Real-world asset tokenization is scaling rapidly on Provenance.
The Insights tab on @artemis provides some interesting data points to back it up.
Over the past quarter, Provenance has continued to accelerate across both TVL and lending activity, with daily lending loan balances recently reaching an all-time high of $19.18B 📊
Finally, a big name has the courage to tell it: we are nowhere near AGI.
Demis Hassabis, CEO of Google DeepMind and Nobel laureate for AlphaFold, put it neat and clear:
"Today's systems are nowhere near [AGI]. Doesn't matter how many Erdős problems you solve… I think it's far, far from what a true invention, or someone like Ramanujan, would have been able to do."
This is the elephant in the room that many AI enthusiasts prefer not to see, or are actively trying to hide.
Erdős problems are well defined, often combinatorial, on finite spaces. They are exactly the kind of problems on which current AI can achieve spectacular performance with a lot of compute and knowledge.
A neural network can search a huge graph of possibilities. It can recombine existing knowledge at unprecedented scale. It can discover surprising solutions inside an already defined conceptual space.
But true invention is something else.
True invention is not only solving a problem.
It is inventing new objects, new dimensions, new connections. It is inventing new problems.
From resolving to inventing there is a discontinuity that we don't know how to bridge.
We are making extraordinary tools.
But we are nowhere close to AGI.
Huge congratulations to @adcock_brett and the entire @hark_labs team on raising $700M at a $6B valuation.
What Brett and the team are building is truly incredible — a company that's thinking deeply about what people actually need from technology, not just what's possible to build.
So proud of our partners at @PrimeMoversLab for being part of this round. Big things ahead! 🙌🔥😎
The Venture Capital Investment of the Decade: Cerebras
"Foundation (@vassallo) did real venture, not just capital deployment.
They found the founder early, built the relationship and incubated the company from scratch.
It was not obvious, not trendy and required conviction before the market saw it.
That is what defines a true investment of the decade." @jasonlk
שורד השבי רום ברסלבסקי התראיין לפוקס ניוז וחשף שם לראשונה פרטים מזעזעים מהשבי שעבר בידי הג'יהאד האיסלאמי.
הוא שיתף על הרעב הכבד- שהעביר ימים שלמים עם חצי פיתה, קצת גבינה ועגבניה רקובה, ובקבוק מים קטן. הוא הוחזק בבידוד מוחלט ובאפילה ללא אור יום במשך חודשים ארוכים.
הוא מספר על מחבל ספציפי שנהנה להתעלל בו ולהשפיל אותו, למשל לירוק לו על האוכל המועט שהיה מקבל, לקשור אותו בלי סיבה, להרביץ לו בכל הזדמנות... עד שפעם אחת רום לא היה מוכן לספוג את זה יותר ופשוט השתמש בכל הכוח שנשאר בו כדי לתקוף את המחבל הזה בחזרה וככה הצליח לגבור עליו במשך 3-4 דקות עד שמחבל נוסף הגיע לשם והצליח להשתלט עליו. רום שלח את אותו מחבל מתעלל לבית חולים, אבל מה שקרה אחרי זה- זה מעגל אלימות אינסופי שרום עבר בעקבות כך, כולל עינויים קשים והתעללות מינית, עד היום יש על גופו צלקות. הם הפסיקו רק כשאיבד את הכרתו והיה על סף מוות קליני.
וככה זה חזר על עצמו גם בהמשך, גיהנום של 738 ימים עד שקיבל את החופש שלו בחזרה... ויודעים מה?
פשוט תשמעו את המסר של רום בסרטון המצורף- ככה הוא סיים את הראיון הזה וזה אומר הכל על כמה הבן אדם הזה מטורף וגיבור ואיזו רוח ישראלית בלתי מנוצחת יש בו.
רום אח שלי חביב אלבי אני גאה לקרוא לך חבר. כמה טוב שאתה כאן איתנו, אתה ניצחת בענק! והמחבלים הארורים האלה או שנשלחו כבר לגיהנום או שהם בדרך לשם, ואתה כאן תמשיך להתחזק ולהשתקם, ותמשיך לנצח אותם בכל יום מחדש!
עם ישראל אוהב אותך🇮🇱💙
Why Larry Page said he’d leave his money to @elonmusk Elon Musk if he got hit by a bus
In this panel with Elon Musk, venture capitalist Steve Jurvetson tells a story of Google cofounder Larry Page saying he should leave all of his money to Elon Musk:
“I could give my money to a nonprofit and a lot less would get done than a corporation that’s pursuing things that are directly aligned with things I care about, like getting off of oil and colonizing other planets.”
