Joey B. welcomes listeners to an engaging exploration of the world of algorithms and their profound impact on modern economies in this insightful episode of the Blind Knowledge podcast.

He introduces his guest, Noah Healy, a recreational mathematician and professional algorithm developer, who brings a wealth of knowledge to the table.

The conversation delves into the intricate web of market mechanisms, particularly focusing on Healey's innovative concept of Coordinated Discovery Markets.

This new approach aims to refine price discovery in commodity exchanges, promoting efficiency and fairness in trading, while also addressing the complexities of supply and demand dynamics.

As they unravel the nuances of algorithms, listeners gain a deeper understanding of how these mathematical tools shape everything from market behavior to the very fabric of economic interactions.

Takeaways:

• Algorithms play a crucial role in optimizing economic systems, enhancing efficiency while reducing costs.
• The concept of a three-sided market structure can revolutionize traditional exchanges by balancing supply, demand, and information.
• Understanding algorithms is essential for navigating modern technology and improving decision-making processes.
• The evolution of algorithms has significant historical roots, tracing back to ancient mathematics that still influence today's computational practices.
• Noah Healey's insights on game theory highlight the intersection of mathematics and market dynamics in shaping economic interactions.
• The potential implementation of Core Disc could fundamentally alter global commodity markets, leading to unprecedented economic growth.

Links referenced in this episode:

• BlindKnowledge.com
• Blind_Underscore_Knowledge
• coredisc.com

My name is Joey B.

This is the Blind Knowledge podcast.

If you know it, you know it, and if you don't, now you do.

Today is a good day.

Today is a scientific day.

We're gonna get mathematical.

We're gonna talk about algorithms, my friends.

Algorithms.

Do you know them?

Do you know what they are?

Google it real quick.

And if you don't have time, hang out with me and my guest today.

My guest is Noah Healey.

We're gonna bring him on in just a moment.

But just a sec, Just for you, if you don't know about it, I gotta tell you.

BlindKnowledge.com.

Hot.

That's hot.

Check it out, man.

It's new.

We're trying to be informative and entertaining.

So we got digital content, digital content creation, and hopefully, if you're a digital artist or a digital media content creator and you want to get on Blind Knowledge, let us know.

Let us know.

We're over on Twitter at Blind Underscore Knowledge.

There he is.

Hey, Noah, can you hear me?

Yes, I can.

Cool, man.

Hey, welcome to the show.

I've never had an actual mathematician or an algorithm developer.

Developer on here, so congratulations.

You're the first one.

Thanks.

Thanks for having me here.

No, it's a.

It's a pleasure.

It's a pleasure.

You said a lot of really interesting things in the prep that I don't want to get into too quick because there's just so much information we have to get out for everybody.

Can you tell us, just to start, who are you and what do you do?

Well, I'm a recreational mathematician, and so that means that I do math for fun.

My interest is computational mathematics, which is the kind of math that describes what computers do and can't do.

And a little while back, better part of a decade ago, I was toying around with a problem in information theory and found a new approach to economic markets.

And so since then, I've been working on developing, patenting, and promoting a better form of economy.

Wow.

Wow.

So a better form of economy.

So what does that mean?

So.

The primary problem of economics is organization.

We have a lot of people, we have a lot of capacity.

There's a lot of wealth.

There's a big world out there.

So the problem is, how do you get people to do things that are actually valuable to themselves and other people?

Because it's very confusing.

And you could get real good at making, you know, pins, for example, but if the world's got all the pins it needs, then, you know, you should stop and, you know, Stop and smell the roses.

Yeah, maybe Stop making so many pins.

Exactly.

Okay, so we need some kind of system to tell us what's important, what's unimportant, that kind of stuff.

And the best system that's ever been developed are open markets where people can come in, buyers and sellers can come together, negotiate prices with one another, and find the spot that causes supply and demand to balance off against each other.

I've found a way to change those two sided markets into three sided marketplaces, creating separate markets for supply, demand and information and negotiation.

And that allows the entire system to be more efficient and consequently less expensive.

And so the less money that we spend in the financial sector, the more money we have for increasing production or increasing vacation time, depending on what people want to do with the extra money once we've got it.

Okay, okay.

So work smarter, not harder.

Save a couple bucks.

And we're talking about exchanges, we're talking about.

Is it commodity exchanges?

Specifically?

Commodity exchanges are sort of the foundation of the financial system because at the base of things, people got to eat, we got to keep oil flowing around, electricity, metals, these basic products are the things that we make our world out of, that we eat, that we drink, and so on.

On top of that are then other kinds of financial exchanges where you trade companies and debts of people that are engag engaging in these activities of producing and using these raw materials.

So I'm kind of starting at the base of the chain and then we can sort of grow outwards from there.

Okay, okay.

It's a lot of information right off the bat, for sure.

So I want to make sure we're breaking it down in a way that everyone can kind of digest and understand.

So what, what led you into this, into this development?

Because you're an algorithm developer, correct?

Yeah, yeah.

So how did you, how did you end up, where did that start for you?

When did you start doing algorithms?

How did that end up into Cordesk?

