> If one currency has 10% inflation and another has 1%, the second countries bonds at 5% will have a higher real return than the first
It's not how it works. If both currency maintain change parity over time, then the inflation rate in one country compared to the other is irrelevant. “Real” (inflation adjusted) numbers make sense for consumers and local governments, but from an investing standpoint, the only thing that matters is the variation on FX rate.
And unlike what the myth of “inflation is the loss of value of a currency” says, those are actually very loosely correlated (and it tends to be anti correlated during inflation spikes due to central banks' interests rates).
Disagree, fairly strongly. In 2008, four trillion dollars evaporated. In order to keep the economy from completely crashing, the Fed created $4T using QE and such tricks. The result was 15 years of flat. No inflation for 15 years. If inflation shows up a decade and a half later, that probably wasn't the fault of how QE was done.
- I absolutely agree that inflation has nothing at all to do with QE, people who claimed that are just idiots who have a gold fetish.
- the problem I'm talking about is the fact that central banks didn't use QE as an opportunity to erase the public debt it bought. At the time it wouldn't have been an issue in any way. But now because inflation is back (due to oil) central banks cannot buy government bonds when they reach maturity and have to raise rates. Then the government bonds have become very expensive, and it has to be paid to the private sector on the market, so whenever a US govt security reaches maturity, the budget constraint increases. Sterilization of the debt would have alleviated this issue a lot at no cost.
Also, we should have taken the lessons of the era and raise the inflation target to 4%[1] at that time (it was definitely politically achievable then, now not so much).
You aren't serious about high-frequency karma ("HFK") unless you colocate your equipment with the routing for your sources so you can beat your HFK competition to a submission by at least 200ms.
We will have reached HFK-ception when the bots are fighting to be the first to submit "How I used AI to build a High-Frequency Karma Farming Bot on Hacker News."
I saw a company pitching almost exactly this on linkedin earlier this month. Except not as a portable heater but as your house's central heating system.
Of course, but the power dissipated in a data center provide zero heat for your house.
(Tangent: I wonder why we haven't seen deployment of organic Rankin cycle generators in AI data centers, the exhaust temperature should be compatible and that could yield a 10-20% energy bill saving).
Easier said than done, but if you water-cool the GPU just upstream of your domestic water heater, it wouldn't be a bad thing. Perhaps using coolant and a counterflow heat exchanger, rather than the potable water, but the point stands. Would just need a secondary tank as a buffer (able to soak up heat from the GPU at all times, even when there's no demand for hot water) which then flushes in when demand exists.
Let's say your GPU uses a constant 300W. It takes approximately 1.16 Wh to heat a liter of water 1 degree celsius under 100% efficiency. This setup won't be 100% efficient, so let's round up to 1.5 Wh per liter per degree (roughly 80% efficient). A typical domestic water heater in the US 40-50 gallons, so roughly 160 liters.
Let's say your incoming water temperature is 18C and you want it preheated to 50C, which is 32C degree differential, which means you'll need 32 * 1.5 * 160 = 7680 Wh, or 25 hours straight to heat the buffer tank from scratch.
You'll need to purchase a small water-to-water heat exchanger ($50), two pumps ($100 each), a power supply for said pumps, hose and/or copper pipe and fittings, and various other sundries, plus the cost of a buffer tank ($600ish), so figure all in roughly $1000, plus the cost of electricity to run the pumps.
At $0.22/kWh you're saving roughly $450 a year with this setup in foregone water heating, but because it's not 100% efficient you're spending $500 in electricity to run your GPU 24/7/365, and that's the maximum you can possibly save with the above assumptions. Scale up for more GPUs and down accordingly for less usage as you see fit.
Alternatively, use air as the heat conductor by placing the GPU laden machine in the same space as a hybrid heat pump water heater.
Just recycle an old water heater as the tank? I think your cost estimate is way overblown but it would certainly eat up a lot of time and effort to DIY. Regardless there's a much bigger flaw with this plan. Water tanks need to be kept above or below certain temperatures. The "preheated" range being described here is distinctly unsafe due to various microorganisms IIUC.
> I wonder why we haven't seen deployment of organic Rankin cycle generators in AI data centers
Because there was never a long term plan for AI data centers. It's an AI market capture and cash grab scheme that ends when local models eat their lunch.
That makes no sense. Do you think their plan is to spend billions of dollars and build infrastructure mega projects just to let it sit idle?
Did the rise personal computing make data centers and super computers obsolete?
Any advance in inference that allows local models to do the job will also benefit hyperscalers. Imagine the sheer amount of compute they could throw at problems if each 32GB of VRAM was enough for frontier reasoning.
They could also be betting that there will only be 1 or 2 big winners. And if they don't win they are saving on expenses, if they do win then they will have all the money they need to upgrade later if they want.
They use it in the city heating system, which is obviously the optimal way of reusing the heat but most data centers aren't put next to such a heating system.
I'm talking about making electricity back from the heat (using a low-temp thermodynamic cycle). It has a low yield (due to the low input temperature) but it's usually economically viable when using heat that would end up in the heavens anyway.
Small models are much smaller than frontier models though, which is how they end up consuming less energy despite low batch count. (Though with local models growing strong agentic capabilities, batching becomes a reality with local models as well).
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