tag:cponeill.posthaven.com,2013:/posts 2021-08-07T11:01:06Z C.P. O'Neill tag:cponeill.posthaven.com,2013:Post/1561814 2020-06-19T19:04:40Z 2021-08-07T11:01:06Z I Don't Write Enough...

... so I think it's time to change that.

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/1494325 2020-01-01T15:30:19Z 2020-01-01T15:30:19Z Twenty Twenty

This past year has seen many interesting developments in the world with some being good and some being bad. One thing that I didn't do so well at was writing more often. In order for that to happen, I need to just sit down and write out what I am thinking (my problem being, when it comes to writing, I over think it to the point of not doing any writing at all.) So to start off the new year and the new decade, I thought it would be a good idea to come up with some forecasts for the following year ahead as well as review the forecasts I had from last year. So without further ado, let's get started.

  • Last year I predicted that the tech backlash or "techlash"  would continue and indeed I was correct. All of the tech CEOs were brought before congress and questioned with some good questions, but mostly bad questions. As it stands right now, the economy is dominated by the giant tech firms and that will continue for the time being. In the past decade, all of their stocks have traded up and made investors happy so nobody has much of an urge to try and stop them. But there are bigger problems brewing with the U.S. presidential election about to pick up steam. The Republicans have remained mostly quiet about techs dominance (mostly quiet, when they aren't complaining about conservative censorship). However, the Democratic presidential hopefuls are skewering tech left and right with some candidates promising to break up the big companies. My forecast is that tech will continue to be skewered in the media and by these presidential hopefuls but not much will change. They are all central to our lives and will remain so for now. (80% probability this will continue)
  • Content Creators are the new thing. Why is this? Because everyone is a creator now and can make money selling what they create. I fall into this category based on the work I do for a living and so I find this trend extremely fascinating and enticing. The internet has enabled anybody with a connection to it to sell a piece of themselves. The content that is being created is a piece of that person. I would like to write more about this but as for now, this is a trend that will continue in the new year and beyond. (95% probability this will continue)
  • The no-code movement is an interesting idea to me. I see it gaining traction in the DTC (direct-to-consumer) space and that makes sense. Founders want to have a working store up and running immediately so they can begin selling their products right away. The app is the store-front, the internet is the distribution, and the customer is on the other end. So by focusing less on building out infrastructure, no-code enables a founder to build their brand and connect with their audience immediately. Where I start to lose interest is in the idea of no-code itself. It still feels like a bland marketing term. (95% probability that founders will continue to build for their audience in any way necessary but a 60% probability that no-code as a marketing term will stick around)
  • Crypto and blockchain are still around but have fallen by the wayside to everyone except for those still directly involved in it. What is interesting about this sector to me is how religious it has become. While the prices are still seeing a bit of a slump and it seems that isn't much interest in it from the mainstream, the industry itself is heads down building. This is a good thing. What has always been needed is less concern of prices and more concern with actually building technology people want to use. There are still some innovative projects being worked on such as Filecoin (IPFS), Blockstack, the DeFi space, and Bitcoin overall. It would be nice to see some new ground made with these projects, but we will see what happens. Both the SEC and the IRS still don't know how to properly work with this new technology and that might be keeping many people out for now. But what has been established over the past few years is the crypto is a viable way to raise resources for a project and business and anyone can do it. That still remains exciting to me so I do hope there are more great things to come for the space. (75% probability the industry will keep pushing forward)
  • There is a U.S. presidential election in 2020 and I don't have any idea who will win, nor will I forecast a winner. What I will forecast is that there will be a hellish year ahead where many of the social networks may be weaponized again and people may or may not get their information from memes. God help us all.
  • Finally, I want to write more, so I will. This is what I wrote last year: "For me personally I am planning on writing more about the role of media in our culture today. This will include everything from music, entertainment, gaming, VR and the like to how media effects our culture in general and the role technology is playing in culture around us. It's my way of helping us find our way forward." It's time to stick to it.


Want proof I wrote this post? Take this key and decrypt it using Keybase.

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/1358711 2019-01-01T22:43:18Z 2019-01-17T22:55:12Z Our Way Forward

A common theme among many people who write essays involving business, economics, tech, and media is that there is usually a year end post where the previous year is analyzed. From there, the upcoming year is forecast in a hopefully illuminating light. This is something I attempted to start a couple of years ago but haven't been consistent with at all. So I thought it would be fun to start it back up again since one of my focuses for the next year is to start sharing more of writing in the digital public. This post is going to be pretty simple; the focus will be on what I think what is likely to happen over the next year. I'm hoping to go back over these at the end of the year and see what part of the analysis was close and what was way off. I'm going to make these as simple as possible. So let's get it started.

  • The security and privacy issues that are a result of the hacking into older companies will probably get worse. We are still in this middle ground between 20th century old industrial thinking and 21st century new information and data thinking and the companies that haven't moved from the former to the latter will suffer the greatest. Why is this? Because these older companies have never had to deal with problems of this scale before and malicious actors will do their best to exploit it to their benefit. What we will see from this is more and more people may start wanting to migrate their data to a place where they control it themselves which brings me to my next bullet point.

  • The crypto space has gone through an intense reckoning over the past year and it may be in for some more this year. I'm not going to forecast the price of any cryptoassets because I really don't know what will happen. What I do think will happen is that more and more of the "projects" and "protocols" that were created in the last couple of years are going to disappear completely. And that is probably a great thing to have happen in the space. The are some legitimate projects that are being built in the space (Blockstack and Mimblewimble come to mind as well as a few others) but there are many more that add very little value. I still think it's early days for crypto and open networks but the kinks are still being worked out. So what I do think will happen is that projects and companies that have built a useful product or service will keep pushing forward and adding value to the space while most others will fizzle out. One big idea that I think will be incredibly useful that can only really be implemented on open networks will be giving consumers the option to control their own data. The majority of the security and privacy issues come down to users not owning their own data. Which brings me to the next bullet point.

  • The techlash is going to continue relentlessly and the aim of that ire will be directed not only at Facebook, but many other tech companies as well. This will be for similar yet very different reasons. Take Facebook for example: The leadership team has had to go from one crisis to the next dealing with Russian interference in U.S. elections, security gripes, and the Cambridge Analytica fiasco. But Facebook isn't the only company dealing with a growing techlash. Googles size, clout, and control over what pops up in its search results (not to mention the algorithmic problems with Youtube), has been making many people uncomfortable and Amazon may start causing even more ire after its incredibly strange search American Idol-esque search for a second head quarters. 

  • AI and automation will be the much bigger story for the economy at large. More and more products will probably have some sort of AI/machine learning algorithms built into them. This will be great for the companies that control and sell those products and services but bad for the jobs that automation will make redundant. The pop culture idea of automation is that there is a robot uprising and suddenly we are at war with the terminator. Sounds very dramatic and al very unlikely. What is more apt to happen is that a piece of AI software will automate some aspect of a business that needed 30 people in a department. Now, that department may only need 2 people and let the rest go. A sort of silent job loss. That is something that I think we will start seeing more of in the future.

  • There are a few other subjects that will probably take center stage and that I would like to dive a bit deeper into understanding are: AI and censorship on the social networks (global communications platforms) and the repercussions of this happening. The subversive hidden networks that are quickly becoming the voice of our culture (an example of these networks can found through hashtags). And of course, the role of technology companies in our society today and how their size, money, and power are effecting everything from politics on the national stage to housing and pricing on the local level.

For me personally I am planning on writing more about the role of media in our culture today. This will include everything from music, entertainment, gaming, VR and the like to how media effects our culture in general and the role technology is playing in culture around us. It's my way of helping us find our way forward.

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/1299519 2018-08-04T14:10:09Z 2018-08-04T21:17:00Z The Chain Files pt 2.
This is a continuation of The Chain Files, a series of essays about blockchain related technologies. For starters, these essays will be focusing on the technical details of Bitcoin and then move into other cryptocurrencies and assets.

Blocks & Header Chains

When transaction data is recorded, it is recorded permanently into what are called blocks. As can be expected, blocks are what make up the blockchain. Each block can be thought of as its own input into a much larger ledger. For example, when Alice sends Bob 15 bitcoin, that transaction is processed by miners and if those transactions are confirmed, they are then recorded to a block. Each block is then organized in a linear sequence over time, this chain of blocks is what makes up the blockchain. As the chain grows longer and longer over time, it becomes much more difficult to reverse any earlier transactions due to Proof-of-Work. 

The main way to identify a block in the blockchain is by using the block header. Block headers are 80 bytes long and calculated by running through the sha256 algorithm twice. The block header hash is not sent through the network but is instead calculated by each node as part of the verification process for each block. Within each block header is the contents of the previous blocks hash. Since changing any input will change the output of the hash, this means that since the header of block 2 contains the hash of block 1's header, block 1's header cannot be changed without breaking the link to block 2. 

Bitcoin requires that that every block header links to the pervious header block which links to the previous header block and so on and so forth until we reach the very first block. This block is called the Genesis block. If a link between a block header and the previous block header cannot be verified, the block is considered invalid and becomes what is known as an orphan block. Because of this, the Proof-of-Work in Bitcoin is cumulative as it builds the chain of blocks connected to the previous block. For example, the Proof-of-Work that is created by the 2nd block header adds to the Proof-of-Work created by the 1st block header. A 3rd block header that links to the 2nd block header then accumulates that Proof-of-Work. These links between blocks are called header chains and are what extend the chain further. Each Bitcoin client looks for the best header chain which is the chain of valid blocks headers that has the greatest amount of cumulative work.

Header chain forks

There are complications that can arise between miners and that is when two miners each create a new and valid header at the same time that both refer to the same header parent. This collision is known as a chain fork and since each header can only refer to one previous header, every Bitcoin miner now needs to choose which of the competing previous headers to use in the next header. When moving onto the next block, the chain that demonstrates the most Proof-of-Work becomes the main chain. It is at this point that the miner who had created the header that loses has wasted their Proof-of-Work and missed out on the bitcoin mining reward.

Short chain forks happen quite a bit in Bitcoin and are usually nothing to worry about. We can take note that much work goes into keeping them from happening to keep the maximum amount of Proof-of-Work added to the headers chain.