So I started getting into algorithms when I got out of college, needed a job, and started working for a local company called Boxer Jam, which was a pioneer in social gaming.

And they put me on task of doing things like analyzing the web logs to work out player behavior and so on.

They had a weblog parser, but it had been built in the early days of their website, so it only tracked a few different pages.

And they also were starting to add extra servers to keep up with the amount of content they were putting out.

And it didn't play well with that situation.

And the session tracking algorithm that it was using was so memory intense that it was actually choking and it couldn't actually finish anymore.

And so I pretty much learned how to program the language that they used while doing that project.

And it took a while, but ultimately, with a lot of help, I was able to increase the speed of the algorithm by, or the speed times the resource usage by a factor of a million.

Okay, so you gave this bad boy the Heimlich basically, and you got this thing unchoked and running, correct?

Yeah, yeah.

And that's, that's a, that's a pretty exciting feeling.

And at the same time, I was also doing deep dives into the, the underlying mathematics of this stuff.

And that's just, it's really fascinating.

And thanks to the fact that the Internet exists and the people who came up with most of this mathematics invented most of the stuff that makes the Internet work, it's all very easy to find on the Internet.

And so that's, that's what I was doing.

I was learning about this new form of math, applying it to my job, making things a lot better than they used to be.

And so that became a real big piece of my life.

And so I'm sure, yeah, then fast forward, you know, a decade and a half from, from there.

I've learned a lot.

I had finished a job and sort of decided to take some time and space to just think about these math problems.

I had money in the bank, so I was okay that way.

And I just wanted to see if I could think up anything interesting.

So I was, I was doing, studying and trying to find some new approaches to some sort of new problems.

And I was working on the problem of communicating consensus, and I found an approach using game theory.

And I was talking to a friend about it, and he asked about using it to predict what markets would do.

And I realized that there was an intriguing possibility that you could build a market with the technology.

And this came out.

And so I started analyzing this compared to the algorithms of the existing marketplace.

And I got that, that, I got that old million x tickle.

This is actually only about 300,000 x, but that's highly significant in real terms.

And so once you understand that, you've got an algorithm that works that much better and you know about the costs that are imposed by the inefficiencies of the existing markets, there's, there's nothing else I could be working on that's, that's more economically valuable than this.

So are you very educated, very well educated, or are you education?

Because it sounds like you have to Be wicked smart to do this stuff.

What's your, what's your educational background like?

So I, I grew up in a university town.

I started going to the University of Virginia while I was still in high school because that's what they do if you finish out a course of high school education.

And I was part of several different pilot programs, some of which took and some of which didn't, to accelerate mathematical education.

So that meant that I'd finished off the math classes in high school by the time I was a sophomore.

Then I went to uva.

Just basically wandered around the engineering school taking interesting looking classes.

The thing about computational math is that the entry point is kind of shallow.

Games like Minecraft provide environments that would allow children to do some rudimentary programming.

But the depth is so great that it almost doesn't matter how smart you are because you can just keep going until you're, you're exhausted and you can't go any further and there's just an infinite amount more that's, that's out there for you to go explore later.

So, so when you say like go and explore, I'm gonna break this down to like just regular like grade school education.

What are we, what are we exploring?

How, how does it go?

Like, what is it?

You know what I'm saying?

So thinking about grade school education, some of the earliest algorithms you learn are arithmetic.

So you learn how to add, you learn that 2 and 3 are 5, but then you start doing multi place adding where you know, 8 and 7 are also 5.

But you got to remember that one that goes over to the next place and turns it into a 15.

And so that carrying the one is the algorithm for adding.

And then once kids get good at that, you learn about multiplication and the whole wedding cake thing where you do the offset and then you get all the numbers that you can add up and stuff like that.

Well, it turns out that that's not the best way to multiply.

Computers use something called the fast Fourier transform to multiply in a way that's a lot faster than the way that we teach people in school.

It's also a lot more complicated.

But that's not the only way to multiply.

In geometry you can multiply by making triangles and making similar triangles.

And so in Every discipline of mathematics, there are techniques that basically let you multiply things, let you add things, and each one of them has an algorithm, and there can be multiple algorithms that do each of those things.

And so that's the exploration.

You find out all the tools that exist, you find out problems, you think about new problems or applying new tools to old problems, and you see how that works.

Is it faster?

Is it slower?

One of the most important things in computation is sorting, putting things in order.

There's a sort of a joke competition around something called bogo sort.

And bogo is like the worst thing ever.

And it's sorting algorithms that theoretically will finish, but might take forever.

So one example of a bogo sort is that you shuffle everything, and then you check and see if they're in the right order.

So if you had a deck of cards, you could put them back in the.

In the right order by, say, dealing them out face up into piles, and then sorting the piles into the right order and putting the deck back together.

Okay, sort.

What you would do is you'd shuffle the deck a few times and then fan it out and see if it was in the right order, and if it was, you're done.

But if it isn't, shuffle the deck.

Again and again and again and again until.

Well, until the universe runs out, actually, because that would.

That would happen long before you'd hit it fair.

Okay, so you.

You're basically.

