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Last week, Oregon voted to decriminalize the possession of all drugs. Ballot Measure 110 passed with a whopping 59 percent of the vote.

Numerous other states voted to legalize recreational cannabis on Election Day as well, namely Arizona, New Jersey, Montana, and South Dakota. Across the board, voters struck down policies that supported the War on Drugs at every opportunity they were given.

But Oregons initiative is by far the most sweeping progression weve seen on this front to date. Its also different from actions taken in other states because the vote did not legalize drugs, but rather decriminalized them. This means it removed criminal penalties attached to the possession of drugs but didnt all-out legalize thema very important distinction.

Beginning February 1, Oregonians caught carrying small amounts of illegal substances will be met with a $100 fine. If they choose to not pay it, or if they are unable to, they can agree to a health assessment at an addiction recovery center instead where they may be prescribed customized treatment plans.

While Oregon might be the first state in our country to try this approach, it isnt an unprecedented strategy.

The ballot measure also expanded access to recovery treatments, housing, and harm reduction services, measures the state will fund through the reallocation of tens of millions of dollars from Oregons cannabis tax. Additionally, it redirects the money saved from not arresting, prosecuting, and caging people for drug possession to treatment services as well.

The Oregon Criminal Justice Commission estimates that Measure 110 will reduce drug possession convictions in the state by 90 percent. Ultimately, this directive removes drug use from the purview of the criminal justice system, and chooses to instead focus on treatment opportunities. For those reasons, the campaign found strong support in the states medical and healthcare communities, which have witnessed first-hand the abject failure of drug criminalization.

While Oregon might be the first state in our country to try this approach, it isnt an unprecedented strategy.

Portugals case study shows that decriminalization does not necessarily lead to higher drug use rates.

Almost two decades ago, in the midst of a heroin epidemic that was ravaging the country, Portugal decriminalized most forms of drug possession. Drug trafficking remained illegal, but drug users were viewed as ill instead of being treated as criminals. Instead of being imprisoned, drug users were taken before a drug court made up of psychologists, social workers, and legal experts who sought health-focused solutions when they were apprehended.

Data from Portugals experiment show it was an overwhelming success.

Arrest rates for drug-related offenses have dropped by 60 percent since 2001, while the number of people enrolled in treatment programs went up a reciprocal amount. Too, Portugals drug overdose death rate has plummeted, and HIV infections fell from 1,575 cases in 2000 to 78 cases in 2013. Meanwhile, Portugals drug usage rate has remained lower than the average use rates in Europe and drastically lower than those found in the US.

Portugals case study shows that decriminalization does not necessarily lead to higher drug use rates, as many critics claim. But despite the countrys success, others have been slow to follow in its footstepslikely due to the entrenched special interests working to keep these policies in place.

It has been said that those who do not know history are doomed to repeat it, and America is no exception as we seem to be living in a Groundhogs Day of perpetually failing policy.

In the 1920s, alcohol prohibition led to an increase in consumption, the production of more dangerous beverages, a rise in organized crime, rampant corruption among public officials, a court and prison system stretched to the brim, and an increase in the crime rate. Sound familiar?

By every available metric, the War on Drugs has failed.

And yet Americans seemingly learned nothing from this period in our history and instead tried the whole charade again with the War on Drugs.

Of all our governments colossally bad ideas, the War on Drugs has stood out for its horrific disruption of the family unit, destabilization of whole communities, devastation of millions of lives, and utter inability to curb addiction in any meaningful way. And thats not even mentioning the fiscal and economic costs of this behemoth.

By every available metric, the War on Drugs has failed.

The country has spent well over a trillion dollars enforcing drug criminalization. In fact, its estimated the federal government spends $9.2 million every day just to incarcerate people for drug-related offenses. States spend another $7 billion or so a year.

We incarcerate hundreds of thousands of individuals for non-violent drug offenses. These people then cant work a job and contribute to our society while in prison, and they will have a hard time returning to a productive life when they are released.

These are people whose families and children struggle to make ends meet without their support. And most importantly, these are people who are not getting the help they need. In fact, a person is most likely to overdose in the weeks after they are first released from prison. The social costs of the War on Drugs are staggering.

