Well, are you able? Do you know how? If you were invited to try, are you familiar with blockchain decentralized ledgers, digital wallets, profiles & identities?
Global based in Ottawa, Canada
Joined November 2021
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CUB retweeted
‘consensus intelligence’ - a new sociological concept?
"not proposed as a settled discovery, but as an invitation to study something that has long been difficult to observe directly in the process of a group of people moving from disagreement toward agreement.
...
Building on triple entry accounting’s third ledger, and on Satoshi Nakamoto’s demonstration that peer-to-peer electronic cash using digital ledgers could establish trust in value without a central authority, we ask whether a similar architecture, extended from money to meaning, can make the process of reaching agreement observable, auditable, and improvable, distinct from the snapshot results that polls and surveys have long provided."
votesmash.substack.com/p/con…
🤖 Made with AI
CUB retweeted
This is the trap:
19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good.
The long term commons is dying, bc nobody will spend the decades necessary to train these people. This isn't just AI - we saw the same thing happen with the failure of apprenticeship in the West.
If nobody invests in the future, you get a dumber future.
I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies.
My observations:
1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively.
2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now.
3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention.
4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is.
5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now.
6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text.
They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it.
7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy.
8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies.
Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here.
9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens.
10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity!
11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it.
12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable.
13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think.
14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data.
15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open.
16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down.
17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building?
18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone.
19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good.
20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped.
21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense.
Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era.
Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways.
Happy to be home and locked in.
Until next time!
CUB retweeted
🇨🇦 ON THIS DAY IN CANADIAN HISTORY
October 11, 1869 — The Day Louis Riel Stood His Ground
On this day, 157 years ago, a young Métis leader named Louis Riel helped set in motion a series of events that would forever change the map of Canada.
In the Red River Settlement, in what is now Manitoba, tensions were rising. The Canadian government was preparing to take control of Rupert’s Land from the Hudson’s Bay Company, but the Métis people who had lived and farmed along the Red and Assiniboine rivers for generations had not been properly consulted about their future.
Government surveyors had arrived and begun dividing the land into square townships, disregarding the traditional long, narrow river lots that formed the foundation of Métis farms and communities.
For the Métis, this was more than a disagreement over property lines. Their homes, livelihoods, language, culture, and political rights were at stake.
On October 11, 1869, Riel led a group of unarmed Métis horsemen to confront a government surveying crew working across their lands near present-day Winnipeg.
They ordered the surveyors to stop.
The crew withdrew.
No shots were fired, but the message was unmistakable: the people of Red River would not accept Canadian authority without negotiations and guarantees of their rights.
It was the first major public act of what became known as the Red River Resistance.
In the months that followed, Riel emerged as the leader of a provisional government that negotiated terms with Ottawa. Those negotiations helped produce the Manitoba Act, and on July 15, 1870, Manitoba entered Confederation as Canada’s fifth province.
Riel’s life would later become one of the most controversial chapters in Canadian history, culminating in his execution for treason in 1885.
But on this October day in 1869, he was a 24-year-old Métis leader standing with his people against a government survey.
A confrontation without gunfire helped change the course of Canadian history.
157 years later, Louis Riel remains one of the most significant figures in the story of Canada.
#CanadianHistory #LouisRiel #MétisHistory
This is symbolically important in the UKR war as President Zelensky's government continues to splinter from internal pressures.
MP Goncharenko was present during the Odesa Trade Union massacre in May 2014. He's now on a list of 'enemies of Ukraine' for seeking peace & negotiation with Russia to end the war. NATO, say what?
After being expelled from the European Solidarity Party, Ukrainian MP and delegation to the Parliamentary Assembly of the Council of Europe Oleksiy Honcharenko has been added to the controversial "Myrotvorets" list.
The Myrotvorets list is a database of individuals considered enemies of Ukraine, who they claim are working against Ukraine's national interests. It has been repeatedly criticised by human rights organisations as violating press freedoms and endangering journalists. Some critics have characterised Myrotvorets as a "kill list", and alleged it is funded by the Ukrainian SBU.
The website described Honcharenko as a "professional provocateur", and accused him of taking actions "in the interests of the Russian-fascist invaders, aimed at disrupting mobilisation events in Ukraine." It explicitly mentioned his recent advocation for peace negotiations and statements that Ukraine is losing the war.
Myrotvorets also listed a number of other "Russian narratives" Honcharenko allegedly spread in the past, including the following:
- Advocating for parents to be allowed to determine for themselves whether their children should study in Russian or Ukrainian in 2011;
- His statements in 2019 that Ukrainian culture is bilingual and that he does not wish for Russian to die out as a language in the future;
- His attempts to grant Russian the status of a second state language and expand its presence in school education in 2011, during his work in the work in the Odessa regional council.