You're taking every kind of which way, whether it's randoms or whether it's actually, you know, planned.

So randomness can be a valuable tool in many algorithms.

There's something called the Monte Carlo method, where instead of trying to figure out a really good theoretical model of how something might happen, you actually build a model of sort of what does happen and just throw a bunch of random instances at it.

So, like.

And see what sticks?

Basically, yeah.

Kind of see what happens.

So if you.

If you can run a computer trial of something, you can run thousands or millions of computer trials, and then.

And then you can use those outcomes to give you a good model presentation of how the system actually behaves.

And so that's actually when I was doing the analysis back at Boxer Jam.

I was using our customer sessions as a Monte Carlo model, a special kind of stochastic system called a Markov chain, where I was treating the customer behaviors as just random bounces through our website, and then I was adding all of them onto each other and.

And looking at what the probability of moving from one part of a website to another one was and that's.

That allows you to figure out what's popular, what's unpopular, what's making you money, what's losing you money.

The algorithm just figuring out, like, all these different instances and which way and whose it's.

And where someone's going to go.

Basically, that's.

I mean, that's.

That's some serious stuff.

It's almost like we should have known about this kind of technology before Twitter, before Facebook, before we actually started hearing about the word algorithm.

Have algorithms been around for longer than we know about them or have known about them?

Absolutely, yes.

The word algorithm belongs to the same root as the word algebra, actually, and they're both named after a guy.

He's.

He's a mathematician from the Arab world from close to a thousand years ago at this point.

But a great deal of mathematics actually has concerned algorithms.

One of the most ancient pieces of mathematics we have is something called the sieve of erosthenes.

And the way that works is you just write out a bunch of all the numbers.

2, 3, 4, 5.

Just keep going.

And you circle the one at the front, the two, and then every two numbers, you strike it out.

And so then the first unstruck out number is three.

Circle that.

And then every three numbers strike out and go like that.

Well, what happens is every number you circle is prime.

Okay.

Okay.

Siva Verosthenes, up until about 30 years ago, was the most efficient way to list prime numbers that had ever been discovered by human beings.

I feel like we went over this at the fourth grade somewhere.

I don't know.

Probably did, actually, when they introduced prime numbers.

That's a pretty classic thing to show kids that they kind of put the first hundred numbers in a little box and like, you know, cross out the fives and cross out the two.

Yeah.

Go down with the threes.

That.

That's an algorithm that is a couple thousand years old.

Wow.

Wow.

So, okay, so algorithm, big, scary, funky word that we all know, but we don't really know how it works.

But we're learning.

We're all learning because we're here with Noah.

Noah, thank you again for coming on to the show, because we, we need the knowledge.

We need you to drop some knowledge on us about algorithms, especially before we get back into more of what you do, specifically in this huge major project we got to get into.

You know, algorithms are fascinating.

You know, I don't know about you.

And, and I assume you.

You find them fascinating because you work in the field, you are an algorithm developer.

But I, I find them Fascinating because they can be tweaked and they can be just the Google algorithm.

It seems like Google itself has actually changed its search algorithm over the years, and it seems like recently, and I don't know about you, but I see a lot more ads come up on the first two pages than I ever did before.

It's almost like you have to search harder to get to the answer on Google, which wasn't the case like five to seven years ago.

Why do they do that?

Why, why do they change it and how do they change it?

Like, is there like an algorithm box that, that they have to open up with a key and say, all right, we're going to change this 5 to it too?

Basically, yes.

That's kind of how it works out, is computers don't just do stuff.

They do what we tell them to do.

And there's a lot of different ways for us to tell them to do things.

But no matter what technique we're using to tell to do things, and no matter what they're doing, at the heart of what they're doing is the algorithm of what they're doing.

And so because that, that computer thing is just pretty much, you know, text numbers, stuff like that.

If, if you think about, like, if, if a mechanic and metal worker owned a car, they could go in and, and move stuff around if they wanted to.

They could, they could raise the engine by a quarter of an inch or, or move the struts of the wheels around if they felt like it.

And that might make the car work better or it might make the car work worse.

But if they wanted to, they could change those things.

Well, computers, the, the thing that puts them together is just a file.

It's, it's just, it's just like anything you might have written in an email.

And so those things are very easy to change.

And it's not like Google is a car that we have several million of and everybody's driving it around.

There's one Google, and they're constantly trying to figure out how to get what they want.

And so they just go in there and, you know, get the crescent wrenches out and move stuff around and see if that works better or worse.

And that's, that's a really severe problem that we have with these, with our current leading companies, is that whether you're talking about Google or Amazon or Facebook, Netflix, Twitter, what makes these companies function is the algorithms that allow them to have the presence show you what they're showing you, do what they do and make whatever money they make and they need to keep those algorithms secret, which is why we've got kind of this heebie jeebies type of feeling about them these days.

And that gets into the game theory.

Google is essentially deciding what's important and what's unimportant on the web.

That's a lot of power, Right?

Yeah, true.

So if you knew how they were making that decision, you could do things that they would decide were important.

Interesting.

But they're not necessarily interested in people being able to decide things are important.

