Project Management

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NiFTy or Nasty?

A still image from a viral YouTube video known as “Charlie Bit My Finger.”

In this post, we explore the NFT: The Non-Fungible Token.  I am only going to give ‘token’ time to defining this, partially because I am still learning about it.  But I think you should know about this technology because (1) like it or not, it appears to be “a thing”, and (2) there is a reinforcement of a project management concept on which I blogged about already this month – that of secondary risk.

Some of you may recognize the “Charlie Bit My Finger” image I put in the header of this postYou’re seeing a screen capture of a viral YouTube video.   I did a Google search of that phrase as I’m writing this and it yielded about 2.3 million results.  You may also have read articles, like this one from the BBC, which describes how this video is now being removed from YouTube because it has become an NFT.  An NFT video of a kid biting another kid’s finger - that just sold for more than three-quarters of a million dollars.  Say WHAT?

So we start with, what is an NFT?  It’s one of those acronyms for which spelling it out helps make as much sense as a FoaB (Fish on a Bicycle).  But here goes: NFT stands for Non-Fungible Token.

Right.

So now we have to break that down.  Fungible is not a word we use every day.  If you asked me what it meant yesterday, I would have said fungible was an edible mushroom.  But no – it has nothing to do with fungi.

I actually have a go-to source for terms like this: Investopedia.  Here’s their definition:

Fungibility is the ability of a good or asset to be interchanged with other individual goods or assets of the same type. Fungible assets simplify the exchange and trade processes, as fungibility implies equal value between the assets.

So what’s fungible?  Cash money is an example.  I can find an equal exchange for a US$1 bill – twenty nickels, or four quarters or ten dimes are equal exchanges.

What’s non-fungible? Again, from Investopedia:

If Person A lends Person B his car, it is not acceptable for Person B to return a different car, even if it is the same make and model as the original car lent by Person A. Cars are not fungible with respect to ownership, but the gasoline that powers the cars is fungible. 

And finally, the last letter of the acronym - token.  Remember, we are still just spelling out the acronym here.  I hope you now “get” the Non-Fungible part, so let’s move on to TOKEN.  Think of tokens as a ‘unit of value’.   This applies to cryptocurrency as well as a token like the old-timey ones we used to use to allow admission to the subway.  Crypto tokens are cryptocurrency tokens. Cryptocurrencies or virtual currencies are denominated into these tokens – units of value, which reside on their own blockchains. Blockchains are special databases that store information in blocks that are then chained or linked together. This means that crypto tokens, which are also called crypto assets, represent a certain unit of value.

So why is this so hot now, literally on fire?  Yes, literally, ON FIRE.

Have a look at this video.  A group of crypto-enthusiasts called Injective Protocol bought a Banksy painting for about $100,000 and then burned it, to make their point about NFTs.

The point they were trying to make is about trust.  By destroying the original they are trying to build trust in blockchain technology. 

Whether or not you get this (I’m still wrapping my head around it) there is, as I said above, the aspect I’d like to tackle here is regarding secondary risk. The secondary risk, believe it or not, is the carbon footprint of NFTs.

According to a recent article, the positives of NFTs for artists are abundant:

Artists around the world were thrilled: NFTs provide the opportunity for them to make significant money on their work, reach a broader audience all over the world and link a digital file to a creator, ensuring authenticity. And with the value of cryptocurrency skyrocketing, some think there's never been a better time to get in on it. 

We could look at NFTs as a way to respond to the risk of theft of art.  That’s nifty. 

However, that same article goes on to talk about the downside – a nasty side - of NFTs.  It turns out that blockchain technology is very energy-intensive.  Blockchain incorporates a "proof of work" (PoW) method to create digital assets and it is – by design – highly inefficient and thus uses significant computing power, translating into large amounts of actual energy usage.  In fact, the computers are, in effect, trying to solve a complicated mathematical puzzle, something like trying to open a safe by trying every combination.  They make millions of attempts every second to solve the puzzle so that they can (on behalf of the ‘miner’) get ‘added to the blockchain’.  The higher the value the token, the more  difficult these puzzles are to solve, and that makes them increase in value, creating a spiraling need for greater computer power and larger data warehouses and stronger cooling units just to keep up.  As you can imagine, this causes an exponential increase in actual power consumption. 

The NFT open-source network, Ethereum, according to the article, is “currently estimated to (annually) consume roughly 44.94 terawatt-hours of electrical energy, which is comparable to the yearly power consumption of countries like Qatar and Hungary.”

So while NFT is ‘nifty’ for artists, it contains a secondary risk.  How do we respond to the secondary risk?  First: be aware of it – and articles and blog posts like this, I hope, help in that area.  Next: make the network less energy-hungry.  Efforts such as Greentouch from the past have been successful at reducing the energy consumption of IT networks.  This secondary risk provides a tertiary risk – an opportunity – for network engineers to focus on algorithms and technologies to keep the PoW vibrant and focused on security while still being less energy-hungry.  This has been done in the past.  I have blogged about GreenTouch, a consortium of IT and telecom companies who are fierce competitors but who collaborated on algorithms to reduce the energy use of the technology simply by using clever algorithms to reduce the number of times optical amplifiers transition from a zero to a one.  This collaboration resulted in a new optical transceiver which was expected to reduce the overall power consumption of the entire metro access network by 27 percent; this translates to about 4 terawatt hours of electricity saved on an annual basis, equivalent in terms of annual greenhouse gas emissions to taking nearly 600,000 cars off the road.  If competitor telecom companies can do that in 2014, think of what an open-source collaboration could do with 7 years of increased knowledge under their belts!