Product bans merely limit the supply of a substance and the competition that can provide it, which in turn makes the price increase on the black market

And we do all of this for horrible results. Americans account for less than 5 percent of the worlds population but consume 80 percent of all opioids produced globally. Throughout its reign, the drug war has contributed to an increase in drug overdoses, led to the creation of violent drug cartels, fostered unemployment, and safe-guarded corrupt public officials.

Anyone can look at the current picture and recognize the War on Drugs has failed. But for those whove studied economics, the outcomes have been predictable and predicted for centuries.

Economist Frederic Bastiat famously wrote about that which is seen and that which is unseen in his essay The Law. When considering matters of public policy, most people consider only what they see and what they intend for an action to do.

In this case, nearly everyone acknowledges that drugs are harmful and that it would be desirable to rid our society of them. The intended goal of the War on Drugs has clearly been to eradicate society of an evil most agree upon.

Its important to remember the limitations of government.

But that which is unseen, all of the unintended consequences of an action, are what good and smart leaders think of when determining the law. When it comes to the drug war, the unintended consequences have far outweighed any intended good from the policy.

The economic principle of the seen and the unseen plays out time and time again when the government seeks to curb behavior through product bans, which we know do not work.

Product bans merely limit the supply of a substance and the competition that can provide it, which in turn makes the price increase on the black market and pads the pockets of the drug cartelsleading to criminal activity, abuse, and corruption. This remains true whether we are talking about gun bans, drug bans, or any other prohibition.

Its important to remember the limitations of government. Do we want people to stay away from drugs and choose treatment when they need help? Of course we do!

Does it help to force them or jail them should they choose not to?

Not at all.

Fortunately, it seems Americans are waking up to these principles, even as our leaders are slow to unwind themselves from the clenches of the drug war. Encouragingly, the coalition who successfully moved this ballot initiative in Oregon is poised to replicate it in other states in the near future.

If Oregon finds success on this pathway, and they seemed poised to, we can hope that others will follow suit.

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Oregon Takes the Lead on Ending the War on Drugs | Hannah Cox - Foundation for Economic Education

Rafa Casal

Nobody doubts Michael Jordan's ability on the court, but, as was shown in 'The Last Dance', it was his competitive nature off it as much as on it that came to define him and make him one of the best in history.

There are few better examples than when he visited the house of his former North Carolina teammate Buzz Peterson, pushing the limits of morality even in the most casual of settings in a friendly card game with nothing at stake.

"There is a famous story about Michael Jordan visiting the home of North Carolina teammate Buzz Peterson and, while playing a casual game of cards with Peterson's mother, Jordan attempted to cheat while the old woman was using the bathroom," Chuck Klosterman wrote for ESPN.

"This is often used as an example of what made Jordan so awesome, as he would do absolutely anything to win, regardless of the circumstance". And because the character in this anecdote is MJ, the story is charming.

"However, I doubt Buzz Peterson would tell this yarn if it had involved his mother and some random dude he met in Anthropology 251 (and if he did, the story would now be about that one time he brought a lunatic home for Thanksgiving break)."

It was that competitive nature which went on to make Jordan an NBA champion six times for the Chicago Bulls, being named MVP five times and making the All-Star team 14 different times throughout his career.

That drove him on to record 30.1 points per game on average in the regular season. When the stakes were higher it only fuelled him more, with that average rising to 33.4 when looking only at playoff games.

Link:
The day Michael Jordan tried to cheat when playing cards against an old woman - MARCA.com

Teaching is the only major occupationfor which we have not developed tools that make an average person capable of competence and performance. In teaching, we rely on the naturals, the ones who somehow know how to teach. Peter Drucker

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Q3 2020 hedge fund letters, conferences and more

The act of teaching is an amazing feat. Read the above quote again. In its current form, the entire educational system hinges on finding the few people that somehow know how to teach. Thats not Talebs definition of Antifragile by any stretch.

It could be argued that no one has done more for the average investor than Jack Bogle, who founded the Vanguard Group of Investment Companies in 1974. Q3 2020 hedge fund letters, conferences and more Ever since its launch in the mid-70s, Vanguard has revolutionized the financial landscape for investors. Its flagship low-fee investment funds Read More

Yet what if AI could change that? What if AI offered anyone the tools needed to teach others. Or the power to learn faster with longer retention. How much greater would we be as a society? Jordan Peterson once said something like (Im paraphrasing), We dont really know the extent to which humans can achieve things. Imagine if we had better tools to teach more people to learn better!