Blocks of transactions

Ultimately Bitcoin is a transactional system, although the are no transactions in the block headers. Miners instead collect groups of transactions that are called the block of transactions and then hash them using a specific formula. That hash of the transactions is called a merkle root. The merkle root is included as 32 of the 80 bytes in the block header and together, the block of transactions and their corresponding header are what make up a block. In the previous post, it was pointed out that hashing some input will produce a specific output. So when miners hash the same block of transactions, it will always produce the same hash as the output and proves that the miner chose those particular transactions. And since the block header is protected by Proof-of-Work, it just isn't possible to change and of the transactions that appeared in that block.

As mentioned in the previous paragrah, the hash of the transactions is called a merkle root which is the root hash of what is known as as merkle tree, which is a hash based tree data structure that is a generalization of a hash list. Within the tree structure, each leaf node is a hash of the block of data and each non-leaf node is a hash of it's children. They are primarily used in distributed and peer-to-peer data systems for verification which works out perfectly for the Bitcoin blockchain.

The Bitcoin blockchain is an interesting piece of technology that demands further study and research. From a technical perspective, by combining the power of such data structures and algorithms as the hash function, Proof-of-Work, and the merkle root, we now have a better way to define attribution to digital space. Each transaction is just that, digital space that is located on a block in the ledger. From a more philosophical perspective, we can think of each transaction that takes place as it's own unique digital moment; further proof that an event*** happened at a specific time and there is proof that it took place. Bitcoin itself is more focused on financial transactions which has been a major contributor to its overall security. This is one of the top reasons to study and understand its technical merits.There are other projects focused less on the financial and more on the digital moment. It will be interesting to see how newer projects in the space will build on the technical considerations from Bitcoin and create their own projects that are more focused on the philosophical sense.

*** In this case, we can think of an event in the abstract and it could refer to most anything. A less abstract and more definite way would be to think of an event as something tangible like a digital photo, recorded music, or a piece of art that is assigned to a transaction and can be found in the ledger, easily giving attribution to the owner via their wallet address. 

Want proof I wrote this post? Take this key and decrypt it using Keybase.

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/1302808 2018-07-16T15:42:27Z 2018-08-09T01:38:27Z A Technical Dive into Bitcoin (The Chain Files pt. 1)

I haven't written anything in a long while and have been thinking about this subject lately and figured I would do a little research and get my thoughts down on digital paper. You can read more about it from this old post here.

Bitcoin has been around for almost a decade now and since its arrival, has had an incredible impact within the technology and business communities. When the white paper was released in 2009, the world had just suffered through the 2008 financial collapse and was still dealing with the after effects. Most essays these days focus solely on the supposed business and economic changes that the Bitcoin blockchain will introduce to the world; those changes have not fully materialized yet although there are many companies, governments, and individuals around the world doing their best to make that happen. However, in it's entire run, the security of Bitcoin has not been compromised. There have been hacks, but those are mainly due to third party vendors such as exchanges making mistakes. The mathematics and technology underpinning Bitcoin itself still runs securely. That made me more curious about it and ultimately led me to the conclusion to learn about it. I'm hoping that this essay will explain some, but not all, of the basic technical underpinning of Bitcoin. So let's get started.

Hash Functions

One of the data types that is used most frequently in Bitcoin is the cryptographic hash or hash function. A hash function takes an input of some arbitrary amount of data (it can be any amount) and turns it into a fixed-length string or byte array. No matter how large the input data is, the output data will always be the same length. However, every time you hash different data, even if the same function is being used, a completely new hash is created.  Sometimes you will see the cryptographic hash referred to simply as "hashes" since there are non-cryptographic hashes that don't have the same security properties. 

The following examples below show how hashing can be used to verify documents with text, photos, or other forms of multimedia. I am using text only for an easy to understand example.

## Run the following commands in a linux terminal
1) echo "this is our secret message. 'the rain in spain falls mainly in seattle'" | sha256sum
   ab9c98ed0d99f6cf8b7f2a8d2e96a1526aae7bf084fcca87edd41029e3adce9e  -
2) echo "this is our secret message. 'the rain in spain falls mainly in seattle'" > hash.txt
3) cat hash.txt | sha256sum
   ab9c98ed0d99f6cf8b7f2a8d2e96a1526aae7bf084fcca87edd41029e3adce9e  -

Running the echo command along with the given text and the sha256 algorithm, we see the output is a string of 64 characters that looks absolutely random. However, both end up being the same string of 64 characters. This is an extremely important part of hashing as it can be used for verification. In this case we have used it for a simple line of text; but it can be used to verify other forms of documentation and identification now. There are a fair amount of companies that are in the first stages of building an identification layer using blockchains and hashing is an important characteristic of how they will function.


So how do hash functions work within Bitcoin? In order to understand this better we have to travel back to 1997, when cryptographer Adam Back created a technology called HashCash which pioneered something called Proof-of-Work (PoW). HashCash used PoW to limit email spam and denial-of-service attacks and it did so by requiring a selected amount of work to compute with a proof that could be verified efficiently. The algorithm was later slightly tweaked by Hal Finney for his bitcoin precursor project as a way to mine coins. But how does it work within the Bitcoin network? In order to understand this we need to understand the technology that is causing the biggest hype these days, the blockchain.

The Bitcoin blockchain is a ledger that contains the entire record of bitcoin transactions that have taken place since the original genesis block. All of these records are arranged in a sequence of "blocks" in such a way that no user can spend their holdings twice. This record is also public and anyone can verify that these transactions have taken place if they have a specific wallet address or hash. Each block references the hash of the previous block and creates a "chain" to all previous blocks back to the genesis block. These blocks are also computationally impractical to modify because each proceeding block would then have to be regenerated as well, creating completely different hashes. This helped solve the double-spend problem of previous digital currencies and has applications beyond money as well such as identity and intellectual property. 

Hash functions and the Proof-of-Work algorithm make it extremely difficult to alter the blockchain which has brought us to where we are today and helps explain why the idea behind a blockchain has turned into such a buzzworthy business focus. Trying to do so would require that all subsequent blocks are re-mined. Doing this is difficult though since any attempt to monopolize the networks computing power would be expensive due to the machinery need to complete the hash functions.


One fascinating use case of a hash function within the Bitcoin protocol is that of the OP_RETURN, a script opcode that is used to mark a transaction output as invalid. Although the protocol caused a bit of an uproar when first announced due to many in the community believing that it was irresponsible since Bitcoins use case was for financial applications and adding this made it a record for arbitrary data, the proposal was eventually accepted. This was the first use case for a digital asset proof-of-ownership and there have been quite a few use cases and apps created since its release, most notably, proofofexistence.

Although the hash function has a been a critical component for cryptography and security software in general, it has started to become a subject more people are learning due to so many newcomers to the cryptocurrency industry. This is a good thing in my opinion for two reasons; first, more people with a better understanding of the security underpinnings of technology will hopefully lead to a demanding of more robust software being built in the future, whether it is based in the cryptocurrency space or not. Second, aspiring developers who are taking what they learn and applying it are also building those security standards into their applications. I know that many people could argue back that this idea is more hopeful than anything, especially with so many hacks taking place among large companies and governments, but only time will tell. We are still in the early stages of attempts by many companies and teams to bring this technology to the mainstream and it is exciting to see many of the results.

Want proof I wrote this post? Take this key and decrypt it using Keybase.

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/1143325 2017-04-09T20:40:34Z 2018-01-16T17:49:44Z Decentralization

At the moment there is a huge debate raging in the Bitcoin community on whether or not there should be a hard fork into two different currencies along the same lines as what happened to Ethereum in 2016. As can be imagined, the debate is intense with many folks taking extreme sides. My goal for this essay is not to weigh in on that battle as my technical understanding of Bitcoin is limited in this debate compared to many of the developers and other folks who are staking their businesses and focus on it. But there is another idea that is constantly springing up in many of these debates and that is this idea of decentralization and more importantly, building decentralized applications. So why is the concept of building decentralized applications gaining prominence?

First, there are three types of ways that applications can be built: centralized, decentralized, and distributed. The majority of general applications being built are centralized, that is, there is a unique core node that must be used in order to access what the app is offering (be it data, an API, or your account) and the core node instructs all of the connected nodes as to what to do. If we take a step back and analyze this idea we can see that all information being produced will flow through a single center (or node). Every person who uses these services is dependent directly on this central authority maintaining the power to send and receive information. Google, LinkedIn, Facebook, and Amazon are all built on centralized stacks and this design works powerfully for them both technologically and business wise. 

Then there are distributed and decentralized applications. A distributed system means that computation is spread across a network of multiple nodes which helps speed up computing and latency of data access. A company like Google builds distributed software to help speed up their services. A decentralized application means that there is no central node that instructs the other nodes on what to do. Bitcoin is the ultimate decentralized application because if one node fails, it will not have an effect on any of the other nodes and the network will continue to operate. For the purposes of this essay, I am going to skip going through anymore detail on distributed systems. 

So why should we care about decentralized systems/applications especially since centralized systems already work so well for these companies already? For one, there is a rather large possibility that these applications will be used for their superior incentive structure, resiliency, transparency, and distributed nature. Using a blockchain (peer-to-peer distributed ledger) to a form a trustless system, value can be created using cryptographic tokens, which can then be used to access the application. As I stated in the previous paragraph, the premier decentralized app at the moment is Bitcoin (and this could very well change down the road) which simplifies the traditional financial system. In order to access the network, one must own some bitcoin, which can then be used to store value, or easily transfer it from one wallet address to another. For example, cross-border payments are made easily since the value isn't being transferred through several financial middleman.

Another way that decentralized applications are being built is as protocols that use another blockchain, such as Ethereum, and issue their own tokens to function. One interesting example is the Golem Project; it lets users access another users computer using their tokens as the exchange of value. For example, if I set up some spare computers and put them on the Golem network, anyone with Golem tokens can use my computers in exchange for those tokens. We suddenly have a way to put our spare CPU's to work. This has been an idea I have thought about considerably using bitcoin as the exchange of value instead. Either way would work well and put spare computers to work.

But let's not get ahead of ourselves quite yet. Centralized services still absolutely dominate the vast amount of users and will continue to do so over the coming years. It may not even be until innovation begins to slow down or companies begin having other problems that will eat up their time (this could be anything from internal problems to governments/states coming down hard on them). If this does happen, then decentralized systems built on blockchains could start becoming more well known as easier and stable computing platforms. In fact, I am thoroughly convinced that these systems will be incredibly important for businesses, customers, and citizens. But it will be a long road before we get there successfully and my goal is help pave it along the way.

You can make sure that the author wrote this post by copy-pasting this signature into this Keybase page and decrypt it for proof.