In addition to working on better networks, this provides opportunities for computer and data storage companies to improve the physical need for energy of their systems, something they are doing already, but this should motivate them to ‘up their game’ in this area.  It also should be a motivator for these companies to source their energy supply on renewables like solar and wind.

So while some technical enthusiasts are “burning up” art, they should also be “burning down” work products to reduce the hunger of NFTs and cryptocurrencies in general for carbon-intensive energy. 


What are your thoughts?  Should innovators be burning art?  Should folks developing cryptocurrencies be mindful of the climate impact of their work?  Would YOU pay $100,000 to own a digital artifact of a painting?

Posted by Richard Maltzman on: May 25, 2021 06:46 PM | Permalink | Comments (1)

Project Managers - we're full of BS

Project Managers are really full of BS.  By that, of course, I mean Breakdown Structures.  Work Breakdown Structures.  Risk Breakdown Structures, Organizational Breakdown Structures, Resource Breakdown Structures. 

That last one – Resource Breakdown Structure – and a recent episode of the excellent US Public Broadcasting Service (PBS – yet another BS, but not a Breakdown Structure) show, called Nova – got me thinking.  Isn’t the Earth a resource?  Is there such a thing as a World Breakdown Structure?

The episode of Nova was called “Decoding the Weather Machine”.  It’s a fascinating show in and of itself, but made even more fascinating in that it states in plain terms that climate change is real, it’s caused by humans, and it has significant consequences to us NOW and certainly to our children and grandchildren.  “We're poking at the climate system with a long, sharp, carbon-tipped spear”, says Paul Douglas, a meteorologist and former climate change denier, “and we’re not entirely clear of the consequences”, adds Harvard scientist John Holdren.  And here's what may be the most amazing and somewhat heartwarming thing: the show is funded by the David H. Koch fund for Science.  Yep.  THAT David Koch, of the Koch brothers.

This is a 2-hour show and it’s incredibly well-produced – and in a way, entertaining (in a worrisome sort of way).  But let’s circle back to our affinity for BS.  Breakdown Structures.  Remember – they’re all about decomposing something too big to get our minds around into smaller chunks? 

Well, have a look at this transcript from a segment of Decoding the Weather Machine:

NARRATOR: To do something about our climate future, we need to know what lies ahead.

KATHARINE HAYHOE: It's kind of as if you are driving down one of our dead straight roads, here in Texas. You can be driving down the road, even staying in your own lane, if you are driving along looking in the rearview mirror, because the road is completely straight, so where you were in the past is a perfect prediction of where you are going to be in the future. But what if you are driving down this road, looking in your rearview mirror and a giant curve comes up? You're going to run off the road, because the past is not a perfect predictor of the future if the road is changing.

NARRATOR: To see the road ahead, scientists at the Geophysical Fluid Dynamics Laboratory, in Princeton, New Jersey, are working to turn our understanding of how the land, sea, ice and air interact into a powerful simulation called a "climate model."

KATHARINE HAYHOE: Using nothing but basic physics, we can actually produce, in our computers, a virtual Earth.

NARRATOR: With this virtual Earth, scientists like Kirsten Findell work to predict where our climate is going, before it's too late to change course.

The first step is breaking the climate machine into its core components.

KIRSTEN FINDELL (Geophysical Fluid Dynamics Laboratory): Every climate model has four major physical components represented. We represent the ocean, we represent the land, the sea ice and the atmosphere all around the earth. Within those four components, we also then break up the earth into little grid boxes. And then we can slice up the atmosphere into thin layers and slice down into the ocean and down into the soil.

NARRATOR: Once they have divided the system into manageable parts, they use well-established mathematical equations, grid box by grid box, to run the model forward in time.

KATHARINE HAYHOE: These models are amazing. They can produce weather systems, even hurricanes; they can produce droughts and floods.

NARRATOR: Worldwide, there are dozens of models. They predict how each part of the climate machine will change, like sea surface temperature, storm intensity or the extent of the ice caps. Every detail is included. But the path to perfect models is still a work in progress, because Earth's climate machine is such a complicated one.

The role that clouds play, for instance, is important, but poorly understood. And the speed at which ice sheets will break apart is another big unknown.

STEPHEN PACALA: We're definitely making progress on making better predictions, but there is still an enormous amount about the climate system that we don't fully understand.

NARRATOR: But the models can be checked against things we know, like air temperature over the past hundred years. The models can be started in the past and run forward. The blue line shows the average of those predictions.

When compared with the actual temperature record, in red, their accuracy is revealed.

Below is a screenshot that compares the model output for temperature when run backward (blue) against the actuals (red) – you can see that the alignment is pretty good, speaking to the ability of the model to forecast.  So what happens when you let the model run forward?

You’ll just have to wait until the next blog post: Backward Pass, Forward Fail.

If you can’t wait, view the show here: http://www.pbs.org/wgbh/nova/earth/decoding-weather-machine.html

Posted by Richard Maltzman on: June 06, 2018 09:48 PM | Permalink | Comments (8)
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