Page believes in those missions and thinks that “a corporation endowed with the right to do that as its business purpose is the best vehicle out there.”
Jurvetson contrasts this with the approach of Bill Gates who spent the first half of his life building a gigantic for-profit company and the second half working with non-profits.
A “purpose-driven business” could offer the best of both worlds.
In fact, Jurvetson shares that the best-performing startups in his venture portfolio often have compelling missions. And it aligns well with Sam Altman’s advice that it’s easier to start a hard company than an easy company:
“The most precious commodity in the startup ecosystem right now is talented people, and for the most part, talented people want to work on something they find meaningful… An easy startup is a headwind; a hard startup is a tailwind. If people care about your success because you seem committed to doing something significant, it’s a background force helping you with hiring, advice, partnerships, fundraising, etc.”
Video source: @StanfordGSB (2013)
Few things in the universe are as powerful and lethal ballyhoo deflators as double-exponentials. Some of you are about to be introduced to it in very direct ways.
No, in the sense we haven’t yet computed everything that is capable of being computed to a certain level of computational depth for a given level of energy.
But, yes, in the sense that we are a lot closer to that wall than most realise. The “island” of what we can solve is tiny and infinitesimally small compared to what is irreducible and completely inaccessible to machines.
We just keep riffing on the same highly correlated computational structures. And neural nets are just very good trawlers for patterns — spurious or otherwise.
I got a DM from a CEO yesterday who charges customers $5,000 for onboarding.
Not as a fee.. but as a deposit.
If the customer (ICP is SMB $1-5M in revenue) completes onboarding within 35 days, they get every dollar back.
If they don't, the company keeps it and uses it to fund a dedicated onboarding specialist to finish the job for them.
(don't worry they call the onboarding specialist a forward deployed engineer)
It sounded a bit crazy at first, but the results are pretty interesting.
Before the deposit, their average onboarding took 67 days with 10% churn during the process.
After, it dropped to 31 days, and 3.5% churn.
The deposit didn't just speed things up..... it filtered for customers who were serious about implementing, which is a leading indicator of long-term retention.
This year so far, 98% of customers have completed onboarding within a 30 days.
Is anyone else doing something like this with setup fees for onboarding?
The singularity won’t be one moment. It’ll be a thousand small ones, each time AI does something we said “only humans can do.”
We crossed that line years ago. Most people just haven’t noticed yet.
Maybe Super Intelligence but my bet is that AGI is a long ways off, if even possible. And I believe that because of spending lots of time with this brilliant human @renegadesilicon. Strongly suggest following him.
Pioneer of causal AI, Judea Pearl, argues that no amount of scaling will get LLMs to AGI.
He believes current large language models face fundamental mathematical limitations that can't be solved by making them bigger.
"There are certain limitations, mathematical limitation that are not crossable by scaling up."
His core argument: LLMs don't learn how the world works. They learn from *human interpretations* of how the world works.
"What LLM's doing right now is they summarize world models authored by people like you and me available on the web and they do some sort of mysterious summary of it, rather than discovering those world models directly from the data."
He illustrates this with healthcare data.
When hospitals collect data on treatment effects, that raw data never reaches the LLMs.
Instead, the models consume doctors' written interpretations. Analyses shaped by people who already have a mental model of how disease and treatment work.
In other words, LLMs are learning from the map, not the territory.
The missing piece, according to Pearl, is causal reasoning — the ability to understand not just *what* happens, but *why*.
And he's clear this isn't a gap that more parameters or training data will close.
It raises a uncomfortable question...
If AGI requires machines that build their own world models from raw data rather than summarising ours, are we even on the right road?
So interesting @renegadesilicon. Always love your unique insights on this stuff. Big warnings about the risks around AI to humanity in a way that I think very few people are thinking/talking about.
I have a chuckle at the hubris of the “permanent underclass” warnings.
The younger and more intelligent you are, the more likely you will use an LLM. But that LLM will, with thermodynamic certainty, drive you more and more to the 50th percentile.
You wont even notice it. Worse, you will permanently **and irreversibly** lose the capacity to notice. You’ll just become more and more reliant on trading depth for speed. And then you’ll be hollowed out. Your capacity for anything irreducible and ineffable — gone.
All while you mocked the “idiots” not joining the bandwagon.
XRisk isn’t the terminator; it’s the annihilation of the right tail of human intelligence. And all our alignment efforts have had the exact opposite intended effect