They want to be able to sort of decide what's important in a way that their customer base that's coming in is going to agree with them.

So they, they haven't solved the problem.

They've basically taken a monopoly, you know, squat over hiding the existence of the problem from the rest of us.

So we actually have a problem of how to have a social information discourse on a web where what's important can be promoted and promulgated and what's unimportant can be, you know, ignored.

We'd all be a lot happier if, if spammers and trolls were ignored.

Except of course, the spammers and trolls, of course.

Sure, sure, of course.

Maybe there aren't any non spammers and trolls.

What do we know?

Like maybe none of us have good ideas.

So.

Yeah, and who's to say the algorithm basically.

Right, the algorithm decides that.

Right.

Well, Google's very happy with that outcome because the algorithm, something they got in a lockbox in the basement and when they decide to change their mind, they go change their mind.

Right, right.

And they can do that at a whim.

I mean, was, here's a funky question for you, but was Google the first one to really start using an algorithm like where, where were these originally used, especially in the age of the Internet.

So the interesting thing is that algorithm is so general and basic.

It's, it's identical to the function of the computer in the first place.

So your computer has hardware and there's some hardware bits that have code in them that they only do one thing, but at a very low level, the computer starts running code to tell it what to do.

When your computer boots up, the absolute first thing it does when the chip wakes up and starts getting electricity going through it is pre programmed.

But that pre programmed task is go look at your memory, go read what's there, go do what it says.

The boot sequence of your machine is in a specific spot in your memory.

Whether you've got a hard drive or HD or whatever.

Sure.

That boot sequence is A program, you can change that program and it will change what your computer does.

If you've got a dual or multi boot machine, that's how it works is you tell the machine to change from booting into this program, this operating system, to that operating system, and it changes text in the boot sector.

Yeah, that's how it starts up, sure.

Yep.

So your computer is algorithm stacked on algorithms.

Stacked on algorithms.

Stacked on algorithms.

What the big tech Internet people started doing was very consciously building sort of supercomputers to run very, very sophisticated algorithms and very, very large scale algorithms.

So what really got Google up to the top was simultaneously building the Google cluster and building an algorithm that took advantage of the Google cluster to be able to create incredibly prompt responses.

Because that was and still is Google's claim to fame.

When you show up and say, I want to know something, it tells you.

Right away, yeah, it's quicker than lightning.

Yeah, it's working lightning.

And normally, you know, back at least when I was using Google before, like a couple years ago, I just feel like a couple years ago something changed or it was like, I can't get exactly the right answer that I'm looking for.

Like, why is the sky blue?

You know, the first answer wouldn't be, oh, because of the reflection of the, the oceans or whatnot.

It would be huge.

Here's an advertisement, here's a possible answer.

Here's something maybe that is not related to your question.

And now it just seems like we're getting further and further away from what Google was great for.

And that was being Google being, like you said, quick to the point and just giving us the answers that we are seeking.

Well, the difficulty is that the entire world is essentially evolving to deal with a world where Google is deciding what right and wrong is.

And so while they haven't published their opinions, people are constantly learning how more and more about how they're, whatever they're doing is working.

And so they're changing and everybody else is changing and reacting.

And there's so much to be gained from getting your eyeballs that, you know, some guy that just does something about the.

It's it.

So the sky is in fact blue because of oxygen and how that bends light and what, what wavelengths of light diffract less and more.

Traveling through oxygen is the reason the sky is blue.

Thank you for that.

I needed that.

I was wondering, but.

And I'm trying to figure out how to, how to hack the algorithm so that they'll get put in front of you because there's not a good chance that you're going to buy their vodka because you're asking why the sky is blue, but there's not no chance.

And it costs them very, very little for you to read the web page.

So.

And Google wants that to happen.

Google, Google's algorithm, I should say, wants that bumped up because it, well, they're,.

They're making money from those advertisements.

When you click through, whether you buy something or not, Google's taken its change.

Makes sense.

Google is the octopus of the world for sure.

I mean that in a good way.

I mean it's got tentacles like everywhere.

It's an amazing company, it's a huge story.

And hopefully, I don't know, I'll just say this, hopefully there's some competition.

We talk about open markets, we talk about, especially in your case, Noah, with algorithms leading to commodity exchanges.

And we're going to get into that in just a moment.

But there's got to be some, there's got to be some competition, you know, there's got to be some, some playback.

Coke needs Pepsi, you know, and, and in this world, the world needs Noah Healey.

And what you bring to the table, it looks like here is core disc coordinated discovering market.

Is that, is that correct?

Yes, that's what I call my.

Okay, so you aren't going to compete with who and what is this in fact?

So the primary people I'd be competing with are commodity market operators, places like the CME Group, the London Metals Exchange, the Intercontinental Exchange, places like that.

And what this is, is a new price discovery mechanism.

So if you've heard about economics, you know about supply and demand, some people make stuff, some people need to use that stuff.

A price needs to be found so that supply and demand will balance off against each other.

If, if they don't balance, you either get shortages or surpluses and both of those can be a disaster.

