Creative Technologist building Archive Signal — rights-cleared, outcome-linked physical data. The answer keys AI trains against.
British Columbia
Joined July 2009
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Affiliate style networks are going to lose this industry.
What's being sold here isn't a widget. It's a person's life's work. Twenty years of records. The thing they built instead of weekends. It's sensitive, it's personal, and for a lot of owners it's their retirement.
And the industry's answer is a funnel. An intake form. A screening call with an AI broker. A fit quiz. Thresholds.
Nobody wants to explain their life's work to a funnel.
Nobody wants to feel like a sheep herded into a pen, tagged and numbered.
I can't even send my own referrers through these systems. These are people I know, sending me owners they know. I'm not feeding a friend's life's work into a form so it can be scored by someone who will never learn his name.
This work is white glove or it doesn't happen. One human who picks up the phone. Who learns the business. Who takes the time. Who treats the owner like the asset is theirs, because it is.
The labs think the scarce thing is data. It isn't. It's an owner who trusts someone enough to say yes.
The best archives never fill out a form.
The AI industry's PACs spent tens of millions on the midterms and barely said the word AI in their own ads. Too toxic. I spend the first five minutes of every business call doing what their money couldn't: getting one owner past the fear to the actual economics.
Education is the job now."
The AI data market isn't ending. It's graduating.
The frontier labs aren't done buying data. They're done buying more of the categories they've already ingested. What comes next is narrower, deeper, outcome-linked, and the terms will matter more than the first price.
archivesignal.ca/insights/th…
The frontier labs aren't done buying data. They're done buying your category.
Behind them, a second wave of labs has to acquire everything the first wave already ingested just to catch up.
The market isn't ending. It's graduating.
A startup called Dream raised $17m after training AI on records covering 100m+ square feet of permitted buildings.
Pretty interesting use of municipal archives. I'm even more curious about the engineering revisions, failed inspections and field changes that never made it into the final approved drawings.
They thought the explosion waited on understanding the brain.
It may actually be waiting on the records of what brains did while they worked.
One reason I am sanguine about the technological singularity is that I have seen it coming for over 50 years. In 1975, when I was 17, I wrote:
"Before man can most effectively aid his problem solving machinery, he must first understand it. At present, his understanding is very poor. However, when the breakthrough in understanding the brain occurs, improvement will come very fast and in an exponential increase. Once man has created an intelligence greater than his own, that intelligence should be able to create an intelligence greater than itself, as man whose intelligence is less than its was able to create its intelligence. Thus, the level of intelligence will shoot up with incredible rapidity, probably within the space of years. Within a few years of a breakthrough, which may occur any time now, man will either have been left behind by a superior intelligence of his own making, which he can no longer understand, or he will have become that intelligence and will no longer be like anything imaginable today."
The lab is a customer. The owner is the client.
Most of the data market has those two backwards, and you can tell from the paper they put in front of owners.
I work for the business owners.
It's their life's work.
Drew Clement-Archive Signal retweeted
Are the companies selling data to AI labs actually good businesses?
This article from @athomasq helped build my intuition for why traditional data businesses like Bloomberg are so good. one of the core ideas is that building the data asset is slow and expensive up front, and below a certain size the data isn't even useful. But most of that cost is fixed, and serving one more customer costs very little.
Bloomberg spends a ton collecting market data and then sells that same data to more than 300k terminal subscribers paying roughly $30k a year each. So each new customer spreads the cost and the economics get better as you scale. Once the corpus gets big enough, it's really hard for anyone to replicate ("my capex is your barrier to entry"), it keeps improving through data flywheels, and customers almost never churn.
The thing is, that model doesn't really hold for a lot of the companies selling RL data to labs. Labs usually ask for exclusivity on the data. So if a lab wants expert data from doctors, the data company pays the doctors to create it, but can only sell it to that one lab. (Note: The environments can often be reused across labs)
So instead of an asset that keeps paying off like Bloomberg's data, these companies end up looking more like a services business that collects a margin on each project.
