Travis Kalanick
Does his own confidence mean anything?
the same scored calls, split by how hard he was pushing at the mention that set his final stance. If credit rises with strength, his table-pounding carries information and you should weight it. If it's flat or inverted, his confidence is noise — treat a ×3 like a ×1. Cells under 5 calls are provisional and get no weight in the replay.
| he was | calls | credit | right/half/wrong | avg α | read |
|---|---|---|---|---|---|
| ×3 table-pounding | 2 | 0.75 | 1/1/0 | -0% | provisional (n<5) |
| ×2 stated with reasoning | 8 | 0.62 | 4/2/2 | -6% | better than coin flip |
Which kind of claim to trust
by the tier of the mention that set his final stance — a dated prediction is a different animal from a passing lean, and they don't have to score alike
| tier | calls | credit | right/half/wrong | avg α |
|---|---|---|---|---|
| explicit_prediction | 9 | 0.67 | 5/2/2 | -5% |
| positioning | 1 | 0.5 prov. | 0/1/0 | -10% |
What he's actually good at
his row of the scoreboard — these are the exact cells rescore.py uses to weight his mentions. w = clamp(2 × credit, 0.3, 1.5), applied only at n ≥ 5.
Live book — 3 positions
his latest stance on every ACTIVE idea. Expression = what he is effectively long or short: supporting a bearish idea is a SHORT, opposing one is a LONG.
| idea | his stance | expression | conviction | flag | eval in |
|---|---|---|---|---|---|
| 🏛️ Blue cities ban or license-cap robotaxis | support ×2 | ▼ SHORT TSLA | 28.2 | — | 219d |
| 🏛️ DOGE delivers real federal spending cuts under unified Republican control CONTESTED | support ×1 | ▼ SHORT BAH | 16.0 | — | 59d |
| 🤖 Independent frontier-model labs lose to hyperscaler capital CONTESTED | oppose ×2 | ▼ SHORT MSFT | 14.0 | — | 110d |
Where his book points
net push per primary instrument across his live stances — conviction × his agreement × the idea's direction. This is his implied book, not a position he disclosed.
Best and worst calls
How he argues
Latest from him
we should watch for this in the autonomous car space too. Cities may get cute and start doing things like that. And what they did on scooters, they could do on cars. ... New York is literally doing this right now.
If you get, if you have an LM or foundational model of some kind that is the best in the world of the scientific method, game the F over. You basically, you just light up more GPUs and you just got like a thousand more PhD students working for you.
So you get a bunch of full-on missionary engineers that work twice as hard, and you have a culture that is ultra fierce true seeking, and you don't get caught up in politics, bureaucracy, BS, and you just go for it. And I think that's where... And then you go, wow, scientific breakthrough, scientific method. Like you start winning on truth, and that will start, I believe, that will start to give the product awesomeness of OpenAI a run for its money.
I do know that every consumer software CEO that has an app in the app store is trippin. They're trippin right now. And I mean big boys. I mean guys with real stuff.
And so those customers are starting to deploy this quarter. And it's pretty interesting. I mean, in our delivery kitchens, the cost of labor is about 30% of revenue. That's what the successful guy, let's say 30%, 35% of revenue. In a brick and mortar restaurant, it's even higher. When they're running our machine, it's between 7% and 10% of revenue.
Except that ruling doesn't happen without Doge. That Doge caused that ruling to occur.
Here's the one thing on the NVIDIA thing that I would counter with a little bit of what's been said here, is like when AI gets cheap, you know what's going to happen, guys? There's going to be a lot more AI, right? I don't think... I think the price elasticity on this one is actually positive. So as the price goes down, the revenue usage, everything's going to go up.
You go through the white paper, you see what it is they did, what they innovated on, the science behind it, the thoroughness, and you're like, these guys are badass. It does not feel or sound like somebody who took something, just when you get through it.
At some point, the amount of data becomes the long pole in the tent. At some point, the quality of the algorithms becomes a long pole in the tent, and more compute is not going to change that.
So, but what happens is when you get really, really good at copying, and that time gets tighter and tighter and tighter and tighter and tighter, you eventually run out of things to copy. And then it flips to creativity, to creativity and innovation.
If it was really hard to hire people and they could even make it harder to hire people, do they fight bureaucracy with bureaucracy that it's harder to spend, harder to hire people, harder to procure certain things that you're supposed to spend money on? You can reduce this spend through a lot of very interesting nuanced rules that they're in control of.
I mean, what I'm hearing about these buildings is that they are super, super empty, like next level empty. And let's just say, I'm really glad I don't hold it like I'm an owner that has a bunch of leases to the federal government right now.
There's two deflationary things that we need. One is DOGE, and two is where AI is going to take us if it really does its thing, and that will keep us in an okay spot economically. But this spend has to go or we're in Greek territory, if that makes sense.
But then there's like this dark horse that nobody's talking about, which is, it's called electricity, it's called power. And all these vehicles are electric vehicles. And if you said, yeah, I just did some like quick back of the envelope calcs, if all of the miles in California went EV ride sharing, you would need to double the energy capacity of California.
So like we're getting to a place where these vehicles are provably safer than human driven vehicles. So, yes, there are mistakes, but they're just provably safer and people are just getting used to it. And that's a big part of the cycle. So I think we're getting out of the hysteria and we're getting into like, yeah, it's just great.