So my mechanism is a way for the makers and users of commodities like corn or oil or steel to pre negotiate the prices at which they will all trade, conduct those trades at the negotiated fair prices and come back and do it all over again.

And by structuring the negotiations as an ongoing sequence of simultaneous, like daily opportunities for months and years into the future.

We actually stabilize the price swings and create a system that's just much more stable and fair than the kinds of markets that we have right now.

Okay.

Okay.

Complicated, but I'm following it.

Okay, so you're basically, you're cutting out the outliers, you're a compression tool.

Basically, you're taking out the very highs and the very lows and you're making it more stable.

Yes.

Yeah.

So in fact, the marketplaces are information filter machines.

What I was talking about earlier, organizing an economy is difficult.

And that's because everybody knows everything.

Right.

You know that the classic you get in the, in the.

You get in a taxi and your driver explains to you how the world ought to work.

Everybody, everybody thinks that they know what their little corner of the world, world an opinion about what, what should be going on.

But what's really going on is some sort of middle of all of that.

The again, you got supply on one side, you got demand on one side.

They got to come together.

Because if it's all one way or all the other way, lots and lots of people are left out in the cold.

Yeah, a lot of perspectives, a lot of opinions, but it's all, it's all there in the soup.

Exactly.

So what we have is a situation where everybody's got an opinion, but there's one final price that everyone will actually agree to.

And what markets are about is getting from all of people's opinions that final price.

And this is just a more efficient way to do that.

Because in my market, instead of sort of incentivizing people to seek out the most foolish counterparty they can get and then taking as much advantage of them as they can, my market incentivizes figuring out where the consensus is going to be as far into the future as you can figure it out, and telling everybody what you've figured out as quickly as you can, because you want to, you want to sort of lay your stake in the ground for taking the largest share of that negotiation.

So everyone's engaged in the negotiation, but the closer you are to whatever the final agreement is, and the longer you've been close to that final agreement, the bigger a share you, you get of the prize that the negotiators divide up.

So the people who figure out what the right answer is going to turn out to be and tell everybody the soonest get the biggest stake.

And as a result of doing that iteratively, eventually you get to the point where everyone's already aware of where everything's heading.

At least, you know, for a few Weeks, chaos and weather and stuff will come up, but people are just constantly adjusting to the future as best that we can.

Okay, it makes sense.

It makes sense.

Very deep stuff.

This is, this is some very intricate stuff.

This is probably the deepest, the deepest dive we have taken here on the Blind Knowledge podcast.

And I love it because this is knowledge.

This is some real deal, real life, discovery, topical conversation that we have going on here.

So this is more than just a stock market.

This is.

Does this go deeper?

Because when I, when you're, when you were explaining it, I'm thinking, okay, trade books.

And there's, there's this negotiation coming in here, and the price pops up on this market because, well, don't worry about that noise.

And the price pops up here because this person would buy it for this much at this price and, and so on and so forth.

Is it like a stock market kind of tool or does it go deeper than that?

It's very much like a stock market tool.

Stock markets themselves are, were evolved from commodity markets.

And so this would be a change in the way that those kinds of markets were conducted.

Once again, starting with commodity markets, but hopefully once demonstrating their efficiency and effectiveness there, expanding out into bonds, stocks, and other kinds of financial instruments as well.

Okay, stocks and bonds.

Stocks and bonds.

You have to have them.

They'll never go away, basically.

And the cool thing about stocks and bonds is one is the, the opposite of the other, right?

So when stocks are up, bonds, prices are usually down and so forth and so on.

Do you have mechanisms in your, in your actual encore disk that make sure that nothing gets out of whack?

You know, are there other boundaries or measures?

Yes, absolutely.

And you have to have that.

So in my system, everything is based on voluntary action with foreknowledge.

So the producers and consumers don't actually have to commit to trade until after the price is set.

And they know what everyone on their side of the fence, what deal they're going to be getting, and then they decide how much to partake of that deal.

So if you don't think that prices came up enough for what you have to offer, then you simply sit on the sidelines and wait for a better day to come by.

And so if the system that took on board everyone's opinions comes up with this price that's too low, the result of that will be a shortage.

And then a big opportunity will exist to fix that shortage by bringing that price up.

And the people that created that shortage, because their reward is all based on sort of the fraction of the pie, that pie won't be as large as they were expecting because a shortage has less trading happening through it, just as a surplus does.

And so they'll do a little worse than they were expecting.

And other people will be able to step in and fix the marketplace up and move forward.

And sort of an analogous thing happens if the price was, was too high, you know, there'll be a surplus and then the people that made the mistake won't get what they were expecting and other people will be able to step in.

So with three sides, there's, there's essentially always a balance point because the, the max only happens when all three sides agree with each other.

And so any attempt, any attempt to sort of catch somebody out or, or screw somebody, each, each party has a veto effectively.

And so if the market gets out of whack and starts harming one of the sides, then they can just say, you know what?

I don't want to play today.

And then that, that drags the entire place back into, into balance.

Wow.

So that's, that's an efficient, and that's an interesting checks and measures kind of function you have there.

So not only is it one to one, I mean, you're talking triangular.