Robotics might be different. I've heard exclusivity is less of a thing there, so robotics data collectors might actually be able to build a corpus and look more like a traditional data business. But unlike the LLM labs, most of the companies buying robotics data don't have robots generating real revenue yet. So the data companies are really betting on the data working and their customers building useful robots and earning revenue so they keep buying.
To be clear, I'm not saying the companies selling data to labs are bad businesses. The evidence on the ground is that they're highly valued and make a lot of money. The point is that they're a unique shape of business that doesn't cleanly map onto the traditional data business model.
Wrote about this and more, a few years ago:
pivotal.substack.com/p/econo…
(might write an update specifically around training data aggregators and vendors)
Drew Clement-Archive Signal retweeted
if you’re an employee of any corporation, in the medium & long term you are effectively generating training data for an ai that will likely replace you.
even if the company fails, your work will still create value as training data.
i know a lot of ppl find this bleak & possibly unpleasant but it’s the truth. & it’s kinda cool to think about. humans were never meant to do jobs that could be automated (we don’t use plow’s anymore). eventually we should all be creating our ai replacements in some sense so that we could live the way we were meant to.
Let the tide go out a little. Let the air clear.
The doom is just affiliate promises coming due: referral-fee fairy tales dressed up as a market. Underneath it, the real market never blinked. Capital is arriving in size, buyers are getting serious, and the records of dead companies are getting bid on.
Tides don't lie. They just show you who was swimming without shorts
An 11-person UK robotics startup just raised $40m claiming it can teach robots new tasks from a single demonstration.
The model was trained entirely in simulation.
I'm building a business around real-world data, so this is exactly the sort of thing I need to pay attention to. If it scales, the economics of collecting millions of ordinary demonstrations change considerably.
Every lens here is long something perishable against a fixed obligation. The GPU-hour expires, the seat flies empty, the cargo reprices. The one input that doesn't decay while the commitments compound is the record of real work.
Data is the only shelf-stable asset in the stack
The durable position may not be owning a static pile of data. It may be the relationships, rights and consent behind a continuing supply of useful records.
archivesignal.ca/insights/co…
Tasks are the part you can generate. Traces are the part you had to be there for.
The rejection-rate test is what keeps this market honest. An environment built on thin traces can't fake lift, so the premium migrates to whoever captured the real thing, with the outcome still attached.
Capture is the moat because time only runs one direction.
Forget the ransom demands. What happens when the data itself is worth more than the ransom?
Years of proprietary operational records, customer interactions, and real-world outcomes sitting on vulnerable systems.
AI is making that data more valuable. And a much bigger target.
Data ownership is becoming an asset protection problem.
#Cybersecurity #AI #DataOwnership #ArchiveSignal
The number underneath the number: 81 minutes a second is roughly 800,000 hours a week, and at $8.1M that's about $10 an hour, right where wholesale data clears. The open question is the mix. How much of that is robots watching robots, and how much is human work with an outcome attached?
The second kind is the kind you can't print.
Drew Clement-Archive Signal retweeted
The next frontier of robotics is tactile.
Models need to be able to develop proprioception and an internal representation of the world around them from touch alone.
The part that rings true isn't the retreat, it's the repricing. When anything can be generated, the scarce thing becomes proof a human made it. The records that were never on the open web, the ones sitting inside businesses and archives, quietly become the most trusted data left.
Closed web or open web, verified human history survives both.
As the internet dies the inevitable future I see is a closed web…
Imagine social media reverts to 2005 era Facebook/AIM…closed loop networks of friends/family only…in person, human verified connections
Websites become outbound info only again rather than places you typically input your personal data.
People rely on human verified information…think 1999 Encarta Encyclopedia cirucilating on hard disks
Paywalls everywhere to keep bots & AI scraping at bay
Cloud data storage dies…data hosting becomes hyper localized on home/business storage.
Laws get put in place preventing companies from storing any data on external facing servers…people will own their own data and choose what to push outbound.
Undersea cables & data centers will be attack vectors in future wars. Networks will become a lot smaller and regionalized
Some will give up and revert to physical media (or digital copies completely cut off from connected networks)