One of the most difficult (for those who've been doing it) & important questions of our current moment of transition from the 'unipolar moment' that ended in 2001 to today's multipolarity: what are the real & lasting consequences of overextension?
CUB retweeted
Adam Back has long had a rarefied status in the Bitcoin world, but as the industry undergoes another major shakeout, a number of Bitcoin-related businesses linked to him are showing signs of increasing strain bloomberg.com/news/articles/…
CUB retweeted
"Sociology has always depended on how people participate in producing knowledge about themselves. That participation is now being reshaped by decentralized digital infrastructure in the current electronic-information era. This paper explores Web3 sociology as an emerging approach in which self-sovereign decentralized digital identity, participant-controlled data, programmable incentives, and verifiable records of contribution become part of sociology’s own methodological toolkit, beyond only a new subject matter to study. ... The paper closes by proposing Web3 sociology as a reflexive, evidence-based practice, one whose value will be established not by the technology itself but by whether it produces social knowledge and interaction people find more trustworthy, useful, just and insightful to build together."
votesmash.substack.com/p/web…
🤖 Made with AI
CUB retweeted
Meet the speakers for the Ottawa Web3 x Blockchain Summit 🇨🇦
Oct 1 · 5–7 PM · Hub350, Kanata, Ontario
CUB retweeted
Web3 Extension Services (W3ES) as a new framework for bridging decentralized digital technology with human-centralized learning, business and collaboration. ... The integration of triple entry accounting (TEA) and small-world network (SWN) theory provides structural and ethical foundations for engaging people with a level of trust, transparency, and scalable coordination in decentralized ecosystems. ... Together, these ideas propose a practical, culturally aware model for scaling digital literacy, decentralized governance, and contestable innovation through globally connected yet locally grounded small-world online and local activity networks.
votesmash.substack.com/p/enh…
🤖 Made with AI
IPv6 + Web3 + AI
IPv6 connectivity
→ vastly expands the addressable network of devices & entities
Web3 identity / assets / protocols
→ extends ownership, identity & agency into networked environments
AI agents
→ extend cognition, action & coordination
Blockchain
→ extends memory, verification, ownership & collective record-keeping
CUB retweeted
Oxford and Cambridge Researchers proved every LLM trained only on AI-generated content develops an irreversible disorder.
They call it "Model Collapse"
Researchers took a small language model and fed it its own output for nine generations. Each new model learned only from what the one before it wrote.
By generation nine the model had forgotten what it was talking about. Ask it about medieval church towers and it answers with a list of jackrabbits.
They called it model collapse. The name stuck.
The mechanic is simple. Every model slightly overproduces the common stuff and underproduces the rare stuff. Train the next one on that output and the rare stuff shrinks again. Do that enough times and the edges of reality disappear.
The rare stuff is where the useful things live. Minority languages, unusual cases, the outliers that make a model sharp instead of average.
Now look at what gets published online. Ahrefs checked 900,000 new web pages last year and found AI-generated text in 74 percent of them. Every scraper feeding the next training run picks that up.
The labs know this. That's why they're paying for human archives. News Corp alone got a reported 250 million dollars from OpenAI for five years of access. The paper called it a first mover advantage. Whoever trained before the flood holds an asset nobody can recreate.
The paper also found half a fix. When the researchers kept just 10 percent real human data in every generation, the damage dropped to minor. Real data isn't optional. It's the anchor.
So the thing being burned right now isn't compute or money. It's the supply of human writing that hasn't been through a model.
Every time someone posts machine output, the next model gets a little more average. That isn't a moral point. It's a measurement.
The paper is two years old. The experiment it describes is still running, and we're the training data.
CUB retweeted
A three-part series continuing the themes of human extension and digital extension (services) with Web3 sociology and consensus intelligence.
votesmash.substack.com/p/web…
🤖 Made with AI
CUB retweeted
MIT published a brutally honest report on what AI is doing to students.
A committee of professors and students spent five months studying how AI changed learning on campus, and the findings read like a warning to every university on the planet.
Study groups are disappearing. Office hours are emptying out. Problem sets and take-home exams no longer prove anything, because AI can produce credible solutions to almost any written assignment in the undergraduate curriculum. Students who lean on chatbots lose mastery and confidence, and some slip into what the report calls cognitive surrender, reaching for AI at the first hint of struggle.
The numbers are rough. 46 percent of surveyed MIT undergrads use LLMs daily. 90 percent worry about their own overreliance. Undergrads who feel AI makes them replaceable now outnumber those who feel it makes them capable.