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/1114441 2017-04-02T03:59:59Z 2019-05-08T23:00:37Z HTTP vs HTTPS

The Basics


Security is now increasingly important as better online experiences now involve trusted third parties and good encryption. A basic understanding of how this works is knowing the difference between HTTP and HTTPS.

Hypertext Transfer Protocol (HTTP) is the system used for sending and receiving information across the internet. It's what is known as an "application layer protocol" so its main focus is on how information is presented to the user. This option doesn't care how data gets from point A to point B and it is also "stateless" which means that it doesn't remember anything about the previous web session. There is a benefit to being stateless which is that there is less data to send meaning there is increased speed. 

The most common use for HTTP is to access HTML pages, which are the backbone of the websites we visit on the internet. However, it is important to remember that other resources can be accessed and utilized through HTTP as well. In fact, this is the most common way that websites that do not house confidential information (such as credit cards and/or usernames and passwords) are setup.


Secure Hypertext Transfer Protocol (HTTPS) is for all intents and purposes, a similar system used for sending and receiving information across the the internet, it's just the secure version. The protocol was developed to allow for secure authorization and transactions. We don't want malicious actors gaining access to the private information we are creating and HTTPS adds an extra layer of security to that exchange of confidential information. That extra layer is made possible because it uses a Secure Socket Layer/Transport Layer Security (SSL/TLS) to move data back and forth. Neither protocol cares how the data gets to its destination although HTTP cares about what the data looks like whereas HTTPS does not.

Google actually prefers websites are encrypted with HTTPS because of that guarantee of extra security. When a business owner, developer, or webmaster goes through the motions of obtaining a certificate, the issuer then becomes a trusted third party. The information in the certificate is used to verify that site is what it claims to be and finally the user/customer that knows the difference between HTTP and HTTPS can by buy with confidence, giving electronic commerce more credibility. For anyone maintaining a site with heavy traffic, Google and the other search engines will put priority on sites with security and keep them boosted in the rankings as long as the multitude of other SEO related work follows their guidelines.

More Detail

Data sent using HTTPS is secured using via the Transport Layer Socket protocol (TLS) which provides three important layers of protection: 

  1. Data Integrity - Data that cannot be modified or corrupted during transfer without being detected.
  2. Encryption - Encrypting the exchange data to keep it secure.
  3. Authentication - Proves that the sites users/customers communicate with the intended site.

These three layers are the main motivation behind the HTTPS protocol and help prevent against eaves dropping and tampering with the communicated content via man-in-the-middle (MITM) attacks. 

How do browsers know who to trust?

Browsers come pre-installed with certificate authorities, meaning they know who to trust. Likewise, the browser software is trusting those authorities will provide valid certificates. A user/customer should be able to trust an HTTPS connection provided the following are all true:

  • Trust that the browser software correctly implements HTTPS with the correct pre-installed certificates.
  • Trust that the certificate authority will vouch only for legitimate websites.
  • The website provides a valid certificate signed by a trusted authority.
  • The certificate correctly identifies the website.
  • The user/customer trusts the protocols encryption layer (SSL/TLS) is secure against eavesdroppers.

It is becoming increasingly important to use HTTPs over insecure networks such as public WIFI since anyone one the same local network can discover sensitive information using packet sniffing. The same goes for using WLAN networks which can engage in packet injection to serve their own ads on webpages. Doing this can be exploited in many ways such as injecting malware onto those webpages to steal users' data and private information.

The case for using HTTPS on your own websites

With each day it seems we learn that more and more information about global mass surveillance and data being stolen by malicious actors. Because of this, the strongest case to use HTTPS is that you are making your website more secure. There are however limits to using HTTPS as it is not 100% secure. It will not prevent your website from getting hacked or stop phishing emails getting sent either. It's importance is in the fact that if you have users/customers that are logging in with sensitive information (such as passwords, social security, etc.), then setting up HTTPS is the absolute minimum price and precaution that should be taken in order to protect them. And with security, you will build trust.

You can make sure that the author wrote this post by copy-pasting this signature into this Keybase page and decrypt it for proof.

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/1082560 2016-08-19T19:08:34Z 2016-08-19T19:08:35Z The Blockchain Meets Seattle

Earlier this week I had the opportunity to be invited to a private event here in Seattle regarding Blockchain technology. The even itself took place on he 48th floor in the old Washington Mutual Tower. The fog disrupted what might have been a beautiful view. The event itself went by rather quickly but it did a great job of explaining how the Blockchain technology can be leveraged in more ways than just creating the killer app "Bitcoin". Some interesting points that were brought up were:

  • Using the Blockchain as a ledger to keep track of IP, land titles, and other assets
  • Goldman Sachs has filed a patent for crypto-security settlement on a blockchain.
  • Microsoft has declared that the Blockchain is one of the "key must win workloads" for their Azure cloud platform and business. They are also collaborating with major U.S. banks using the technology. 
  • Some governments are already beginning to invest in their own local, or private, blockchains.

What was most surprising to me was the actual plans and partners Microsoft has for their Blockchain-as-a-service (BaaS) tech they have on their Azure cloud platform. Some of those partners include:

  • Bitpay
  • Multichain
  • OpenChain
  • Coinprism
  • Augur
  • Slock.it
  • Ripple

Putting tech like this in the hands of developers easily will be a huge factor in spreading it far and wide. The event was exciting to me as I was able to speak to a number of people about an area in tech that I have been involved with for a few years now and it also showed how many people are interested in learning more. I hope to be a part of those bringing this technology to the masses over the upcoming years. 

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/1057204 2016-05-29T20:14:50Z 2016-06-29T19:11:46Z Yesterday

"It's no use going back to yesterday, because I was a different person then."

- Lewis CarrolI

Life has changed quite a bit for me and I am ready to get back in to writing again. I miss it terribly and have made the decision to start posting some of the ideas rattling through my mind as of late. Writing really is the best way for me to clarify some of my thinking.

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/974387 2016-01-20T01:41:21Z 2016-01-21T01:04:05Z Looking Forward

As is standard practice in the New Year, many people enjoy writing about what they think will happen in the coming year ahead. It’s not something I’ve ever done before and so instead of just quickly throwing together a quick list and publishing it, I thought it would be better to look at a few important developments taking place in the world and see where they may be heading. So here are my thoughts on the four spaces that could have an impact on the global economy.

  • Emerging markets are going to go through quite a bit of pain due to the price of many commodities falling to earth. China seems to be going through a bit of turmoil within its economy which can have a negative ripple effect across the global economy as a whole. The country is beginning to really experience a huge amount of pain brought on from the large debt that has been built up in some of its industries. For example, since the price of steel and iron ore have fallen so much since early 2015, the construction boom has slowed down and claimed many jobs in Europe. In response, fiscal and monetary stimulus will be used as best it can in order to help bring back demand. Doing this could hinder the exchange rate for yuan which is already feeling a large amount of pressure from the outflow of capital. If the yuan is benchmarked against a basket of currencies, its value could decline which could lead to a serious erosion of value in market valuations in much of Asia. What should be watched for is just how much China’s economy and market movements effect the rest of the worlds markets. As China grows bigger, the countries presence will begin to be felt in countries that it has invested in or have invested in it, for better or for worse.

  • Bitcoins presence will also begin to be felt in both currency markets and some products and services in the financial industry. It is the easiest way to store and send monetary value using the internet and the bigger it gets, the more people will learn about it. Many venture capitalists and industry players still talk about finding that killer app that will bring it further into the mainstream, but it still may be slow going on that front. Many banks and financial institutions including Visa and Goldman Sachs are starting to use the underlying technology, called the Blockchain, with the help of many fintech startups that have built a product or service that these companies will find useful. The use of technology by these companies will help push it towards a larger consumer base to use. There should also be more massive market movements expected for two reasons: 1) the community is still divided over the block size and (with a major core developer leaving rather loudly) and 2) its presence has been felt in China with the price popping when there are problems with the market and traders reacting by quickly moving resources in Bitcoin. Big things could make some headway this year provided there are some creative uses of the technology and the community does not remain split.

  • Security and privacy will remain on everybody’s radar as important subjects to understand what is going in. Security, already a huge geopolitical issue, will become a subject that effects more and more people as technology rapidly advances. We walk around with devices that can be tracked at all seconds of the day so it should be fully expected that more security related services and products will make their way to the market. Nobody wants their phone hacked into since it carries such a great amount of details about our lives and there will be many companies that answer that concern. But the other side of that coin is the subject of privacy; a slippery slope due to so many differing opinions of how to define privacy. Right now, most people don’t understand cryptography or the mathematics behind what makes it work; nor do they know to what extent companies and states can track their every move and search query and place all that information into a large context about our lives. On the business side, in the case of retail and advertising, I side with giving the consumer the choice of whether to do or don’t want to be tracked and sold to. If they do, to what level of privacy are they willing to forgo their privacy? And in the case that consumers aren’t given that choice, tools should be made available for anyone to use to give them the privacy they expect. And in the case of security, companies and states should work together to come to a middle ground that gives both what they need. This being the year of a presidential election, it should be expected to hear this subject brought up quite a bit.

  • Lastly, Artificial Intelligence (or machine intelligence) will become a bigger part of our lives; probably even in ways that we aren’t fully expecting to happen. But where it will remain most important to us will be in our working lives. As more and more rote work is able to be automated easily by computers, we will find them working by our side and helping us achieve things we didn’t thing possible to an even greater degree than they already are. The use of machine learning will help with completing sets of tasks and jobs more efficiently. Large tech companies like Google, Facebook, Baidu, Alibaba, Amazon, and Microsoft will help pave the way to a greater use of AI by making their products more intelligent and useful. These same companies will also go a step further (some already have) by open sourcing their code so that more researchers, smaller companies, and independent developers will have access to the same tool sets, thereby producing new products and creating a healthy ecosystem. It can even be thought that these same smaller companies or products will be purchased by the larger players and seamlessly integrated since the underlying technology will be the same. It’s exciting to think about what kinds of new products can be made.

These four developments will have a large impact on the global economy and marketplace whether positive (AI related products, Bitcoin, Privacy) or negative (Commodity prices crashing, Security) and it’s interesting to think about what lays ahead for us. There are a few developments I left out that will definitely have a large impact on the world and those include the price of oil ($28), augmented and virtual reality, and a U.S. Presidential race. Should be an interesting year that lays ahead.