Last week we discussed what makes a great AI company and how you can find industries to fish for interesting AI ideas. You can read that here.

This week we shift our focus to the education space. By the end of this essay well answer three key questions:

But before we dive in, its important to reiterate what were seeking. We want to invest in companies with defensible datasets that provide a unique moat that competition cant replicate.

In turn, this data solves a real problem for its customers. We dont want AI for AIs sake. We want wide-moat companies solving problems with data their competitors cant copy.

The purpose of AI in Education (according to Pearson): the scientific goal to make computationally precise and explicit forms of educational, psychological and social knowledge which are often left implicit.

This brings us to our first wrinkle for AI applications in Education. Education isnt as binary as, say, Finance or Retail. In short, AI in Education tries to make explicit predictions from Implicit data.

Pearson, the worlds largest education and book publisher in the world, expands on this idea of explicit vs. Implicit (emphasis mine):

In other words, in addition to being the engine behind much smart ed tech, AIEd is also a powerful tool to open up what is sometimes called the black box of learning, giving us deeper, and more fine-grained understandings of how learning actually happens (for example, how it is influenced by the learners socio-economic and physical context, or by technology).

Good AI Education companies master three models:

AI Education companies need to solve something other AI companies dont: How to show the model so that the subject learns as well as the model itself. In other words, its not enough to optimize how the algorithm learns. AI Education companies must optimize for how each student learns.

These issues matter in two models: Pedagogical and Learner. Pearson explains what AI models must do in each model. Pedagogical models for example must grapple with the following issues:

On the other hand, learners (or students) using AI education technology deal with other issues like:

Finally, AI Education models must adapt to the learner model, which includes:

Lets look at how the AI Education model works on a granular level.

Like most AI software, AI Education algorithms benefit from the virtuous data cycle. Ill let Pearson explain its impact on Education AI (emphasis mine):

One of the advantages of adaptive AIEd systems is that they typically gather large amounts of data, which, in a virtuous circle, can then be computed to dynamically improve the pedagogy and domain models. This process helps inform new ways to provide more efficient, personalized, and contextualized support, while also testing and refining our understanding of the processes of teaching and learning.

This virtuous cycle creates Data Dominance between the student, the teacher, and how the program teaches the students. Again, the first-mover advantage is huge here. The AI algorithm thats taught the most students will have the most data.

This will lead to an optimized level of displaying the learning material. Which in turn helps the teacher teach better. Which leads to increased learning from students. This leads to more data about how students progress. Which leads to better data on how to teach. And the chorus repeats (as seen on the chart below).

The cycle reverts to the three basic models of AI Education: pedagogical, domain, and learner. Pearson explains how AI implements each model to improve the one model that matters most, the learner (emphasis mine):

Deep analysis of the students interactions is also used to update the learner model; more accurate estimates of the students current state (their understanding and motivation, for example) ensures that each students learning experience is tailored to their capabilities and needs, and effectively supports their learning.

Were seeing this implemented a few ways:

Intelligent Tutoring System (ITS): One-on-one personalized AI-based tutoring. The AI program learns how the student learns and what theyre struggling with and optimizes their learning experience. All this is done without a human teacher.

Intelligent Virtual Agents (IVA): AI-based virtual assistants that help a student solve a problem or question. Think of a friendly teammate in a Call of Duty video game. While most IVAs feel heavily scripted, future AI-based assistants will enjoy a more normal, collegial tone with the student. Theres a great research paper on this which you can read here. One of my favorite quotes from it is (emphasis mine):

In contrast, we envision virtual agents that cohabit virtual worlds with people and support face-to-face dialogues situated in those worlds, serving as guides, mentors, and teammates. We call this new generation of computer characters animated pedagogical agents.

Intelligent Virtual Reality (IVR): IVR has the power to transform classrooms like no other AI-based technology. Through immersive learning experiences, students can now travel through their history lessons. Age of Awareness put it this way (emphasis mine):

Educators have used VR tools to create recreations of historic sites and engage learners in subjects such as economics, literature, and history as well. Learners may receive transformative experiences through different interactive resources.

I know I sound like a broken record when it comes to defining the benefits of AI. But a solid comprehension of the underlying mechanisms allows us to spot companies solving real problems and companies adding AI to their name for a share price bump. This is especially true in education given the three esoteric models and implicit-to-explicit data wrangling.

Now were out of the weeds and into the application. Lets shift our focus to the major players in the AI Education space.