Yeah, yeah.

It's actually kind of interesting.

In game theory, three player games are very special because in a free for all three player game, there's actually no stable equilibrium because two sides can always team up against the third.

But by reversing the power structure and making it so that any one side can kind of halt the game by stepping back, we create a situation where no two sides ever want to team up because if they do that, then all they're doing is hurting themselves.

Okay, all right, so there's, yeah, there's, so there's incentive, basically.

That makes sense.

That makes sense.

It makes so much sense.

And literally we just.

You just came up with it.

You know, it's funny how that happens.

Noah, where do you see this going?

Like what, what's your goal as far as core disk goes, as far as implementation of this type of resource or the resource itself?

What, where, you know, in a perfect world, where do you see this going?

Well, I'm working with several people who are attempting to build out and, and have marketplaces that would have this technology in them.

Where I'd like to see this is to have one of those get off the ground, find some successful customers and actually carry its advantages through, have those customers become sort of industry leaders in their region or country because they're using markets that are Just much, much more efficient.

And there's, there's precedent for that historically.

And so building from there, I'd like to see other industries and new customers come on board to these marketplaces until they just become a standard part of how we do things.

And the costs of running our economies just go down precipitously.

So this is more than just the US Economy.

This is more than just a country's economy.

You're looking at this as being, you know, a global, and a global type tool.

Everyone on the planet eats food.

Oil is a global market that's worth a trillion dollars a year.

Steel, copper, aluminum, wood, wool, cotton.

There's, there's nothing special about any of this stuff.

It's, everyone uses all of these things.

We all need them to, to live and, and have homes and clothes and so on.

And while the US has some of the most efficient markets in the world, they're not as efficient as CDMs are.

And so this would offer us a big boost economically.

It would offer other countries an enormous boost economically.

So how so, Lake?

What's an example of that that you can give us?

So kind of my standard example is wheat farmers.

So American wheat farmers have an average margin of around 14%.

So a family farm that's growing about half a million dollars in wheat will earn about $70,000 a year.

They have market costs that are probably on the order of around 5%, which means that something like $25,000 of the revenue that they could earn is being used up by the marketplace.

So a CDM that was operating at a 1% overhead would increase their profits from $70,000 a year to $90,000 a year.

Okay, all right, that's a big jump.

Yes, it is, because commodity production, and in many cases commodity consumption as well, are low margin businesses.

You know, people aren't, aren't making bank creating gravel or iron or anything like that.

There's sort of a magnifying effect.

Every point that you can add to somebody that's operating at a 10% margin is a 10% increase in their actual final value.

Oil companies, which are famously some of the largest revenue companies on earth, operate on margins between 8 and 12%.

So an oil company that might be doing a quarter of a trillion dollars a year operating at 8%, maybe they're losing 3 or 4% of their potential revenue to the marketplace.

That wouldn't be unreasonable.

And in that case, a one point shift on, you know, a quarter trillion dollars to be two and a half billion dollars for every point that you could shave off that thing.

So that's, that starts looking really, really big.

Yeah, that's significant for sure.

Absolutely.

So you're cutting waste, you're cutting loss.

You're.

You're basically making it more transparent and also a simpler, smoother process for commodities exchanges, for any kind of, kind of exchange that would really need a CDM or a coordinated discovery market.

Right, exactly.

Yes.

Wow, it's so interesting.

Like, where.

How did you get into this?

Like, was there something that happened to you in your professional life or maybe, you know, you said earlier that, you know, you went through some time just kind of discovering, like, what's something that, that could help the world in a way.

How did you get to this?

I mean, not everyone comes up with a, oh, yeah, let's just come up with a CDM and get a point share and just kind of just go this way.

And the other thing, a lot of people are just trying to tie their shoes in the morning.

You know, what influence for this?

It was a handful of lucky accidents in a row.

Okay.

I was thinking about this problem.

I worked at a number of companies.

Some of them got big, some of whom never got the chance.

And what I've usually observed at companies is that the computers knew more about the business than the managers of the business did.

Because in our algorithmic days, the computer knows everything.

You know, customers don't interact on the Internet with people.

They interact with your web server.

So your web server knows everything about your customers, everything that you can know, but you don't.

Because reading the web server is a real pain in the neck, so.

Right.

A lot of it's coded too.

Exactly.

Yeah.

It's difficult.

So I was thinking about this problem.

What if you've got people, machines, sensors, all kinds of different things that can communicate with each other on a network.

How would they all agree on something?

And so that was.

That's what I was thinking about.

That's cool.

And that's when I decided to pick up game theory, because game theory is a mathematical tool for describing agents with their own interests, interacting with each other.

And, and so that's where I came up with this, this approach where you could pay a set of agents and sort of create an interest in what you wanted them to be interested in, to, to get a consensus, get information out of them in an efficient manner.

And I was, I was talking to my buddy, and he asked if you could use that, if you could pay for stock market tips.

And I said, no, you can't, because you can't pay enough for stock.

Like the People who know the information are making money themselves.

You'd have to pay them at least that much money for the tip, but that's how much money the tip is worth.