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/965514 2016-01-07T04:24:48Z 2016-01-07T19:52:03Z The Prediction Business

Businesses are in the prediction and risk business, a bold statement if there ever was one. No matter what, when running a business, there are going to be things that are known and things that are unknown. We can control the things that are known to us and act on them in clear ways. But the unknowns are a bit trickier because they are unknown. We just don’t know what they are there. For example we don’t know how our customers or the market are going to respond to a new product. Or if we should be doing a heavy amount of fundraising based on the customer growth curve and whether or not we have sustainable operations to continue heading down this path. This is the risky part of the business.

In order to better understand how to predict these variables, more companies should be collecting as much data as they can about their businesses and understand it the best they can. Data is the 21st century oil. Reading it is the 21st century literacy. Big Data is what the press is calling it. Almost all companies collect data now and because of this, it is much better to understand how it works, what is the information is trying to convey, and how the information can be used to make a forecast, tell a story, or just inform. General statistics has always been used to mine and learn from data in the past. But with faster computer speeds and access to an almost unlimited bandwidth, it is now much easier to run much more advanced algorithms over the data using what is known as machine learning. What is machine learning? It is applying statistical models to the data you or your company has so that smarter predictions can be made about the data you don’t have.

With each new set of data we encounter, new uses for algorithms must be found. And while machine learning has a great amount of potential to the way companies approach their business problems and the way entire industries operate, it can still be thought of as a branch of statistics that is to be used on big data. And the tools that machine learning bring are designed to make better use of that data.

How can we approach thinking about big data and machine learning along with it? The enormous scale of data available to firms can be challenging; using machine learning is as much about data analysis as it is about adapting to the sheer size of any particular data set. A great way to think about data is how long and wide it is. What is meant by this statement is our data set will be long depending on how many rows it happens to have. Let’s say we are analyzing a large company’s data and what it will look like; we can imagine each row being one unique customer. Depending on the size of the company there could be up to millions or even billions of customers. So with that line of thought, the more customers there are, the longer or higher our data set will be. Width then corresponds to the number of columns in the data set. So in our case, each column is considered a unique variable assigned to our customers. For example, our columns can be purchase and browser history, mouse clicks, and even text. This data set can become rather large and overbearing and this is where machine learning makes use of a tool set to better analyze wide data.

We can further refine our initial question down further by asking what machine learning is used for? The most common application is to make predictions and this is why it is becoming so important to businesses. Being able to make predictions about data that isn’t available can be used to formulate sales, marketing, operations, and financial strategy. Here are a few examples of how it is used in industry today:

  • Personalized recommendations for each customer. (Amazon product recommendations, Spotify and Pandora recommending new music, and Netflix movie recommendations)
  • Forecasting customer loyalty (How often they shop with a company down to the time and what they consistently spend their money on.)
  •  Fraud detection and credit card risk (More banks and insurance companies are using their data to make predictions about what customers may be a moral hazard)
  • Facial recognition software (Facebook makes great use of this when it recommends who should be tagged in a photo)
  •  Advertisements that create their own copy and images (M&C Saatchi partnered with Clear Channel UK and company called Postercope to create these ads.)
  • Personalized assistants (Apple’s Siri, Google’s Now, and Microsoft’s Cortana are just the big name examples of what can be accomplished. There will be many, better assistants down the road.

The common identifier is the need for a unique business process and the decision that must be acted upon to get to that accurate prediction. Each of these examples come from complex environments where a correct decision depends on many different variables. (Our wide data). And each prediction will ultimately lead to an outcome with whatever it is helping the model become continuously better.The business value of machine learning is enormous even with its limitations are taken into consideration. It is focused on prediction which means the model of the environment might be all that is needed to make the right decision.

So let’s get into how machine learning can be used in practice. Within each machine learning algorithm there are generally three broad concepts. They are:

  • Feature Extraction: This determines what data to use in the model.
  • Regularization: Used to determine how the data are weighted.
  • Cross-Validation: Tests the accuracy of the model.

What each of these concepts does is separate the “signal” from the “noise” which is common in most every data set and helps sort through the mix to get to better predictions.

Feature extraction is the process where the variables that the model will use are discovered. There are times where all features are dumped in to a model and used but more often than not this doesn’t happen due to overfitting. Features help aggregate important signals that are spread out over the data. For example, if your company runs an online music store, each feature could correspond to musical genre, record label, or even the artist’s home county. Once these data points are collected they are combined through automation that clusters the features together and the model can then analyze customer predictions. A very well-known business case is Netflix’s movie recommendation algorithm. The more each customer uses their product, the more data points they are able to collect about that user and the company is better able to predict what movie or television show the customer is interested in watching.

After we have our features chosen we must understand if the data we have been collection and what it is being combined reflects a signal or noise. So we begin by playing it safe with the model using regularization. This is a way to split the difference between a flexible model and a conservative model. For example, one effect is known as “selection” which happens when the models algorithm focuses on a smaller number of features that contain the best signal, discarding all other features. Regularization helps the model stay away from overfitting, (overfitting is when a model learns patterns from the data that ultimately are not helpful and won’t hold up in future cases) and helps it learn from both signal and noise.

In order to test the accuracy of the models predictions, a process is used called cross-validation. To test that the model is “out-of-sample”, which is when predictions are made on data we don’t have based on data we do have, our initial definition of machine learning. This is done by splitting the data into two sets called the training and test data. The model is first built using the training data and then more tests are done with the use of the test data. Keeping a clear partition between the two sets is instrumental in not over estimating how good the model actually is.  

There are many examples of machine learning being used in production that we use on a daily basis. In some cases, we might not even be aware that our technology is using it in the background. Netflix was used as an example of a business that makes great use of its data. Amazon is also extremely data driven with their product recommendations being used skillfully with each customer who shops with them. However, the company that probably uses machine learning the most right now is Alphabet, Inc. (The Company formerly known as Google.) Machine learning not only guides how their search engine works so efficiently, but is also used in Google Translate, Nest, their self-driving cars, Google Now, and many other products they offer. The more data they collect from us, the better they will be able to fine tune their algorithms in their products so that they will interact with us seamlessly.

One final intriguing example is how a digital agency is using artificial intelligence to create ‘self-writing’ campaigns in London. How it works is the ad itself is placed on a bus stop and has a camera connected to it. This camera registers commuters’ engagement based on whether they look happy, sad, or neutral. Then, an algorithm executes various responses based on the commuters’ responses to the ad. This campaign in particular only used a fake coffee brand since it was more of a test than anything. But if the proof of concept works, we may start seeing more interactive billboards out and about.

Being able to make the correct forecast and predictions for your company isn’t something that can be done with 100% accuracy, but businesses that do utilize the data they are collecting to its fullest potential find they are better able to cope with the uncertainty of variables they can control. Forecasting isn’t about getting the answer to your questions correct, because that isn’t going to happen. Forecasting is about being able to make sound judgement from the data and the algorithms used to mine that data will help anybody or business get a bit closer to the answers they are looking to find.

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/915202 2015-10-12T20:58:57Z 2017-04-14T02:49:05Z The Creation and Capture of Value

In order for a business to create value after cost it must create and distribute that value in the most efficient way possible. It can be considered the starting point for any and all businesses which leads to our first question to think about: how is that value created? Simply put, it is created through work. That work could be anything from administrative tasks (such as filling out the right paper work for customer orders), technical (deploying code to servers), and creative (marketing copy, product and/or logo design, etc.). The business can then create value through that work, sell or trade it to a customer base, and capture some of that value through profit. Based on this definition, we can begin to clearly see that businesses add value in more ways than just making a product and selling it. Every moving piece of that business should be moving towards the end goal of creating and capturing that value.

Now let’s start thinking about the types of value that can be created. First, let’s remember that not all types of value are created equal by any means. A value that is considered a commodity is easily replaceable in the minds of customers. For example, if your products aren’t distinguishable from your competitors, then that competitor will be primed to take your place should your business falter by any means. There are ways around this though; if your company is able to create a new and more efficient process for doing business or in possession of unique skills focused on the creation of value, then you will be able to more readily differentiate from those trying to eat your lunch. Having any of these things is a competitive advantage and should be fully utilized towards the goal of value creation.

Measuring value creation is important if only so that you understand what the value is that your business is making. The first method is by measuring revenue. Revenue tells you that the way your business creates value was worthwhile to your customer base since they are willing to pay for it. Notice how I didn’t say profit. Many businesses successfully create revenues but no profits (think Amazon), but many will not be able to do this for very long. A business needs a profit in order to survive and sooner or later a lack of making any profits will bring that business down, no matter how great the product or service. (Amazon has been able to successfully navigate this pasture by shrewdly reinvesting its revenues into future initiatives such as AWS.)

Then there is perceived and exchange value, which are interrelated. The exchange value is straightforward in that it is the amount of value exchanged between a buyer and seller for a product or service. For example, if you go to the store and purchase a pair of shoes, the price you pay for those shoes is the exchange value.  The perceived value is defined by our perceptions of usefulness of the product or service. In economics there is a consumer surplus (C.S.); when the consumer surplus is greater than zero (C.S. > 0), then the customer is better off making a purchase than not. So value is created when the perceived value of a product or service has a certain degree of usefulness, consumer surplus is greater than zero (C.S. > 0) and that same value is then exchanged to the seller.  

As we can see, it’s incredibly important for a business to create value. It won’t survive for very long if it doesn’t create value by differentiating itself in the marketplace or not making a profit in the long run. In order for a business to survive, it must capture a portion of that value it is creating. If businesses ultimately want to succeed, they must think clearly about how they are going to capture the value they are creating. Businesses that don’t do this may be leaving money on the table.

There are a number of different ways to capture value with some being more common than others. For instance, price based on value changes according to the offerings worth to the customer. What this means is businesses don’t set their prices based on what their competitors are doing. They might also discontinue the process of marking up prices based on production costs. Instead, what they are doing is looking at what their customers want and setting their prices accordingly.  What is important is that the customers’ perception of value must be discovered. This is obviously different with each customer but there are different models of discovering this missing piece. An obvious example is auctioning, which doesn’t work with all business arrangements but is incredibly effective when it does. The most common example of an auction is with online advertising, where each buyer sets the price and the seller can choose whether or not to take that offer. Of course this is controlled less and less by human interaction and is guided more efficient using algorithms to guide the software. There are some downsides such as prices that may be less than satisfactory for the seller, but must be honored regardless.