Diana Yin wrote a great Medium article on AI in Education. In it she highlights four major players:

These players dictate where we look for investment ideas in this space. And if we want to know where to invest, we should learn about each of these major players.

As you guessed, Tech Giants involve the leading (and largest) technology companies from around the world. Thats Google (GOOGL), Microsoft (MSFT), Apple (AAPL) and Baidu (BIDU).

These companies arent 100% focused on EdTech. But what they lack in focus they make up in capital and labor force. Each of these companies has near-unlimited capital to throw at AI Education ideas. Google has Google AI Education libraries. Microsoft has an AI school. Not to mention the legions of MIT graduates eager to put their skills to work.

Tech Giants face an issue. Their AI Education ventures might work. But any success wont likely move the needle. Another thing to consider is companies like GOOGL and MSFT are happy to spend money on EdTech moonshots where profitability isnt a goal.

Educational Giants dominate the learning landscape. The list includes companies like Cengage (CNGO), McGraw Hill, Pearson (PSON) and Education Elements.

These companies are transforming from pure-play educational products/services to digital platforms.

The article notes a few changes like PSONs new AI department, Knewtons alta, and Duolingos new AI research group. Yang admits that while educational giants lack the technological knowledge, these companies sit on goldmines of data.

In effect, educational giants are doing what great AI companies do: create defensible datasets. The company with the largest database of homework problems will win in the race for the best AI-based homework platform.

PSON or CNGO could find ways to leverage their existing database of education content and incorporate AI-based education tools into their platform. That alone could change the margin profile and market multiple of the company.

EdTech startups have a lot going against them:

Yet what they lack in funding, size, and talent they make up with an obsessive company mission: to make the best AI education product possible. A few examples of EdTech startups include Cognii, Squirrel AI and Alef.

Cognii is a leading provider of AI-based educational technologies like virtual learning/teaching assistants. Squirrel AI is the first AI-powered adaptive education provider in China. The companys also led by Tom Mitchell. You know, the world-renowned machine learning luminary. Not bad having him as your Chief AI officer.

To get a sense of how fast these EdTech startups can grow, look no further than Alef. The company prides itself on its Alef Platform, a service that helps students learn at their own pace through AI-based engagement learning modules.

The Arab-based company started 2016 with 8 students on its platform. Today it has over 121,000. Thats an increase of 15,124x. Not bad!

The weakest of the four, Higher Ed and Research Institutes are plowing money into AI. MIT committed $1B in 2018 to build an AI college. Harvard established the AI Initiative. There are also myriad AI campuses abroad like Alan Turing Institute and Oxford Artificial Intelligence Society (OAIS).

Heres why this is the weakest of the four: Big tech is stealing top AI teaching talent from universities and research institutes. Tech poaching from GOOGL, BIDU, and FB have devastating trickle-down effects. A NY Times article reveals the exponential growth in professors leaving schools (emphasis mine):

From 2004 to 2009, 26 university professors moved into industry. In 2018 alone, 41 professors made the move. The steep rise in departures over the last decade and a half indicates that the trend will continue.

An AI professor exodus from higher education leads to a (not so) virtuous cycle. Fewer professors mean fewer AI graduates. Fewer AI graduates mean fewer AI start-ups. Fewer AI start-ups means more funding and technology in the hands of Big Tech. Back to the NYT article (emphasis mine):

But at the universities the professors left, graduating students were less likely to create new A.I. companies. When they did, they attracted smaller amounts of funding, according to the study. The effect was most pronounced in the field of deep learning, a technology that has become a crucial part of new A.I. systems.

In time, the brain drain from academia could hamper innovation and growth across the economy, the study argued. The knowledge transfer is lost, and because of that, so is innovation, said Michael Gofman, a finance professor at the University of Rochester and one of the authors of the study.

Poaching AI professors makes it tough to compete with the Tech Giants. Luckily to win at the AI game you dont need the largest reserves or the strongest talent pool. You need the best data. The most defensible data. Remember, the technology and the algorithms are commodities. Its the data that matters most.

Lets look at companies using differentiated datasets to capture AI-based Education market share.

There are two ways we can invest in AI-based Education: Direct and Indirect

Indirect examples of investment include GOOGL, MSFT, FB, BIDU and Nvidia (NVDA). These companies have exposure to AI-based education companies through various offshoot ventures. The downside of indirect investment is any AI-based Education successes wont move the needle for these tech giants.

