>What it would be reasonable to say is if his model had correctly predicted the outcome of a significant sample of elections, then you could say his model has some accuracy or predictive power.

I don't know why you're couching that in a hypothetical, FiveThirtyEight has repeatedly done that exercise.

>But it still would never have been accurate or right in the specific instances it got wrong

It is core to the concept of a probability that the result is going to go the opposite way from the prediction sometimes! It's meaningless to call it "wrong".

The 70% figure is saying “we know most of the information needed to determine what the outcome of the election will be but we don’t know everything so can’t be certain”. There is no process where you can know every factor that determines the result in advance with absolutely accuracy and I don’t know why people expect there would be.

[1] https://www.sciencedirect.com/science/article/pii/S026137942...

Take Google or Meta: Today Google makes a shit-tonne of money and to make that money they need to run some servers. The servers are extremely cheap relatively to the revenue they make running the business. This makes them a very attractive stock - the core of why SAAS looks great. Now let's assume the monopoly path. Google can win. I think they likely will win. But now they're going to spending... how many hundreds of billions constantly training new models? The cost of providing the service suddenly isn't small relative revenue they're getting. So even for them it looks awful for their valuation.

So one of the things I strongly look for when hiring is for people who have a high sense of personal responsibility. They're not going to just throw shit out there because it's easy or quick. They know they are responsible for what goes out and they really are going to own that responsbility.

In the same way, take a look at anything senior management says about their ICE or military contracts. It's not that I think they're doing something bad or that the military shouldn't have access to good technology. It's that at best they seem entirely disinterested in that what they're doing could be harmful or that they have any responsibility if it is.

It's not that I think Palantir is helping the US government bomb Iranian school chilren. It's that I don't think it would bother them if they were.

The fundamental problem is that we're trying to create a simulation model of real hardware that is (a) realistic enough to tell us something reasonable about how to expect the hardware to behave and (b) computationally efficient enough to tell us about a in a reasonable period of time.

You've got all these silicon valley guys running around "venture investing", the truth is it's more of a life style than a money making exercise. They made their money decades ago, and now they're just sort of hanging around desperately trying to tell everyone how clever they are.

It's difficult to (a) see how he can say this having given any real thought at all and (b) understand why he's going to on news interviews and winging it.

So this turns into a death march.

If you are behind, the only thing you can do is make massive capital investments to catch up. Once you're ahead you can sell tokens until someone else catches up. And, breaking the model of normal of places like chip fabrication, your billions of investment may only keep you ahead for 2 months. So you have a tiny window to sell those tokens.

But we talk about inference separately for a reason: largely inference cost is the scaling cost. Once you have a model the margin on your inference is how you get to profitability, as long as your margin is positive you can make the entire enterprise profitable by just selling more tokens. This is the same fundamental business that chip fabs work on. Yes it costs them a lot to get to the next node, but what's important is the margin they can get on the wafers they sell, because they sell tonnes of wafers.

It's pretty core to the concept of SAAS businesses that yes, you do consider all costs. But you want to focus on the margin of the bit that scales. This is why WeWork exploded, the thing they were scaling only scaled up at negative margin.

The point is that if their inference margin is positive, they can "just" scale up and become profitable. If their inference margin is negative, then scaling up the business actually causes problems.

Secondly, it's not clear that the current LLMs are a run up to AGI. That's what LeCun is betting - that the LLM labs are chasing a local maxima.

Can AI be misused? No. It will be misused. There is no possibility of anything else, we have an online culture, centered on places like Twitter where they have embraced being the absolute worst person possible, and they are being handed tools like this like handing a hand gun to a chimpanzee.

The data in the chatbots dataset about that phrase tell it a lot about how it should behave, and that data includes stuff like Elon Musk going around calling people paedophiles and deleting the accounts of people tracking his private jet.

"Social experiment" you might as well run around shouting "is jus a prank bro!".