
Founder @StateChangeAI. Youtube: https://xitter.cf/t.co/6PBjwzkS4q Statechange: https://xitter.cf/t.co/pxiIlMM5TD.
East Burke, VT
Joined July 2012
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Frontier AI models are becoming interchangeable gears in a larger machine — the practical move is choosing the right model-plus-harness fit per job, not loyalty to one model, since new frontier leaders keep displacing the last favorite.
Full conversation: youtu.be/p6PijF6Vci8
“The token efficiency is much better — token cost seems to be lower because it's more efficient.”
Full conversation: youtu.be/p6PijF6Vci8
“It's actually the thing that surprised me the most about it is how fast it is.”
Full conversation: youtu.be/p6PijF6Vci8
Where does value survive when AI models get commoditized?
Specialist data, regulated context, or proprietary process — otherwise you become an invisible extension of someone else's prompt.
Full conversation: youtu.be/esvm0CLeT0s
“Unreal Engine creates bigger unlocks when working with models, which then increases the value of Unreal Engine as more people are able to make more extraordinary stuff with a whole lot less labor.”
Full conversation: youtu.be/esvm0CLeT0s
“The differential for me comes from specialist knowledge or regulatory environment — where it's not as easy to just do a straight substitution.”
Full conversation: youtu.be/esvm0CLeT0s
AI subscription pricing will pass through three phases before it settles: generous today, expensive and metered next, then cheap once infrastructure matures — the same arc that took phone plans from per-minute billing to unlimited data.
Full conversation: youtu.be/KuBmIm4QMyE
40 billable hours x 50 weeks is a way to think about billing. Make the hours more valuable: feed delivery with study and selling, then bring expertise and trust to the work. The client can get a better deal while you can capture a higher rate.
Ray Deck @ statechange.ai retweeted
one thing I'm picking up from the MIT AI conference. Robot hands might be solved in 2-3 quarters, and that's going to be a huge deal
I’m generally bullish on Ai, but this analysis sounds like the early read on CDOs prior to 2008, Eg “The equity tranche is supposed to be this volatile and the more senior tiers are protected”
CoreWeave's debt, explained simply, because I like to speak plainly:
Secured debt = the mortgage. Backed by the house. The GPUs and the contracts they serve are the collateral.
Unsecured debt = the credit card.
Backed by a promise.
Everyone is panicking that $CRWV UNSECURED yield to MATURITY hit 15%
But a credit card rate spiking doesn't mean the house (or in this case the GPUs/data centers) lost value.
The secured lenders or the ones who underwrote the actual GPUs and contracts aren't selling here.
Now zoom out, because this is analogous to the entire AI market in wrapped up in a single company.
Trillions in secured, asset-backed debt are loaned against GPUs, data centers, power contracts, and signed capacity deals with hyperscalers. This money doesn't care about stock prices or investor sentiment.
It gets paid if the contracts pay. (Sounds pretty similar to $next if you ask me…)
Below that in line is unsecured debt, a bet on the companies themselves.
On top of everything is the equity, a bet on the story.
So when you see scary headlines about "AI debt," ask one question: which layer?
Yields spiking on unsecured debt and equities dropping while secured debt sits calm simply means the market is repricing the story.
The assets are fine.
The buildout keeps getting funded because the secured debt holders still believes in the collateral.
The day that changes… well the day secured lenders start questioning GPU values and contract quality, is the day the AI trade actually breaks and we should run for the hills.
Until then, volatility at the top of the stack (unsecured, equities) is just the shock absorbers doing their job.
And “great” comes far more from the problem solved by the software than the prettiness or even medium-term maintainability of the code. The difference between great and perfect is about filling in the latter.
"That creates predictability for the customer, and the customer is willing to pay a premium for that predictability."
Full conversation: youtu.be/KuBmIm4QMyE
"The iPhone comes along and the thing that becomes much more important is data — and we stop getting subsidization. Phones become much more expensive."