So there's a mismatch.

Like if you pay.

Yeah.

There's no incentive.

If you pay enough to get them to tell you, then it's not worth enough for you to make the payment.

And that was rattled around my head as I was walking back to my house and I was like, you know, there's kind of an identity thing going on there.

Markets are paying people for information.

Could you use my approach and kind of create a marketplace that used my approach to pay efficiently for the information that's coming into it?

And so I was like, well, that's an interesting thing to think about.

Let me work on that problem and see if it's possible to come up with an answer.

And so I did.

And then once I had, that's when I started doing analysis on it and discovered that it was not just a very efficient way for a marketplace to operate, but a more efficient way than an existing market to operate.

Okay.

I mean, hey, yeah.

And is it something.

Do you consider yourself an entrepreneur at this point, or do you consider yourself a developer?

A little bit of both.

At this point, I'm definitely got my entrepreneur hat on.

It's the wild, wild west, buddy.

Yeah, you know it.

That's kind of how the world is willing to accept new ideas at this point.

So, sure, yeah, it's a good point.

I'm working on the patent and trying to make connections and deals.

And like I said, there's a handful of people that I've managed to connect with around the world that are working on getting set up.

Who are you trying to connect with?

Who's the audience?

Like, if I could wave my magic wand and say, hey, we're going to get 10 people of this, this kind of audience on right now just to check out Noah and check out the cdms.

What kind of audience are you looking for?

Who are you trying to get in to this.

This discussion?

Or in other words, you know, who do you want paying attention to this?

Well, primarily for.

For just pure CDM market operators.

So that would be people that run auction houses.

They can operate markets, brokerages, or existing market operators.

Some of those people are also governments in many parts of the world.

The government, basically.

Yeah.

It's not just people.

These are entities, these are groups of people.

And like you said, governments, like whole governments, basically, yeah.

Wow.

Wow.

Okay.

I mean, so it's.

Yeah.

I mean, there's the reason I ask that.

It's like, how does one take a.

Take something like a cdm, like core disk?

And how do you get that message out there to the people that need to see it or need to hear it or should or even would.

Would entertain the thought of implementing something like this?

It's got to be an interesting struggle on your end to figure that out.

I'm assuming that's.

I will.

I will identify with the struggle part.

The interesting.

It's.

It's more just work.

I sort of.

I come up with an idea that.

That might be able to bear some fruit, and then I just pour the energy into it and, you know, kind of come up above water a year later and kind of see how it's going, see what I might be able to do to improve.

If there's a different track, figure out what to do, come up with something else, and then just start pouring the energy into the new thing.

So.

Sure.

That's.

That's.

I've been doing podcasting for about a year now.

This is probably about the 60th recording I've made, and about 50 of them are online at this point.

Not bad.

Those are good odds.

Those are good numbers right there.

Yeah.

And I'm just.

And I'm also working on the patent, which has been its own special kind of nightmare.

Oh, I'm sure that's probably a whole nother episode right there.

Yeah, yeah.

If you.

If you.

If you want to.

It's actually.

It's been quite the tale.

I might actually be the first person in history that has had the patent office accept their patent twice and reject their own own acceptance of that same patent both times.

Tell us more about that.

What happened there?

So a couple of years ago now, I'd gotten a notice of acceptance.

Basically, if you tick the boxes with your examiner, then they issue a notice of acceptance, and then you take your.

The thing that the examiner said was acceptable and the form that says it was acceptable, and you submit those and they give you a patent.

So we got it in aces.

Okay.

My examiner's boss called my attorney and said, you know, I think there's a problem.

We're not going to be honoring that notice of acceptance.

And then they didn't talk to us at all.

Now, the notice of acceptance has a very short expiration date.

So if you don't actually apply for the patent before the expiration date, it's over, it's published, you can never patent it again.

They hadn't officially talked to us at all, so we had the acceptance letter, we put in the application, and the day went past.

Then a couple months later, they sent back something saying, no, you can't have the patent after all.

And here's the reasons why.

I did a deep logical dive on the reasons why.

In algorithmic terms, they were asserting that 1/2.

The supercomputer was using Fortran, the fastest language of actually even today.

And the home computer was using basic, which is long been acknowledged as not a particularly fast language.

But the supercomputer was using a bad sorting algorithm called bubble sort.

And the home computer system was using something known as quicksort, which is called that for an excellent reason.

And the home computer beats the supercomputer at a million objects back in 19.

Really?

Yep.

Wow.

Better algorithms are much, much better than worse algorithms are.

All right, that should be a bumper sticker right there.

It's so true, though.

So it's like the whole, whole algorithm notion, the notion of the algorithm using algorithms in our day to day.

You know, it seems like everything that I touch in technology right now uses an algorithm.

Even, like even putting the television on, you know, different commercials.

I wonder if the commercials that are programmed in there, do they use algorithms?

Is there anything that doesn't use an algorithm at this point?

That's actually part of what made cable systems function and things like if you're watching.

So I think I saw the super bowl last time.

Most of the super bowl ads are, you know, super bowl ads.

They're, they're, you know, whatever the next Marvel thing is and ticket thing that Amazon wants to do and stuff like that.