Another model is known as demand-driven pricing; what this does is let the price change due to the fluctuations in demand for a product or service. The most common example to date is Uber and it’s constantly changing prices. Although there can be a large amount of complaints if the price is too high, there are reasons for it, and Uber acts on those reasons with a mechanical efficiency. The business is a money making machine. How it works is the company raises and lowers its prices based on demand for rides. Other factors that are taken could be day of the week, whether it is a holiday or not, weather, and even city, etc. Using these variables (and many, many others), the company can maximize its profitability by knowing how many cars to have on the road and in what location at any given time. Demand-driven pricing at its finest.

A further form of value capture model enables customers to set their own prices. Although this doesn’t take place in most industries, it is especially prevalent in the travel industry where buyers can decide what price they want to pay and sellers can take it or leave it. Unlike auctions though, this transaction is mostly kept between the buyers and sellers. Doing this lets the seller maintain their prices even if they are discounting for incremental sales.

Next up is for companies to capture value by using two-sided market forces to their advantage. Although the name may not ring a bell to most people, it is a model everyone has seen in action and is used efficiently by media companies. For example, many publications are free for the general public to take (think local periodicals in many major cities) and they make their money, in turn, by charging advertisers to place ads in their publications. The money is then used to subsidize the content that is created for the publications and the process is continued. Although Vice Media is a much larger entity now, when it was just a magazine, it was free to anyone (who could find it) and the vast amount of value was captured by charging advertisers (especially American Apparel). Of course they made money with subscriptions, but most people just searched high and low for a free copy.

Then there are those businesses that use what’s known as the “price carrier” in their offerings. This is the experience that businesses will hang a price tag on while customers may not be coming through the doors just for that in the first place; they may be coming for something else entirely. Think about it, we will sometimes purchase a product or service to gain access to something else the company is offering, but has no price tag on. Starbucks is a great example of this. The majority of their customers walk through their doors for a quick coffee before work in morning, but there are those people who come in just for the wi-fi. Using it isn’t for sale, but purchasing a coffee or pastry will give you access to it. So a good question to ask yourself is whether or not your business is hanging the price tag in the right place. What would happen if you moved it to something else?

One of the most prominent examples of value capture is called the razor-and-blades model, which was pioneered by Gillette. It’s a rather simple model: customers get the razor handle for free, but must purchase blades continuously since they get dull rather easily. And charging for the blades is where the value capture happens. This model was also utilized by technology companies who sold printers and printer ink replacement cartridges. The printers are always cheaply priced; the ink is not.

The final model to talk about is one we all know well since the phone carriers are incredibly efficient at using it; it is known as bundling. How it works is the price of the new phone is subsidized by any extra hardware, software, and data features we purchase with it. The phone is cheap; the options that are bundled with the purchase are where the value capture takes place. Car dealerships also excel at this too; when you go to purchase a car, the sales people will usually bundle in many products or services you may or may not even need since those add-ons are where the money is then made.

Both value creation and value capture are incredibly important to keep your business running smoothly over a long period of time; more so than your competitors. Both are equally important; both need to be studied rigorously for the best understanding of how the complement one another. Value creation is the work that a business needs in order to create value to offer to their current and potential customer base; capturing that value is what will keep customers happy and the business running smoothly. Combining the two will be what ultimately either keeps a business alive. And that is something to constantly been thinking about when focusing on your own business.

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/896032 2015-09-15T01:58:03Z 2015-09-15T01:58:03Z Broken Windows

Technology is changing a large aspect of how we live our daily lives without giving us the time to slow down and catch our collective breath. This aspect of technological change should be enough to make anyone pause, but there doesn't seem to be any end in sight. This isn't a negative, it's just what it is at the moment. I'm also not saying that this is a good or a bad thing; it just is. Where my concerns are starting to focus on is what the trade-offs we may not be paying attention to are. Technological change is a Faustian bargain and it seems to be happening before our eyes without an consideration to the trade-offs of living our lives around it.

What am I talking about regarding trade-offs? Technology gives and technology takes; there is always a hidden price to pay for letting it become the centerpiece of our lives. This isn't at all obvious to most people. For most, the price they pay may be of greater importance than the trade-off. For example, take Facebook. In order to use the service we must sign up and agree to the terms before we can begin using it. That agreement is the price right there; we are agreeing to let Facebook do whatever they want with the data that they collect about us. And there are a variety of resources they use that data for such as marketing and advertising, making their product stickier to customers, and user experiments. The ultimate price we pay is our online privacy. I'm not condemning or condoning it; again, it's just what it is.

Online services aren't the only technologies with major trade-offs. Another example is smart-phones and their now ubiquitous nature. They are fantastic devices that many people in the world use on a daily basis, but at what price? How difficult is it to have a conversation with someone without them staring at their phone in the middle of you talking with them? How has it changed our social interactions with other people, and is that a good thing? Automobiles are another example; we were suddenly able to get somewhere faster and more conveniently than ever before and that revolutionized the start of the 20th century due to not only how they were made (Ford lines) but to how it gave people the ability to travel easily. Now we know that they are responsible for contributing to air pollution and traffic gridlock. They have done an incredible amount of damage to our environment and that will not be easy to fix, if it ever can be.

Technological change is our modern day version of Bastiat's Broken Window Fallacy. We buy into a new technology and can only see one side of the benefits. For example, we might start using an app that hires people to deliver us goods (such as food or clothes) and/or services (such as taxis). By doing so, we revel in the new found freedom from the convenience it provides our lives and the time it now affords us, allowing anyone to now focus on much more important parts of their lives. But this is only one side of the story. We aren't opening our eyes or even looking for the trade-offs that are taking place. And this needs to change.

Very rarely do we ask what this technology is undoing for us. What is the other side to that trade-off? How does using this new technology effect the people who are working for it (such as drivers for Uber or deliveries from Postmates). Are the laws being changed to accommodate these new companies and should they even be changed in the first place? What will be the long term effects of those changes to our economy and even our own jobs? There are some very interesting critiques of both Postmates and Uber.

These are all questions that are difficult to answer but the very real point that I am arguing about in this essay is that most of us know very little about the social and psychological effects that new technologies have on our society. And it's in our best interest to really start thinking about, and understanding the costs that these technologies bring to us. 

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/825552 2015-06-10T22:46:46Z 2019-05-09T15:47:24Z Predicting through Randomness

I wrote this back in January of 2015. I wanted to wait until closer to the end of the tournament until I posted it. What I found interesting about it was that although I didn't answer all of the questions, those questions I did answer gave me a fairly low brier score, which is used to show how accurate the forecast happened to be. Sticking with financial markets and indexes seems to suit me as there is plenty of data available and I have zero issues with knowing that I am completely wrong and need to quickly change my thinking. Political campaigns we're a bit harder to tackle and are not something I would want to even try to forecast. In the end though, forecasting is a great tool to have in the toolkit, but really if it's understood by the user. 

7/3/15 - Just received my feedback report and I placed in the top 5-6% of the tournament, which is on the edge of superforecaster territory. The tournament was an amazing amount of fun but what really struck me was how much I don't know about anything. Still, the experience was great.

Forecasting is hard and there is no way around it. When you are trying to forecast the outcome of a certain idea or situation, it is nothing but back and forth with data points and constantly digging to try and find the right answer. Simply put, there is no right answer because the data is constantly in flux. There are people who make entire careers out of forecasting, some more on force of personality (think political pundits) than actual hard analysis, and some on actual deep dive analysis (those with an incentive to find the answer, think investors). I've been thinking about forecasting now for the past year since I was accepted into the Good Judgement Project tournament and it has been a fantastic way to learn about what is happening in the world around us and quickly become humbled by what we know and think we know. Forecasting, if done correctly, forces you to look hard at the reality of what is happening and make judgments from there. And many times, we have to redo what we were thinking about. 

For a quick history lesson, the Good Judgement Project was started in 2011 by Philip Tetlock and funding from IARPA. Over the course of the next few years, the researchers found that accurate training plus a strong dose of statistics, pyschology, game theory, and interactions among team members helped create highly accurate forecasters. These people were not government trained at all. Most of them were only getting their news from online sources. The same same sources that most anyone has access too. However, these amateur forecasters were displaying a 30% more accurate prediction than those in the intelligence community with access to classified information. I thought this was pretty exciting and applied quickly thinking that it would be great way to finally apply some of the game and probability theory I learned in college. But that wasn't the only reason I wanted to join. 

Thanks to the influx of more technology driven businesses there has been larger amounts of data available not only on open source, but for companies as well. Most companies have been using data to understand their customers for years (the credit card companies can paint an accurate portrait of a consumer and do it with ease), but there are new techniques being used to accurately mine that data. These techniques come from a brand of artificial intelligence and are focused mainly in an area called machine learning. What machine learning does is construct algorithms, study the results of those algorithms, and learn from that data. For example, there is one algorithm called "Association Learning or the Apriori Algorithm". It is generally known as the first algorithm data miners try. What it does is learn interesting relationships among data in large sets. An easy example would be the groceries you purchase from the store. Management can mine each transaction that is made and discover if there is any correlation between items being purchased. Perhaps people who buy apples also buy cheese. Management can then market their products in order to keep that relationship continuing and perhaps even discover more relationships. Many companies that do this can learn quite a bit about their customers (even though some argue this can't be done right now, with the way technology is growing, it probably will soon.)

For me, gaining more confidence in my ability to apply mathematical probabilities to real life events is something I've been wanting to do but never had the proper outlet to succeed at doing. And having spent the past five years learning all I can about computer programming, this finally feels like an excellent outlet for me to pursue. And sure enough, once I started learning how to apply data mining techniques to large datasets, hours started flying by as I immersed myself into this new world. Gaining the right information to then apply as knowledge is becoming the new currency in the world today. There is so much raw data that anyone who can sift through it and correctly analyze and accurately communicate what it means will easily by a one-eyed king in the kingdom of the blind. 

There are a few rules and although I'm not going to give a rundown of the entire training materials, I will explain a few ways it is currently being used.

