[1] https://en.wikipedia.org/wiki/Dagen_H

But there are significant differences. From the Wikipedia article:

> Indeed, fatal car-to-car and car-to-pedestrian accidents dropped sharply as a result, and the number of motor insurance claims went down by 40%.

> These initial improvements did not last, however. The number of motor insurance claims returned to 'normal' over the next six weeks and, by 1969, the accident rates were back to the levels seen before the change.

So those results returned to normal over 6 weeks, but the shared spaces were measured over 2 years. Also the changes were more drastic. 36 accidents over 4 years (9 per year) vs 4 over 2 years (2 per year).

Rural and underdeveloped areas are going to be the largest market IMO. Everyone can access a smartphone but not everyone has the luxury of seeing a Doctor in person, and if they do the time/travel costs can be significant.

Disclosure, I work for an EHR startup with a Telemedicine product.

I understand the 'sharing economy' desire to make use of underutilized resources, but this doesn't seem like an economically feasible way of doing so. The model works for Uber/Lyft: cars are a relatively high upfront cost compared to the cost of gas, but computer hardware is often less expensive upfront than the electricity costs of running it for a year. Additionally, much of the economic value in a service like Uber or Lyft is provided by the driver, not just the use of the car. In this service, the user doesn't provide any value, in fact, they're using up cycles/space that could otherwise be monetized.

This is harder for the company itself to do, because if they just hire people to go into libraries, universities etc. to install mining bots they might be criminally liable. "Uber for CPU cycles" seems like a less felonious enterprise than installing malware on public-use hardware.

My electricity rate is is 15.7 cents per kw-hour. During typical usage (MS Office, web browser, programming), my Intel 6-core desktop (without LCD monitor on) draws about 150 watts.

For back-of-the-napkin estimates, let's round the kwh cost up to 16 cents and the wattage to 300 watts (to cover scenario of some of cpu cores being 100% pegged). The electricity cost of 24x7 for one year would be ~$413.

What remains would be bandwidth costs -- if any. I like many others have Verizon FiOS and even if others have Comcast or ATT, there's no obvious residential bandwidth costs I can think of to calculate. Maybe... if the homeowner wants to upgrade the speed from 75GB@$99/month to 150@$199/month because the he wants to download the datasets faster. That extra $100 wouldn't have been spent for plain web browsing. So conceivably, that would be $1200 per year. What we don't know is how big the datasets are that must be downloaded. I assume the upload size would be minimal because the compute tasks appear to be variations on "y_output = computecombinationsmontecarlobruteforce(x_input)." The y_output answer would usually be order-of-magnitude(s) smaller than x_input.

Assuming there are no extra bandwidth costs, it would be hard for a company to buy computer hardware for less than a homeowner's $413/year electricity cost.

Perhaps suchflex's particular business model is financially wrong. However in general terms, it does seem possible to find a monetizing sweet spot of computing tasks that takes advantage of the idle and wasted resources of existing home computers. However, if the homeowner has to buy extra hardware that was only dedicated to suchflex, that's probably where the economics won't make as much sense.

That's the issue though, this wouldn't be similar to your typical usage. Instead, if they're using your GPU to train neural networks, it'll be running close to or at full capacity.

I realize that you rounded the costs up, but lets just look at the costs of a GPU often used for machine learning - Nvidia GTX 980 TI. According to Nvidia, it draws 250W under load which according to your figures would result in a yearly cost of roughly $344. That's just for the reference card, a typical card that a consumer would purchase would draw even more. You can buy a 980 TI for a little more than $400. That doesn't even begin to look at hardware actually designed for commercial and research applications.

I think that it's possible to find a way of monetizing computer resources, however, I think it has more to do with arbitraging differences in electricity costs. Suchflex's model certainly wouldn't work where I live (electricity costs in NYC are roughly 20 cents per kw-hour) but parts of the US are under 10 cents. I could see a company attempting to profit from these differences by setting up hardware in a cheap state and negotiating a favorable electricity rate. Heavy computation could then be done on these networks for significantly less than it could in New York or California.