But a couple of super bowl ads are local ads.

There's two or three spots that come in where whoever, the law office in your town or you know, the, the tire store, whoever does an ad on the Super Bowl.

And the way that works is they block the timeout and then every single local station gets that time slot.

They know when it is and they cut into the feed and, and do their local one.

That's an algorithm.

That's awesome.

That's because that 100% made sense to me.

I followed that 100%.

And that's, and that's really was my goal in taking this interview is because the, the idea of the algorithm, the idea of development, the idea of coding, the idea of anything that's not tangible for some people is so difficult to understand.

And the way you just said, the way you just said that and the way you just summarize what an algorithm is and you've done it multiple times within this interview, I appreciate, appreciate you for that and I hope others do too, because it's a hard thing to understand when you can't hold an algorithm.

You know what I mean?

When you grow up and maybe you just work on a farm.

How do you describe that?

There's so many things that touch our technology and our day to day now through algorithmic ideas and development and it.

And I appreciate you just giving some, some summaries like that so we can drop some knowledge on some folks about algorithms.

Yeah, yeah, so, so we put that package together and we sent them a pretty sharply worded letter, you know, oh,.

Yes, back to the patent.

Sorry.

Yeah, right.

Saying, you know, what the hell?

Basically, yeah, okay, yeah, what happened?

So they said, oh yeah, that's right.

That's insane.

What we said, here, have another notice of acceptance.

And then three weeks after that they said, we're going to withdraw that.

And they sent in the formal thing and they were like, nope, no, no notice of acceptance.

Quality control got a wind of this.

And they said, no, you're not allowed to talk to them.

We can identify them.

We don't understand what they're talking about, so we can't give you a reason for why it's happening, but it's, it's no.

And they said no, and we can't override them.

And that's just what's going on.

All right, okay, so what now?

So what do you do with a patent sue?

So we've, we've got, we've got an appeal going in and, and we'll see, we'll see what the courts say at this point.

But part of the appeal is that they're violating my civil rights.

Because, because their purview doesn't allow them to do this.

They're, you know, that they can, they can object to a patent for a reason.

If somebody else has already had the idea, that's fine.

It's not patentable.

But they've acknowledged that nobody else has had the idea.

If the idea isn't useful, that's fine.

It's not patentable.

But the guy whose job it is to think that it's useful and that guy's boss, they both think it's useful.

If the patents in certain subject areas, then you're not allowed to have the patent.

But there's an entire division at the CME Group that patents market mechanisms on the regular, and they have dozens of market mechanism patents, so they grant market mechanism patents.

So.

And they aren't giving any reasons for why they're doing it.

They're just like, no shadowy figures inside the patent office that you.

You are said no.

Like, literally, that's where I'm at right now.

That's unreal.

And then you have to deep dive and you have to dig, and you probably have 20 phone calls just to get an answer, if you can get an answer.

And I. I think that could be its own podcast.

Is the struggle of getting something patented.

Yeah, well, that.

That.

That's.

That.

Yeah, it is.

It's its own story.

If you want to.

If you want to bring me back and.

And talk, we're gonna need to.

We're gonna need to.

We're gonna need to bring it back on here for part two, because this is the saga that we gotta.

You know, there's just so much to unravel here too, Noah.

What's something that we should let everyone know about when it comes to core disk?

What's something you really want to make sure everyone knows?

I think the most important thing to understand is that the current costs of the existing markets are roughly the same size as the economic growth rate around the world.

And so if my algorithm gets started, works out, and expands, and takes over from these less efficient mechanisms, then it would free up an amount of potential that's roughly equal to the amount of potential that we're currently working with.

And.

And we would start becoming wealthier at basically twice the rate that we've been becoming wealthier for the last couple centuries.

Wow.

That's the real deal.

Noah Healey, everybody.

Noah, thank you so much for taking the time to join us here on the Blind Knowledge podcast.

I'm gonna have to check in because there's so much more that I can actually.

Actually ask about algorithms.

Maybe we can do a part two about.

Especially this patent saga.

I gotta know more.

We all got to know more.

And, sir, thank you for taking the time and coming up with a cool technology.

Y' all go check out Core Disc.

C O O R D I S C dot com.

Noah, it's been a pleasure, sir.

Hope you have a good rest of your night, dude.

Yeah, thanks for having me here, Joey.

It's been a lot of fun.

Likewise.

Likewise, man.

All right, that was Noah Healey.

He is professional algorithm developer and recreational mathematician who has become an expert in game theory and designing marketplaces.

He came up with Core Disks.

C O O R D I S C dot com.

It's commodities exchanges.

It's about tightening up, making it fair.

Algorithms and so much more.

Coredesk.com yo, that was a crazy interview.

I usually.

I have more to say.

I didn't have much to say because I was learning.

And we're gonna need a part two with Noah.

So special.

Shout out to Noah.

Thanks again for joining us, everybody.

Every time.

Everywhere, everyone.

Hope you like the episode.

It was a little different this time.

We're trying to drop some knowledge on y'.

All.

My name is Joey B.