  • It is a foolish to ask for predictions for the fundamentally unpredictable. What I mean by this is that although there is a method and process for accurately forecasting the probability of an event happening, there are still mostly events that cannot be predicted. The best we can do is ask ourselves the right questions and move forward from there. But the big key is knowing the right questions to ask and when to ask them. Trying to predict where the ball will land in a game of roulette on every play is mathematically impossible (unless there is cheating involved because the probability of guessing correctly just three times in a row is .00182%). However, if you know what the odds are against you (trust me, the house has a 5.26% advantage on every spin) and what your long term expected value for continual play is, then the game becomes much more manageable. The movement of the financial markets could also be thought of as unpredictable and if you are going to play that game, your best bet is to have a strategy and system in place. (I'm not going to go into details on this as I don't work in finance and am not a professional trader. If you really are interested there are mountains of information online and in book stores. Again, I will say that the house has an advantage.)
  • Forecasters need positive and negative feedback. When there is a question you have been thinking about, the worst thing you can do is make your forecast and then walk away when you are done. Forecasting requires constant iteration over the course of the questions lifecycle. The outcomes change constantly and the best thing the forecaster can do is constantly take in the feedback they are getting and apply it the best they can. Not listening to the feedback, even if it's negative can be detrimental to the overall outcome. Always take in the feedback and move forward from there. 
  • Prove yourself wrong. If there is a event of some sort that you are trying to predict and the evidence seems to point overwhelmingly to a specific answer, try finding evidence that will prove that outcome wrong. Maybe it's just an opinion piece or from a new source that may not be entirely credible, but find it and analyze it anyway. As I stated in the last point, outcomes can change in the blink of an eye, even entirely predictable outcomes. But confirmation bias can be even more detrimental to a proper forecast analysis. Knowing all sides to a story is always a good bet. 

It isn't just the financial industry that has been using forecasting successfully. Although everyone from George Soros, Ray Dalio, to James Simmon's Renaissance Technologies are incredibly effective in the short term (milliseconds for Ren-Tech) and longer term thinking (macro thinking application in the case of Dalio and Soros), other industries such as technology to sports are effectively using these techniques to model better outcomes for their businesses. The big five tech companies, Google, Apple, Facebook, Amazon, and Microsoft use and offer forecasting techniques in a myriad of ways. Google is by far the most innovative technology wise (type anything into the search input box and watch it "magically" try to predict what you are going to type), but all of these companies use forecasting in ways that weren't even possible 10 years ago. Amazon can predict what you are going to buy and Facebook can predict how you will react to a status update. That is just the basic level of forecasting these companies can accomplish. Then there is sports and more specifically the "Moneyball" techniques that were used in baseball with the Oakland A's and Boston Red Sox. (It helped that the Red Sox owner, John W. Henry owned a CTA fund and had successfully applied forecasting and trend techniques to make money in the markets which is how he could afford to buy the Red Sox in the first place). I am convinced that other industries will start getting better at collecting and applying the data they get far more effectively.

Which leads me to write about how using the analytics techniques I mentioned above with more of the technological models, machine learning, plus the addition of human judgement to get to effective forecasting. These two approaches aren't mutually exclusive and should be combined. In fact, that combination is probably one of the best ways that businesses (and human beings in particular) can really start to effectively use artificial intelligence; by using machine and human judgement to attempt to forecast better results. Machine learning algorithms can model data faster and more effectively than a human being can but a human being can use quick judgement to understand when to use and not to use certain data. Weak A.I. is still the most prevalent in the world today (think Siri, it's artificial intelligence, but it isn't very intelligent.) but we may start to see the emergence of stronger A.I. over the next 10 to 20 years (machines that can learn, apply, learn some more. Like how a child learns by doing.) As more and more data becomes available, there will be more and more of a need to accurately forecast outcomes using that data. Data science and analysis plus applications of new technology in the field of A.I. will play a huge roll in pushing this forward.

From everything I have learned since I joined the tournament, I am extremely excited about what will be possible. For me, learning how to apply forecasting techniques has opened my eyes to my own capabilities and what I would like to do with them. I've always been fascinated by the emergence of A.I. and have wanted to somehow work in the field and am started to find ways to become more and more involved. I had already been pushing forward learning as much as I could about machine learning (yes, I took the Coursera class, but I still want to learn more applications) and since I have started to mine more open datasets, how these algorithms really work is becoming more clear to me. It is a fascinating and exciting field to be in and I can't wait to see what's to come.  

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/846381 2015-04-24T20:28:37Z 2015-04-24T20:28:37Z Truth

“When you’re young, you look at television and think, There’s a conspiracy. The networks have conspired to dumb us down. But when you get a little older, you realize that’s not true. The networks are in business to give people exactly what they want. That’s a far more depressing thought. Conspiracy is optimistic! You can shoot the bastards! We can have a revolution! But the networks are really in business to give people what they want. It’s the truth.”

- Steve Jobs

This quote is from 1996 but it still rings very true today, especially with algorithms controlling much of the content that we are seeing. It was important back then and doubly important now but we must learn to think critically for ourselves as opposed to just accepting what is being fed to us. Some of it may be true and some of it may be false, but the best we can do is make that decision for ourselves, not by a large company.

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/845866 2015-04-23T21:39:04Z 2015-04-23T22:00:44Z Ideas

An idea is not a mockup

A mockup is not a prototype

A prototype is not a program

A program is not a product

A product is not a business

And a business is not profits

-Balaji S. Srinivasan from Startup Engineering

I've been working at a smalls startup now for almost close to four years and although it has in no way shape or form ever gone down the celebrated path you hear or read about in the press, it has definitely been a learning experience for me. Over the past couple of years I have iterated over a couple ideas that I have prototyped in the Bitcoin space (BlockShare.IO, and a news aggregator called CoinGazr that became a little too convoluted but I am thinking of return to work on.) and the music space (MusicGenius, and my boutique record label Context + Form Digital) and I love the process of taking an idea and trying to bring it to life. I have some other ideas floating and I hope to build on those over the next months as well. And I really hope to turn some of those into 

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/844864 2015-04-22T17:32:29Z 2015-08-20T22:59:34Z Beauty

"Mathematics has beauty and romance. It's not a boring place to be, the mathematical world. It's an extraordinary place; it's worth spending time there."

Marcus du Sautoy

Mathematics was never my stronger subject growing up. In fact it was the one I hated the most which is something I find humorous now since I find myself engrossed in it one way or another on a daily basis. It intrigues me to no end and I have days where I want to do nothing more than focus my thoughts and time on a specific mathematical subject. Lately I've been engrossed in the very basics of abstract mathematics using logic and proofs. Learning and applying it has helped open my eyes and mind to a whole new world that I never knew existed. It has a beauty and elegance to it unmatched by most other subjects. 

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/844323 2015-04-21T20:44:42Z 2015-04-21T20:44:43Z Constraint

"I’m of the opinion that most investors would be better off making fewer decisions and getting rid of any unnecessary clutter from their portfolios and investment process. Placing constraints on yourself is a great way to do this. The first step is understanding yourself and your own flaws, something that’s not as easy as it sounds, since the easiest person to fool is often yourself."

Ben Carlson on Placing Constraints on Yourself

This quote is directly related to the markets and it is excellent advice too. I think it's under appreciated in most all areas of our lives as well. Cluttered thinking and action can lead anyone down a vicious cycle and constraining your thought process and approach can help alleviate some of the unwanted pain. I've been finding myself a bit more active on the investment side of things in my life lately and this advice hits home to me. What I've discovered is that learning one particular area of investment suits me well once I understand the long term effects of such a process. It isn't easy but it is definitely worth it.

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/843724 2015-04-20T22:21:31Z 2015-04-20T22:21:32Z Focus

"Focus was ingrained in Jobs’s personality and had been honed by his Zen training. He relentlessly filtered out what he considered distractions. Colleagues and family members would at times be exasperated as they tried to get him to deal with issues—a legal problem, a medical diagnosis—they considered important. But he would give a cold stare and refuse to shift his laserlike focus until he was ready."

The Real Leadership Lessons of Steve Jobs
C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/814022 2015-02-24T01:16:03Z 2015-02-26T01:08:07Z Learning Data Visualization using Processing

Learning data visualization techniques using the Processing programming language has always been a skill that has been on my list of things to learn really well and I finally got around to get started. I've used other technologies and methods before for data visualization, most notably R and RStudio, so when I got the opportunity to learn how to take that skill to the next level I jumped at it. Here is a visualization of all the meteor strikes that have been collected around the world. The bigger the circles, the larger the impact. I'm not going to go into a huge analysis since I'm sure it's been done many times before, but I am excited to get cracking on other data sets in the near future. 

The code and data can be found in this Github repo and the Skillshare class can be found here. 

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/806135 2015-02-04T18:57:05Z 2015-02-04T19:58:14Z One API to rule them all.

A few months ago I wrote about how API's could be used as a personal key that will give access to various platforms that people belong to and sell their products or services through. I still strongly believe that this is a good idea especially in lieu of so many people using different platforms to make money. (Think everything from Etsy to Uber or even how musicians and artists could benefit from having one with streaming platforms like Spotify). But another function of an API that I hadn't thought of is getting rid of layers of management. I ran across this post today which talks exactly about using API's in that way.

What is interesting to me is what the possible ramifications of this could be. People are already starting to get worried about automatons taking over the working world, but I don't think it's as cut and dry as that. Although I do think that more jobs will be automated in the future, I would argue that was going to happen no matter what. Technology is snowballing in that direction regardless. But somebody will need to work with this type of software and those people will have to be trained with new skills. Ultimately, most autonomous software isn't even completely autonomous. It needs the feedback of a human who is trained properly to work with it. This is where the ultimate synergies will reside; where human beings and machines will work together seamlessly and augment one another. This also gets me thinking about what types of new jobs there will be in the future that we can't even fathom right now.

So as API's and software start digging away at different layers of management, it will be those who know how to work with that software that will be the better off. I am also curious as to what kinds of scenarios will be possible if  or when this type of system is in place. Much to think about. 

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/806080 2015-02-03T00:44:55Z 2015-02-03T00:44:55Z An Approach to Thinking Deeply

I discovered this article over the weekend and have been pondering it quite a bit since then. The quote that really got me was this: 

"People vastly prefer passive activities like reading or listening to music over spending just a few minutes by themselves. Being alone with no distractions was so distasteful to two-thirds of men and a quarter of women that they elected to give themselves mild electric shocks rather than sit quietly in a room with nothing but the thoughts in their heads."

This was shocking (no pun intended) to me and at I had a hard time believing it at first. Then this morning on a packed bus, I sat down and quietly looked out the window for a few minutes. There was a new concept in probability that I had been reading and thinking about and I wanted to take some time to wrestle with the concepts in my head. When I looked up I noticed that all but two people were staring at their mobile phones. The first one was me and the other was a child who couldn't have been more than five years old. For many years now I have always tried to have quiet moments where I can reflect on what is happening in my life. Some people call this meditation, but it isn't quite like that for me. There are always thoughts reverberating through my mind and sometimes it feels best to just reflect and think on them. Focus is the key element here. But I haven't always been successful doing this and the rise of smart phones has made it a bit more difficult to actually accomplish such deep thought. 