Direct investments offer higher payouts if AI-based Education wins. But also carries greater risk if it fails to gain market share and defeat competitors. That said, theres a handful of interesting businesses that warrant deeper research:

Were saving our top ideas with our Collective members over the coming months. These companies arent on the above list, but were very excited about their prospects!

If you want to learn more about the Collective and get access to our deep research reports, check us out here.

Learnings greatest bottleneck isnt teaching. Its the assessment phase. In essence, AI can create a just-in-time (JIT) assessment cycle. Just-in-time assessment will revolutionize education the way Toyota shocked the auto manufacturers industry.

In fact, Education is the model industry when it comes to understanding the symbiotic relationship between humans and machines. AI will never replace teachers. Maybe thats what the real world will look like once we adopt AI. Ill leave you with this quote from Jay Richards, author of the book The Human Advantage: The Future of American Work in an Age of Smart Machines (emphasis mine):

Thats exactly right because we tend to think of all this technology as replacing what we are doing. They replace only the old way of doing what were doing. True people who used to need private physical can get it online but people who didnt use to be able to afford physical trainers can get it effectively free online. Thats because the technology allows that to happen. So rather than thinking of the technology as replacing us, think of it as sort of our extension in time and space, our entrepreneurial prostheses which extend ourselves and our creativity into different domains.

Next week well look at our fourth and final industry: Finance. See ya then!

About the Author

Brandon Beylo

Student of value investing for over 13 years spending his time in small to micro-cap companies, spin-offs, SPACs and deep value liquidation situations.

See the article here:
Where Were Finding AI Investment Opportunities: Education - ValueWalk

The Encampment Tigers' first varsity season in 30 years has come to an end, but not without a fight against the No. 1 team in the 1A West Six-Man Conference in the semi-final round of the 1A Six-Man Playoffs.

On November 7, the Tigers were hosted by the Farson-Eden Pronghorns to determine who would advance to the 1A Six-Man State Championship. For, at least, the first half of the game it looked as if Encampment would down the Pronghorns, who dealt them a loss in the regular season.

"This was a well played and hard fought game by both teams. From start to finish both teams got after it and it went down to the last minute of the game to determine the winner," said Kegan Willford, head coach of the Encampment Tigers. "It's really tough to lose games that are this close because you are always looking back to moments in the game where you could have made a different decision that maybe could have changed the outcome."

When the Pronghorns had hosted Encampment in September, they had sent the Tigers home with a loss of 32-74. The week before, Farson had downed the Hanna, Elk Mountain, Medicine Bow (Miners) with a 0-70 loss. The Tigers, however, were determined to continue to prove themselves as they narrowly lost, 41-42.

In the 1st quarter, Encampment led 7-6 thanks to a 60 yard rush from Caysen Barkhurst and a conversion from Koye Gilbert following a pass from Dalton Peterson. In the 2nd quarter, the Tigers continued their scoring as they doubled what they had done in the first 10 minutes.

A 15 yard rush from Peterson put Encampment at 13-6 and a 62 yard rush from Quade Jordan increased that score to 19-6. A point-after-touchdown from Michael Anderson added two more points to that lead. While Farson-Eden scored another touchdown before the end of the first half, the Tigers went into halftime leading 21-12.

When both teams returned to the gridiron, the Pronghorns had appeared to find their footing and tried to speed past Encampment. The 3rd quarter saw Farson-Eden put 14 points on the board while the Tigers only had 6 points, which came from a 40 yard rush by Jordan and Encampment still led 27-26.

In the final quarter, Encampment regained their footing and went nearly point-for-point against the Pronghorns. The gains by Farson-Eden in the previous 10 minutes, however, were difficult to overcome. The Tigers were able to put 14 points on the board, but trailed in scoring by two points against Farson-Eden.

Anderson scored a touchdown following a 24 yard pass from Peterson to push the lead 33-26. A flurry of scoring from the Pronghorns, however, kept Encampment scoreless for most of the final quarter as Farson-Eden switched the lead to go 33-42.

A 3 yard pass from Peterson to Kagan Gilbert, however, helped close the gap and a point-after-touchdown from Anderson put the Tigers 41-42 as the game ended.