Full conversation: youtu.be/KuBmIm4QMyE
“Study is an investment.”
Shipping is about taking these ideas, taking this reputation and turning it into a result for client value.
Take a look at your week: what are you doing to study?
youtube.com/watch?v=INQe4zyZ…
Many thanks to @nickytonline for his gracious hosting today - a fabulous conversation. I learned a lot!
And we're live! Come hang with @ray_deck @BekahHW @shookcodes @dandigangi
xitter.cf/i/broadcasts/1jxXgBLqP…
I sometimes work on ideas by making songs about them with Suno. I thought this one was a lot of fun while also a cautionary tale for an eager software founder.
suno.com/song/73b00075-6720-…
Ray Deck @ statechange.ai retweeted
The only four real moats btw
Moats! Does anyone have one anymore?
Yes. Yes, they do.
But only a small subset of what people call moats create real long-term value and defensibility.
Not actually a moat:
1. Distribution: This does matter more than ever. Building products has gotten easier, but getting them to customers has not. Distribution, however, does not create durable defensibility. It helps create the raw ingredients for one, but it is not itself a moat.
2. Execution: Yes, speed is critical for startups. But a company that has to sprint forever is not a good business. Ultimately, it will misstep, slow down, and competition will catch up. Speed gives a company more shots on goal as it tries to build a great product and a great business, but it must eventually find something more durable.
3. Brand: The idea is that when everyone is building the same thing, the most resonant brand will win. But for startups, brand works more like customer acquisition spend: Put money in, get growth out. It’s not something that will effectively keep others from competing long-term. Brand can become a true moat over time (as with Coke or Nike or Apple), but it just takes too long to really matter for most startups.
What you're looking for is compounding advantages: a self-reinforcing flywheel that gets stronger and stronger over time, ultimately making a company very hard to compete with long-term.
There are only four compounding advantages that really matter. Every major tech company has at least one:
1. Switching costs: The more deeply embedded a product is in the way a customer works, the harder it is for that customer to switch. This increases retention and revenue per customer, which can be reinvested into the product.
2. Network effects: The more users a product has, the better it gets for everyone. This increases engagement and attracts more users.
3. Scale economies: The more volume a company has, the larger and more efficient its operations can be. This lowers unit costs, funding lower prices or a better product at the same price, further driving up volume.
4. Data effects: The more usage a product has, the more data it generates. This data can be used to improve the product and increase usage.
If you can’t see the potential for any in the startup you’re considering joining, that’s a red flag.
In today’s incredible post, @danhockenmaier shares a simple framework you can run in about an hour after getting an offer:
1. Identify a startup’s compounding advantage
2. Gauge how strong it could get
3. Measure whether it’s working
Inside: dozens of real company examples, the specific metrics to track, and benchmarks for what good and great look like.
Read it here: lennysnewsletter.com/p/how-t…
Ray Deck @ statechange.ai retweeted
I believe the "we ship 30,000 changes/month" stories. But I suspect the type of product you're shipping into is important.
If your product is like a big garden with 500 trees, each can be trimmed on its own. Integrations, tools, connectors, edge-cases, security fixes. Each change makes one tree better, so tons of changes can run in parallel.
Other products are more like a bonsai. Every cut changes the whole thing. If I want to cut here and you want to cut there, we have to actually agree first.
I wonder if a lot of the "code review" debate might be apples and oranges depending on whether you're a platform or a bonsai team.
A formalism of this is Kauffman's NK landscape. N is how many decisions you can make. K is how entangled they are. A wide-surface product has large N but small K. So it can absorb enormous parallel change. A tightly integrated UX has high K, so parallel changes collide in what they're trying to achieve, even when the code merges cleanly.
A subscription vendor exchanges cash now for cash later. When the price of time rises, those future receipts are worth less today—and carrying the wait costs more.
Full essay: youtube.com/watch?v=Ya7PV-ce…












