Joseph Noel Walker (in Washington DC 12-17 Nov)

Compute in Australia:

Over the next month, I'll be running a series of episodes on my podcast focusing on the question of whether / how Australia should scale up a large domestic compute industry. Think many GWs of AI compute.

I'll release episodes across the month, as I record them, rather than all at once, because things are moving so quickly.

First up is Andrew Charlton, who is arguably the key member of the Australian government shaping our AI strategy. Will publish that in the next 24 hours.

Subscribe wherever you get your podcasts so you don’t miss the episodes as they drop.

Full note announcing the series here:
josephnoelwalker.com/compute-in-aus…

See you for the first episode!

Who is the best legal academic in the US to speak with about (1) what’s likely to happen when generative AI fair use cases start reaching the Supreme Court, and (2) whether / how any changes to Australia’s Copyright Act & training compute regime could be used as evidence in those cases?

From a purely interview craft perspective, I don’t think I’ve seen a better Trump interview than @Douthat60’s

US midterm results come at a great time for Australia's training compute push.

New episode with John Hempton (@John_Hempton), legendary Australian hedge fund manager and short seller.

I wanted to get John’s thoughts on the likelihood of an overbuild / bust in AI compute, and what implications that has for the Australian government’s data centre push.

John talks me through why he thinks the global compute build-out is “fibre optics Mark II” - i.e. why AI is “the most amazing technology of your lifetime”, yet compute looks like “the worst business in the world”.

John’s recipe for the worst business in the world is: “high capital cost, low marginal cost, competition, and technical improvement”. With these ingredients, new entrants push prices down to marginal cost, businesses can't recover their capital costs, and they go bust.

To get an adequate return on the $7 trillion of capex expected to be poured into compute by 2030, John estimates you’d need $2 trillion per year in revenue.

That would most likely have to come from businesses replacing workers (or not hiring new ones), as consumers won’t get you there. But companies typically don’t fire people for a 10% saving. So to pay $2 trillion a year, they’d need to save at least twice as much, or $4 trillion in wages.

The OECD wage bill is about $40 trillion per year. So you’d need to save, for argument's sake, 10% of the wage bill across the entire OECD.

But that kind of saving seems politically and practically implausible in such a short timeframe.

So expect excess supply and bankruptcies. (Again, none of that is to say the underlying tech is useless - far from it.)

John also shares the thing that shocked him most about Anthropic's S-1: namely, how compute-efficient it is. By his estimates, Anthropic has spent only about $12 billion on training compute over the lifetime of the firm. Which makes the $7 trillion build-out look even worse.

There's so much more of interest in this conversation, but let me share one last thing. There's an interesting question as to exactly what the productive infrastructure left over from any overbuild will be. It might not be the GPUs, but the fabs and/or energy generation. John tells a great story about how old chip fabs he thought were worthless ended up finding an application making the tiny sensors in car air conditioners and Tesla batteries.

After all this, the policy implication for Aus govt is to happily facilitate the compute build-out, but just don’t subsidise the world’s compute the way China subsidised the world’s solar panels (another example of a bad business).

Watch below, or wherever you get your podcasts!

0:00:00 – The most amazing technology of your lifetime
00:03:10 – A short seller discovers jaggedness (from first principles)
00:19:40 – The Situational Awareness blow-up
00:24:46 – The worst business in the world
00:31:02 – Fibre optics, Mark II
00:42:41 – The best model was shockingly cheap to train
01:01:26 – Why the $7 trillion build-out can't earn its return
01:11:13 – What survives a compute bust?
01:22:59 – A data centre is a better neighbour than a prison
01:27:30 – A bad business is not a bubble