Mary Martin

Michael Anderson kicks for a field goal against the Pronghorns on Saturday afternoon in Farson-Eden

"I am extremely proud of our team for putting forth great effort and never giving up. This was a game that not a lot of people were giving us a chance to be competitive in, but as a team we knew that we could go out and give ourselves a chance to win," Willford said. "We were in position to do just that, however we fell just short. It's unfortunate for the seniors to have this as their final football game, but they have left their mark on this program beyond just playing the games. These seniors were so instrumental in the creation of the program from going in front of the school board to volunteering and fundraising. This group should be proud of what they have accomplished in such a short amount of time."

In just two short years, the Encampment Tigers went from not having a six-man football program to ending their first varsity schedule in the semi-final round of the 1A Six-Man Playoffs against one of the top teams in the state. Though Willford is losing some key seniors after this season, it appears that there are enough future footballers to keep the program going for some time to come.

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Tigers leave their mark on Farson-Eden - The Saratoga Sun - The Saratoga Sun Homepage

Jon Koncak wasnt a household name as a 7-foot center in the NBA. Prior to his professional career with the Atlanta Hawks, Koncak was a high school standout and a college star at SMU. He was also a teammate of Michael Jordan on the 1984 U.S. Olympic basketball squad. Although he never outplayed Jordan, he outearned him for a bit in the NBA, earning the nickname Jon Contract.

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When Jon Koncak was in high school, he was a heavily-recruited 7-footer. In high school, he grew about three inches per year and hit the seven-foot mark by the time he graduated. Koncak was a talented basketball player recruited by big-time schools but wound up signing with Southern Methodist University.

At the time, SMU was in rebuilding mode under head coach Dave Bliss. Koncak, showing his sense of humor, explained to the Atlanta Journal-Constitution in 2019 why he chose SMU. Well, SMU coach Dave Bliss was the only one that could spell my name. All the other coaches spelled it John.

No, really, the Missouri coach called and just expected me to sign with them because I was from Kansas City. SMU, meanwhile, sent an assistant coach to, I think, every game my senior year. It was neat being part of a program that we brought into the forefront.

It was a tough go early at SMU as the Mustangs went 6-21 and 1-15 in conference play. Jon Koncak averaged 10 points per game as a freshman, but by the time he was a junior, Koncak and the Mustangs were rolling. As a junior, Koncak averaged 15.5 points and 11.5 rebounds per game. The Mustangs went 25-8.

I was recruited by 80 or 90 schools and I go to this place that had 4,000 kids and we won just six games my freshman year, Koncak told the AJC. We beat all the powerhouses and my junior year (1984), we were one point away from beating Georgetown and Patrick Ewing and they would go on to win the national championship.

In his senior year, SMU went 23-10 and Koncak put up 17.2 points and 10.7 rebounds per game. Koncak went on to become a first-round pick in the 1985 NBA draft. The Atlanta Hawks selected him with the fifth overall pick.

Jon Koncak spent 11 years in the NBA primarily as a backup center. In his rookie season, he averaged a career-best 8.6 points per game. He never averaged better than 5.7 points in any of the 10 other seasons. In 1989, however, Koncak signed an unheard-of six-year, $13 million contract for a backup center. According to the Atlanta Journal-Constitution, Koncak at one point was making more money than superstars Michael Jordan, Larry Bird, and Magic Johnson.

Koncak remembers why he got the big deal. During my fourth year, Kevin Willis had broken his foot and was out and (coach) Mike Fratello didnt want to play Moses (Malone) and I at the same time, he told the AJC. So he started Cliff Levingston. But one day, Cliff was late to practice and it ticked off Fratello. He came up to me and asked if I could play the No. 4 spot. I did and in the first game, I scored only eight points but had 20 rebounds. He kept me in the lineup and we went 13-3 and in a couple of those games, I scored 20 points. I finally felt things were going my way and the Pistons offered me the offer sheet and then came the huge offer from the Hawks. The first game the next season when I took my first shot in Atlanta, the fans booed. I had been a fan favorite before the contract. I became the scapegoat for what was happening to the Hawks.

Koncak knew it was a big contract, but he wasnt about to turn it down. Hey, I cant justify what they offered me, he told Sports Illustrated during his playing days. But what was I supposed to do? Say no? The league is changing. I think maybe this is just the start.

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Jon Koncak Once Made More Money Than Michael Jordan and Larry Bird, Earning the Nickname Jon Contract - Sportscasting