running every knowledge worker's agent in containers would need 40x the world's CPUs
" Really the whole hyperscale world and all of the mobile world is built on this idea of containers."
"The problem with containers is, if you imagine every single knowledge worker on earth has one agent that's running for them, which seems, I mean, pretty conservative, I think most knowledge workers are going more than one agent, and you imagine those agents are all running in containers."
"The problem with containers is you have to import a whole operating system and tool chain, all this stuff, just to power that."
"Not even looking at GPUs, just looking at CPUs, you need 40x the number of CPUs that are produced in the world in order to just run the agents for those knowledge workers, which is, like, that's not going to work."
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LibCapital retweeted
The UK's bond market is collapsing.
Today, the yield on a 30Y Bond in the UK hit 5.95%, its highest level since March 1998.
Yields in the UK are now 15 TIMES above 2020 levels, with the highest borrowing costs among G7 countries.
What is happening? Let us explain.
(a thread)
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LibCapital retweeted
Do not use your energy to worry. Use your energy to believe, to create, to learn, to think and to grow.
—Professor Richard Feynman
BBC Archive
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LibCapital retweeted
stanley druckenmiller averaged 30% for 30 years without a single down year.
asked for a book recommendation, he picked a 4,000 year history of interest rates.
the price of time by edward chancellor.
others he's endorsed over the years:
- hedge fund market wizards by jack schwager
- more money than god by sebastian mallaby
- the intelligent investor by benjamin graham
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LibCapital retweeted
A heated exchange about the U.S.-Iran conflict this morning between CNBC hosts @joesquawk @andrewrsorkin.
When Sorkin seemed initially to put the U.S. and Iran in the same financial “dire straits,” Kernen put a fast stop to that. $97 oil is a problem, but Iran’s currency is collapsing and 1,000% inflation is catastrophic. Comparing financial headwinds for the U.S. and Iran is “ridiculous.”
Sorkin also contended the Trump administration had “lied repeatedly” and accused Kernan of accepting it all.
Kernen would have none of it: “I have hoped that it ends, and I have hope for a favorable outcome. I haven't, and I'm pointing at you taking glee in every setback for the Trump administration in prosecuting war.”
Worth the 2 minute watch.
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LibCapital retweeted
We're publishing our most detailed threat intelligence report to date.
It covers how people tried to misuse Claude—for cyberattacks, influence operations, surveillance, biology, and building weapons—and how we found and stopped them.
We disrupted every operation in the report, and used the lessons from them to strengthen our safeguards. Where appropriate, we also shared what we found with authorities and other AI companies.
These cases are not typical: we’re highlighting some of the most sophisticated misuse we’ve seen. But they’re especially important to discuss, because they show us where AI misuse is headed, where our safeguards work, and where they need to improve.
We’re publishing this report so others can spot the same activity on their own platforms, and so we can give the public a clearer view of how emerging threats develop.
Read the report: anthropic.com/threat-intelli…
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LibCapital retweeted
Broadcom CEO Hock Tan breaks down AI economics:
Open-weight models burn $100B compute to make $30B in revenue.
Frontier models spend $100B to make $120B in revenue.
One of these won't be sustainable
source: Goldman conference
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$STDN extremely tight borrow on 2.67m shares lent out.
One piece of good news will send this and there's a few expected soon.
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