In summary, the value of a consumer's unused computer has more to do with their electricity rate than their hardware.

I don't see how the calculations support that.

For example if we use your worse case scenarios of an entity (such as Suchflex) that had its own datacenter in a 20 cent kwh region and the crowdsourced homecomputers in a 9 cent kwh region, that's a difference of 11 cents.

If we round up the energy usage to 600 watts (pc + GPU), that 11 cents is an annual difference of ~$578. However, for Suchflex to even run computations at all on their own hardware -- whether its 20 or 9 cents -- they have to spend capex of ~$1500 of motherboard+cpu+gpu. That's the $1500 the homeowner already spent for his own purposes. Therefore, Suchflex can redirect $1500 to pay commissions/awards/etc towards pure computations instead of buying their own depreciating hardware (which includes buying/renting the physical datacenters to hold it all).

It seems like the homeowner's hardware is a very significant part of the arbitrage/monetization equation. Yes, there is also potential arbitrage in regional differences of electricity rates. However, the greater arbitrage (at least the first 3 years) is the unused time on residential pc that would have been wasted. That "unused time" arises from computer hardware that was already purchased for other purposes than Suchflex.

My suggestion was not that an entity use crowdsourced home computers, rather that it would be more efficient for a company to setup their own hardware and rent CPU cycles that way. The big difference is that Suchflex is limited to using hardware that consumers regularly purchase, whereas a company could use significantly more energy efficient setups and negotiate a better electricity rate. This is essentially what AWS already offers. Additionally, if you already have to transmit everything remotely, there's no need to stay in the US. Iceland offers rates around 4.3 cents. I chose the 980 TI for my example because it's about as close to perfect as you can find for this scenario while sticking with consumer grade hardware, average setups would be much worse.

My general point is that I don't think Suchflex's model is viable unless, as pliny mentioned, you have access to free electricity through some less-than-legal means (or you live in Iceland).

I think it's theoretically possible for electricity costs to overwhelm hardware costs but so far, I haven't seen any numbers that make this disparity obvious. Some example AWS costs[1]:

  g2.2xlarge is $0.65/hour
  g2.8xlarge is $2.68/hour

More analysis would be required to see if particular computation tasks can done 15x faster on AWS optimized instances than the unoptimized residential computers ($0.65/$0.043==15x).

Without concrete spreadsheet of tasks, performance runtimes, and cloud costs, I still don't see obvious evidence that AWS (or Google Cloud) will be more cost efficient than unused home computers.

[1]https://aws.amazon.com/ec2/pricing/

Spot instance prices are typically far less - for g2.2xlarge they average around $0.1/hr - https://ec2price.com/?product=Linux/UNIX&type=g2.2xlarge&reg...

And note that customers can't run arbitrary or secure workloads with this proposal - they just want to mine crypto on your hardware and give a small percentage of returns to you as rent.

When theDAO was launched, I toyed with the idea of an etherium GAPP similar to Flex but for anything - but it would be a hell of a lot of effort to build, and I'm not sure there is demand - people won't bother for $10/week, especially if it takes up a lot of their HD space, bandwidth and makes their GPU take off (noise).

For this the suchflex guys earn Gridcoins, which they can sell directly on the market and convert to money. But a user could leave out the middleman alltogether and just mine Gridcoins (or alternatively other cryptocurrency but for that you need ASICS) themselves.

However, lots of the technology being developed (robust autonomous flight, improved endurance & range, lower costs) will speed up developments for other applications that make better use of their strengths. Even more importantly, companies like Google are finally forcing the FAA to craft realistic regulations that don't completely cripple commercial applications. The FAA's lethargic pace has already severely hampered domestic development to the degree that Google had to do most of their development in Australia.

The exception to that is in teaching. It did fill a big need there, but we never managed to make the business model work (long high touch sales cycles, but universities only willing to pay very low prices per class). We also never found a growth model for this.

I use something similar, along with dotbot (https://github.com/anishathalye/dotbot) to wire up other application’s settings files. There’s lots of good examples around to steal and tweak.