The committee's answer surprised me. They refused to fight AI with surveillance. The report calls AI detectors unreliable, says lockdown browsers feel like spying, and warns that policing students builds a classroom atmosphere of mutual distrust.
Instead, MIT wants to rebuild education around the things AI can't replace. That means oral exams, semester portfolios, in-person project work, and a required social component in every subject. The report even floats the idea of rethinking grades entirely, since without a GPA to optimize, much of the incentive to cheat with AI evaporates.
The committee warns professors against replacing undergrad research assistants with AI agents just because they're cheaper, because a university exists to grow people, not output.
The most famous tech school on earth admitted the machines broke its way of teaching. Its answer is more humans, not more software.
"This is an absurdly large amount of compute."
By now, it's amply clear that the big bosses of US frontier AI labs are really, really scared of China.
And, fascinatingly, part of the answer why might be a tiny place in Inner Mongolia that you've probably never heard of: Ulanqab prefecture.
Heard of it? Didn't think so.
Even myself, who's traveled to Inner Mongolia twice, had never heard of it before researching this story.
This is because, at first glance, this place is pretty unremarkable. Check its Wikipedia page (en.wikipedia.org/wiki/Ulanqa…) and you’ll see the only thing it’s famous for is being the birthplace of He Pingping, who briefly held the Guinness record for world’s shortest man.
That’s it, that’s the list!
Otherwise, it's just a sparsely populated - 1.5 million people, minuscule by Chinese standards - stretch of windswept grassland on the Mongolian plateau.
Except there is one number that tells you there is more to this place that meets the eye: this tiny prefecture consumes nearly 1% of all of China's electricity - and that number is growing by double digit numbers every year.
And yes, I did write **consume**, not produce.
In fact, when you divide Ulanqab's electricity consumption by the number of households living there, each household in this remote corner of Inner Mongolia "consumes" about 105,000 kWh in electricity, roughly 10 times the U.S. average. Insane electricity consumption numbers.
So what are the Ulanqabese doing? Are they all running 10 American households' worth of appliances?
Here is another number that might clue you in on the answer: over the past few years, this unremarkable prefecture has quietly signed over half a trillion yuan (and, yes, that’s trillion, with a "T") worth of investment deals from all the major Chinese tech giants.
What is going on, you'll have guessed by now, is the most ambitious data center buildup anywhere on earth - and it's really surprising this hasn't been talked about more because the scale is beyond anything else, and by an immense margin.
Take Elon Musk's so-called "Colossus" datacenter in Memphis, Tennessee which he pitches as "the world’s largest AI supercomputer."
According to their own numbers (x.ai/colossus), Colossus has 200,000 chips, which, let's be clear, is already super impressive.
In datacenter lingo, this converts to roughly 5000-6000 "racks": you know, the fridge-sized cabinets full of blinking lights you see in every movie scene set in a server room.
How many racks are they building in Ulanqab? Over 5 million.
Yes, about one thousand times the scale of "Colossus" 🤯
I tripled-checked the number: it is indeed the official number published by authoritative sources such as "Science and Technology Daily", the official newspaper of China's Ministry of Science and Technology (stdaily.com/web/gdxw/2026-08…).
This is an absurdly large amount of compute.
What we see appearing in this Inner Mongolian steppe may be the closest thing to a world brain humanity has ever built - a place where a large share of the world's thinking will physically happen.
And - as we'll see - what makes this story so fascinating is not just the scale, but why it's getting built in this particular patch of Inner Mongolian grassland which combines a number of characteristics unlike anywhere else.
If you believe, as I do - and as both China and the US obviously do as well - that AI will be the defining technology of the 21st century, this makes Ulanqab one of the single most relevant geopolitical places in the world right now.
The full story - the Chinese government plan Ulanqab is part of, energy prices, chips and the strategic questions this raises - is here: arnaudbertrand.substack.com/…
CUB retweeted
“It is a kind of point of mythic awareness.
Myth is the instant awareness of the totality of a complex process.
Electricity compels us not only to think, but to live mythically.”
Marshall McLuhan
‘Humpty Dumpty, Automation, and TV’
1963
CUB retweeted
“The computer will become an immediate extension of language and corporate consciousness. It will become a poetic form of research which will harmonize all of the arts and sciences in a retuning of the sky.”
Marshall McLuhan
‘The University, Where it’s at.’
1970, unpublished
CUB retweeted
“So the computer, although it is sternly dedicated to the task of doing 19th century librarian work, has, in spite of itself, revealed the knowledge of mythic pattern, structure, and profile, all of which are quite excitedly loaded with discovery.”
Marshall McLuhan
1966