Lately I've been thinking about very specific things (algorithms) and focused thought has become a key to understanding the way I think through a problem and solve it. Or at least understand it on a deeper level. My process has been very simple. Normally I will review, study, and apply new concepts when I am at home, but I can't always be at home to work. So I write down a problem or concept in a small notebook I keep in my pocket, glance over it throughout the day, and then when there is time, I exercise focused thinking about that one topic for the next 20 - 30 minutes. Sitting on the train or bus (but mostly the train) helps me relax and do this without too many distractions. My goal is to know the problem inside and out instead of a cursory understanding. In fact, having read this book a number of times and applying the methods to my own thoughts has helped me grow intellectually. What is it I hope to achieve over the long term? To become a more effective thinker and problem solver and to think critically instead of haphazardly. 

So it's disheartening to read that people would rather receive an electric shock than be bored. Our minds are made to think effectively and critically analyze problems to make our world a better place. And it's no secret that doing so takes a tremendous amount of work. But it takes work for a reason and the best reason, at least to me, is to understand ones self at greater level. It's strange to think that some people don't want to know themselves at all. Or as Bertrand Russell once said "many people would rather die than think. In fact, most do."

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/802993 2015-01-26T22:55:37Z 2015-01-27T05:40:04Z Thoughts...

I had taken the time to write a very specific post over the past week on probability and forecasting but when it came down to it, I decided to nix using it at the last moment. I will eventually post it (probably this summer), but since I haven't written anything in a while I thought I would at least use a quote here. 

"Whether you think you can or you think you can't... You're right." - Henry Ford

I'm not sure of the circumstances that made Henry Ford say this but I fully agree with it. If you think you can do something then you will find whatever means necessary to accomplish it. Likewise, if you don't think you can do something, then you will find every possible way not to do it, whatever it is. It's a quote I think of whenever I feel stuck in a rut or not sure. I just ask myself if I think I can do whatever it is I've set out to do. If the answer to myself is yes, then I do what needs to be done. It's as simple as that. 

I haven't written much here in a while and would like to get back in the habit of sharing my thoughts and ideas here. Composing one or two essays a week for now should be good to move forward. It feels good to be writing again. 

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/771197 2014-11-18T02:55:44Z 2015-07-14T15:25:20Z Building an Audience Machine - "Conversation is King"

I've been reading a book by Cory Doctorow called "Information Doesn't Want to Be Free" which argues that copyright and creative success in the digital age are changing dramatically. I agree with this sentiment and it's not only because this book is wonderfully argued, but because I have been doing it with my own creative endeavors for the past year. Selling art isn't what it was 20 years ago and because most people expect music to be freely streamed or torrented, making a living as an artist has changed dramatically. 

Exactly one year ago I made the choice to start releasing my music. It needed to happen for several reasons. First, I was finding that I was creatively stymied in my work environment and needed a way to express myself. Second, I hadn't ever released my own original compositions to the world, only a few DJ mixes, with one being featured prominently on a British radio show back in 2006. (I don't have a link). Third and lastly, recording and releasing my own music was something I have wanted to do since I was 8 years old and now was a great time to do it. Why? Because there are so many great distribution channels and ways of reaching people all over the world and that is fantastic way to get your products (in my case music) to people.

But I had to put some limitations on my work first. Each piece of music had to be composed, recorded, and released all within a one week time period. Also within that time period I needed to create an album cover, copy describing the track, and put together any other marketing components. The example I took was a methodology based on scrum and kanban. My weeks generally ranged from Monday as my start date to Sunday as my release date. The limitations worked wonders for my creative output and I found a musical voice for my project quickly and was able to expand on it with each new subsequent release. Although this started as a project that was primarily dance music focused, it quickly turned into a more elaborate electronic music project that was starting to showcase my composition skills. Skills that have felt hampered by not creating or releasing anything for the past few years finally felt a bit of release. My happiness levels shot through the roof. What I found was that by adopting this system and adhering to the process helped me get things done. I was able to release music consistently and started to slowly build an online audience which I will get to next.

Another limitation that I placed on myself was not telling any of my close friends about it which meant I had to build an audience organically through social media and other methods. This has proved much more difficult than I would like, but in the past year alone, I have connected with a multiple amount of people all over the world who simply enjoyed the music I was making. It is a fantastic feeling to be able to do this and it's something that I want to continue to do. However, without a large marketing budget, it is much more difficult. That aside, I will continue pushing forward over the next year. 

What started as a project that was going to help me showcase my skills and release some creative tension quickly snowballed into more creative work and bigger ideas that needed to be acted upon. I had to make a choice as to whether or not I should release music as a single musical project, or as a boutique record label with the possibility of adding more artists further down the road. I chose the latter based on the idea that it could offer me more freedom and the opportunity of working with other great artists in the future. Once that choice was made, I operated the entire project from the perspective of a business day in and day out with the ultimate goal of releasing an albums worth of material by sometime in late summer '14 (which was accomplished). To help market my eventual album release, I contacted a few electronic music blogs until I found one (BeatsandBeyond) that would let me submit my new material and have it posted. Doing it this way really helped clarify my thinking as to how I would interact with customers (fans who liked my music) and release more music down the line. I also went into this knowing full well that there was a huge probability that no one would actually make a purchase of any music. The fact remains that people just aren't purchasing music like they used to and that is fine by me. It is a commodity that is for the most part free (torrents and streaming) and it is harder to swim against the stream then anything.

How has everything fared in the year since my first song release? Here are the numbers: I average about 100 - 150 plays a week on Soundcloud when I am not promoting anything. The majority of my fans and followers came from Soundcloud as well and I found that talking with and regularly updating them with new music and news was the best way to keep a relationship going. It should also be noted that when one of my tracks went up on their trending section, I found myself gaining on average about 1000 plays per week for that track over the next six weeks or so. It was a fantastic way to get my music heard. People still love music and that is exciting to know. Advertising on Reddit was a small success (seeing as how I didn't have much capital to pour into the two campaigns I did) and I was able to convert new fans based on them clicking the advert, sending them to Bandcamp and getting them to listen to the music. From there these fans could decide to support me by paying or download my releases by giving me their email address. There is a bit more power in Reddit then most people may realize and using it for future campaigns will be a must. My click through rate was a bit under 0.6% (this metric is an average for all Reddit music campaigns). I didn't sell a whole lot of albums but I did make a little money (not a lot but a fair amount to justify the work that was being done) and that was enough for me to continue on. Lastly, and most importantly, a year ago I had zero fans or support and now my Soundcloud followers (whom I consider fans) fluctuates around 600 (give or take a few). People seem to stop following ever once in a while. That is fine by me as I would rather the label builds up a solid fan base of people who really enjoy what is being released. 

Now that I have written through my thoughts and experiences of the past year, I should probably explain what I am expecting over the next. The plan is to continue creating and releasing music. And with that it should be added that each fan relationship should be carefully cultivated. I would rather have 100 people love the products (music) we are releasing (and pay for it) than 1000 people who don't care one way or another. Building and maintaining that audience is going to be priority number one. I'm sure by now you are asking "didn't you say that people don't want to pay for music?". In fact I did and I will follow that up by saying I am going to be experimenting with new methods of building an audience by stepping into new territory. Licensing music and creating music/sounds for specific media platforms are examples that are already currently being looked into and implementation will begin over the next few months. But I would also like to add a few more services slowly as well such as sonic branding, strategy and intelligence for companies looking to add music to their campaigns, and finally a tool I built to help discover information about artists. It is still in it's minimal viable product stage, but you can check it out for yourself here: MusicGenius. (I have a plan with what I want to do with it. I just wanted to make sure I could build it first). If that seems like a lot to handle then you are right. But just know that I will slowly be building out these new methods. And the hope is to build a company with the revenue to not only build a bigger customer and fan base, but hire employees who are just as passionate as I am and can fill in the roles that will be needed. If you think that sounds less like a record label and more like a marketing and tech company then you are correct. The future music incubators will have to be a cross between marketing and technology since those are the best ways to reach out and have conversations with people. And instead of relying on the old adage "Content is King", they will instead have to be focusing on the new one, "Conversation is King." Marketing and technology make it easy for people to communicate with one another and for brands to communicate with people. Building an audience will take more than content, it will take starting the conversation.

I've had some other projects that I have worked on throughout the past year. But my focus is now going to be on building this business into a viable record label and business. And that won't be easy at all. Like I said, my main focus with it is going to be building an audience for the music that will be released. I am super proud of everything that has been accomplished since the release of my first track a year ago, but now it's time to up my game. I've connected with some pretty cool people all over the world. And in the second year creating more great music, as well as fun and new ways for people to experience it, will be my main mission. Now it's time to take that mission to the next level.

Check out my latest release here:

And go here too:


C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/761531 2014-10-28T16:38:34Z 2014-10-28T19:38:07Z Private and Public Thoughts

I don't post much of my writing in the public as I would like to. To me, there is nothing better than to open up a notebook, pull out a pen, and start writing to my hearts content. Most of the time these are just small, private ideas or thoughts that need to get out of my head. So I lay them down on paper and can comfortably walk away afterwards. I've always thought of this blog as being a place where I can write a more detailed analysis about whatever it is I am thinking about and apply my mind to understanding it in greater detail. 

Lately, that has been with three things: Bitcoin and it's applications with the world around us (not just the financial world), The Internet of Things (I just ordered some Arduino supplies like a touch screen. Learning how the IoT may work seems super exciting to me. I will need to purchase some sensors soon too.) And finally, functional programming. In fact, I have been throwing myself head first into an online course given at the University of Washington called "Programming Languages". I love this course and I love what I am learning in it. The best way to describe it is like this: In order to learn a language like English, we must first understand the analysis of how it works. What is a verb? A noun? An adjective? And how do these elements effect the language? We can move forward from there. This class works in much the same way. What is the languages syntax? In other words, how do the various parts of the language work? Does it type-check? Do we understand the semantics of the language? Finally, does it evaluate? What do the expressions in the language evaluate too? I have been learning so much that it is very overwhelming at times. But it in order to learn, we must keep moving forward one step at a time.

Now is the best time as any to start putting more of my thoughts out there in public. And writing about something can really help analyze and clarify any elements of our thought process that we are not sure about. So that is my resolve, to start clarifying my thoughts in more of a public space.  

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/759456 2014-10-23T18:30:19Z 2014-10-23T18:30:19Z Isaac Asimov and great ideas.

There is an article printed in M.I.T.'s Technology Review written by Isaac Asimov about how people get new ideas. I suggest you go read it

"Probably more inhibiting than anything else is a feeling of responsibility. The great ideas of the ages have come from people who weren’t paid to have great ideas, but were paid to be teachers or patent clerks or petty officials, or were not paid at all. The great ideas came as side issues."

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/741501 2014-09-27T19:04:51Z 2014-09-27T19:04:51Z Thoughts on Moats and Monopoly

The economics of the technology field is drastically changing from the past. Businesses that are known for selling physical goods still deal with diminishing returns. By virtue of this very fact they run into their own limitations. Yet the very idea of diminishing returns is taught in both business schools and to many economics majors. (I should know, since economics was a major focus of my undergraduate career and we learned quite a bit about this subject). In many aspects, this idea makes sense. Especially in the commodities business. In fact, most all commodities run into diminishing returns. You can only grow and mine so much of your resources. Remember, I am talking about physical commodities here. But the fact still remains that it has been assumed, especially in the past, that most companies will reach a state of diminishing returns selling their products in the markets.

However, this line of reasoning is dramatically changing as more and more companies are now in the business of collecting/selling data and information. So what is the result we are seeing with this change? Now we are starting to see companies that have increasing returns as the processing of resources turns into the processing of information. What makes the concept of increasing returns interesting is what it enables a company to do if it has them. For starters, those companies with finite resources will compete with one another until the price is pushed down to an equilibrium level. Those companies that go below this level will start losing money and may find it harder to stay in business. On the other hand, those companies with increasing returns will move further and further ahead and have a higher probability of locking in their particular market. Not only does this idea go against the grain of your typical competitive analysis, but it also gives these highly competitive businesses the ability to operate differently in their market.

There are only a few companies (but I am sure there will be plenty more in the future) that are able to operate this way and thus are able to push super far ahead in their markets. One such company that I will briefly analyze is BuzzFeed. This company may not be the first that comes to mind and may even seem a bit silly but hear me out. BuzzFeed, along with Vice, are leading a new wave of media companies into the 21st century. They are long term focused with BuzzFeed's main focus being on social advertising. And Vice is busy changing the rules of the media game with video. In fact they reach a young audience in a way that makes most advertisers drool. 

Back to BuzzFeed, You know you have seen their idea of social advertising. How many times have you been super annoyed by some of your Facebook friends taking all those ridiculous quizzes to see which movie character they are. Or what their real career should be? Those are all paid for social advertisements and they work because most everybody takes a break from their work day and takes one or more. After all, I want to know which super hero I really am. Although it is mostly by luck that there is even a social advertising market in the first place, they lead it with focus, efficiency, and fun. By having the right platform, with the right product, at the right time, they have enjoyed a large amount of increasing returns and become one of the highest valued new media companies right behind Vice. They are enjoying increasing returns not because their process can't be repeated, but because they are already so far ahead of their competition. 

Another such newer company that uses increasing returns in an extremely valuable way is Uber. In fact Uber is changing many rules of the game and if the company is analyzed closely, more people might see just how much change it can bring about. Uber has created a loop that will just continue pushing their returns to astronomical levels. Think about it like this, there is an initial demand for available cars, this creates an increasing demand for more drivers, which increases faster pickups, which leads to a greater demand of available cars. Also, an increasing demand for drivers leads to less downtime for drivers, which means lower prices for customers, which again, leads right back to a higher demand for available cars. Uber has this down to a science. Let's not forget that they are also partnering with smartphone vendors, car manufacturers, credit card companies and insurance companies. Slowly but surely, Uber is making itself the undisputed market leader and the competition will not be able to keep up. There are also other variables that I have not even analyzed here such as car ownership going down, the density of their coverage and how it is growing quickly, the dual rating system (the drivers get to rate the users), and trust. Let's also not forget the damage they are doing to the taxi industry

The last company I'm thinking of doesn't need much of an introduction at this point but Google's search functionality enjoys increasing returns by the shear fact of how powerful their market share has gotten. Peter Thiel argues they are a monopoly and how that is good for them. I would argue that they have built a powerful moat around their search and advertising business and it is become incredibly difficult to compete with them in these arenas.  Only time will tell if this thesis is correct or not.

I'm sure there are a few ideas right now that small startups are looking to create a business out of and I'm pretty sure that one or maybe two will enjoy increasing returns to the point of creating a protective moat around their business. In fact, I wouldn't be surprised if it was in the Bitcoin/Altcoin community right now. But as I said earlier, only time will tell.

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/736588 2014-09-04T19:01:11Z 2014-09-04T19:01:11Z __________________, Genius.

I just found this piece written by Biz Stone for Wired magazine last year and thought I would share it. Here is my favorite quote from this article from Wired. 

"By simply announcing himself as a genius on his business card, Wile E. Coyote epitomized the spirit of the Silicon Valley entrepreneur. When you’re starting a company, you sometimes have nothing more than an idea. You have to begin somewhere, so you declare yourself an entrepreneur just like Wile E. declared himself a genius. Then you make a business card and give yourself the title Founder and CEO.”

You can learn about "Faking It" Here.

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/735571 2014-09-02T19:22:30Z 2014-09-02T19:26:00Z Personal API Key

I read an interesting blog post yesterday by Albert Wagner of USV. In it he talks about the impact that many new companies are having with freelancers as traditional employment is falling. And while these marketplaces are growing, the biggest influence will be access to the information they employ (with the marketplaces winning since they will have access to all of their information). His suggestion is an individualized API key that would have the right to access full read/write capabilities in the marketplaces system that the user can then see via an application. Or as he put it, it will be the right of the individual to be represented as an algorithm. My thoughts immediately started thinking about how this could be done via blockchain technology but I will have to look more into that idea.

I am currently working on an essay about the "increasing returns" that information can bring to a company and should have it posted within the week. If you are interested, you can check out Albert's essay here.

C.P. O'Neill
tag:cponeill.posthaven.com,2013:Post/727526 2014-08-18T19:27:42Z 2015-07-14T03:06:08Z Kitchen-Table Economics

There is a question that economists have dealt with for years which is "if we have markets, why do we need the corporation"? I'm pretty sure this isn't a thought that crosses most peoples minds on a day to day basis but it is something that I have thought about from time to time. So how do we find the answer? Let's first look at the question another way, "What is the best way to properly allocate capital and organize labor"? From that question, we can say say that the best method has been the corporation. So why is that? And is the corporation the best way to go about it?

Economist Ronald Coase was able to analyze and answer this question with his seminal essay The Nature of the Firm. Due to a set of issues related to discovery costs, transactional costs, and obscured reputation, it is simply too expensive to keep making and doing certain processes over and over again. So the solution has been to start a corporation and create and sell a product or service to the public that way. It works pretty simple. People are hired into a corporation to work on a specialty and they focus primarily on that output. Each employees input (whether that is an expertise in marketing, finance, sales, or product creation), contributes to a much larger output (a finished physical good or service) that the corporation then sells to its customers. For example, Ford's assembly lines in the early 20th century are an amazing example of inputs coming together for a bigger output. In fact, Ford's process was so great that it revolutionized the way that factories were run, and help put in place more specialty focus for each employee. This process caught on to many other companies in other industries and suddenly more products could be made much quicker and gotten out to more people in less time. For the majority of the 20th century, The U.S. was the biggest exporter of manufactured goods.

Skip ahead to the 21st century and things are not what they used to be. China is the number one exporter of manufactured goods and the U.S. has slowly turned itself into a service driven economy. Because of this, I think that we will start to really see some major changes with manufacturing. In the past, and thanks to the assemly line revolution, large quantities of the same product could be made quickly and shipped to its destination in a short frame of time. Furthermore, with Deming's statistical research and systems put into place (think lean manufacturing and six sigma), manufacturers could suddenly make their products faster with little defects. Japans rise to dominance in the 1980's is a prime example of what could happen when manufacturing is done correctly. (I will skip over their economic fall from grace in the early 1990's to the present). 

But times change and broadly speaking, more people want products or services that are individually made for them. This isn't something that is easy to do when your entire line of products has little variation and your company must sell a large quantity just to break even. The fashion industry is catching on pretty quickly thanks to the Spanish behemoth Zara. Although they don't manufacture clothing for each individual person who wants it, their supply chain is so streamlined that they can sell out of a popular product, have more quickly made, and shipped back to the store in a heartbeat. Their process is unreal and a testament to how effective a well run supply chain can be for a company. There are only three other companies that I can think of with such an effective supply chain and that is Apple, Amazon, and Wal-Mart.

So what about hardware products, or smaller products that don't need huge assembly lines to create? Will it be easy to manufacture these products that are tailor made for each person in the future? Part of that answer could be 3-D printing, which has the ability to really throw the manufacturing world on it's head. 3-D printing is still in the early adoption phase, but once more people start to see how it can be used, and the price of printers comes down to the point that most anybody can easily buy one for a cheap price, we will probably start to quickly see some changes with the manufacturing process. It has even been stated that 3-D printing could bring about the second manufacturing revolution here in the U.S. I don't know if it's true but I'm pretty sure we will find out quickly enough. With this type of technology, more and more people will be able to print and manufacture their own products right at their own kitchen-tables (or garages if they need more space). But this technology is still in the early adoption phase and unless there a few companies that are able to cross the chasm soon, then this will remain a niche technology for those who understand it.

The other part of the answer are the newer types of marketplaces that give smaller creators access to more people. Etsy is the most prominent example I can think of at the moment and it has done wonders to create a whole new market for people to sell their creative wares to other people. These micro niches give people access to (mostly) hand-made products that will work out perfectly. And I wouldn't be surprised if customers contact these smaller one-stop shops to request a product made specifically for them. 

So what does all this have to do with my original question, "if we have markets, why do we need the corporation"? Because we are starting to move into a place where more people will start making their own products and services to sell to other people on an individual basis. And by that I mean more products will be tailor made for people instead of the one size fits all approach. These people are creating new marketplaces at their kitchen table with technology leading the way. And some of these people will have products that sell more than others and it will require them to better organize their labor and allocate the capital working for them. And the best way to do that will be through a corporation. Love them or hate them, the basic idea of the corporation is the best we have come up with yet to do all of this. 

C.P. O'Neill