I sing my heart out to the wide open spaces
Pet Townshend
This month, we’ll study the difference between cost and price, why it matters, and how knowing how both behave in tandem is the key to success.
I know from our analytics that most of you are in the business of working out costs and prices. For many, it is hard to separate the two, especially if you work in or with the government. Today’s analysis directs itself to commercial operations. In a future newsletter, we’ll look at how to adapt this framework to the public sector.
All too often in business, someone comes up with a seemingly great idea and gets fellow workers excited about it. It gets pushed into production. Producers then wait to see what the market will bear for it, often falling short of projections.
What if you could change the paradigm?
Suppose you could see market openings and limits and test sample specifications and sales targets before you commit resources to a configuration. That would improve your chances of success.
You’ll have to work to enable this vision, but you will find it worthwhile.
When we at Hypernomics look at a market, we begin with Demand. As shown below as the red plane, that means finding the ordered pairs for Quantity and Price. We create a series of price bins (either equally spaced or binned by geometric or Fibonacci methods) and determine the ordered pairs (as the purple hexagons) representing each bin’s average price and total Quantity. Then we run a regression curve through them, which represents Aggregate Market Demand.
To the left of that curve, we find the Demand Frontier, a regression through the outermost points on the Demand Plane. This curve shows the limit of the products this market can absorb over time. As markets mature, the Aggregate Market Demand and Demand Frontier slopes often approximate one another.
If we examine the points closely, we’ll notice a price gap. Using its midpoint, we would find the 1) Quantity limit the market will support at that price (the vertical red line coming down from the Demand Frontier) and our 2) Target Price (the horizontal red line originating from the Demand Frontier).
To support that price, we’ll need to offer our customers something they like, here as Features A and B, which show up as the green Value Space at left, with the target Price as the horizontal red plane. We’ll need to figure out the Value Surface that the combinations of Features A and B command (the points for which we excluded from this view, for clarity). As seen on the left, there are Cost Surfaces for one or 500 units below the Value Surface. If we further bound our potential offering with Constraints (the vertical orange planes), we now have a region restricted on all sides. Conceptually, this expanse is not different than a like delimited region, such as your head.
Now, if you suspected that you had a deviated septum, your ear, nose, and throat doctor might order a CT scan, in which the doctor would develop section cut views of your head.
We can do the same thing in markets, using Financial CAT scans. Thus, after carefully setting up a 4D arrangement and taking cuts in both the Sections A and B directions, we can predict the 1) maximum Quantity Sold (reducing the 4D problem to one in 3D). Then we selected 2) the Price (dropping the remainder of undetermined dimensions to 2), 3) Feature A (the distance of the black plane from the origin, reducing the problem to 1 dimension), and 4) Feature B (the Vertical Profit Line, the final dimension). The per-unit profit line on the left times the number of units on the Demand Plane gives the projected profit.
In the process, we reduced a 4D problem to a single objective of maximum potential profit.
To complete the analysis, we’d examine all open price points and all viable combinations of the Features considering risk as well, searching for the best potential configuration.
Watch this video to see the analytical steps in action:
Together we stand, divided we fall; come on now, people, let’s get on the ball and work together.
Canned Heat, Let’s Work Together
Upper Demand Frontiers form in every market outside of commodities. You can prove this by plotting shares for all S&P 500 companies on the horizontal axis against their sales prices on the vertical (best seen in log-log space). For any given day, you’ll find an Upper Demand Frontier takes shape.
Frontiers limit sales. How do you work around them?
Hypernomics enables us to see how interconnected markets work. In (A), the Boeing 787 Business Jet was, for a period, pushed up against its Demand Frontier (the dashed blue line). One of its compatible engines, the GE Genx-1B, found itself in a like condition, hard up against its Demand Frontier (in B, the dashed orange line). What to do?
If GE, whose engines make up about a quarter of the B787 cost, finds their Learning Curve (recurring costs, as the solid orange line) below their price limit, they could drop their prices and make more profits. That would enable Boeing to lower their B787 business jet price and do the same. Knowing your partner’s place in the market is key to making them and you more profits.
#hypernomics #profits #markets #partner
https://hypernomics.com/wp-content/uploads/2022/05/ge-turbine-detail21-e1668687544111.jpg6451291Mdleminer472https://hypernomics.com/wp-content/uploads/2022/07/Hypernomics_Dan_Revised_5.pngMdleminer4722022-11-17 04:21:012023-01-27 13:19:51Help Your Partner; Help Yourself
It is the obvious which is so difficult to see most of the time.
Isaac Asimov, I, Robot
Here’s a question with a seemingly obvious answer: How many stocks are part of the S&P 500? If you guessed 500, you’d be close, as there are 504 companies listed there today.
You likely know that not all S&P companies have issued the same number of shares, nor do all share price match. Too obvious? Not really.
Consider what you were undoubtedly told if you ever took an economics class. According to Paul Samuelson (Economics, 9th Ed., p. 63), “the equilibrium price, i.e., the only price that can last…must be at the intersection point of supply and demand curves.” Samuelson would have you believe markets have but one equilibrium point.
But we know that is nonsense: 504 stocks in the S&P 500 form 504 quantity and price pairs. While they are viable, all, in the language of Hypernomics, enjoy sustainable disequilibrium as their stock prices exceed their costs.
What’s really going on? It turns out the value of products goes up as producers add features customers like. At the same time, as prices go up, quantities sold fall. To see this phenomenon, one must employ Hypernomics.
To find out how this works with as many as 8 dimensions, go to our new Hypernomics YouTube channel here:
Assumptions are what we don’t know we are making.
Douglas Adams
Launched in 2000, the Airbus ceased its A380 (A) production in December 2021, as the 251st unit rolled off the line. That’s lots of big jets. But, their 20-year goal was 1250. How did it go so wrong?
Assumptions are what we don’t know we are making – Douglas Adams
Launched in 2000, the Airbus ceased its A380 (A) production in December 2021, as the 251st unit rolled off the line. That’s lots of big jets. But, their 20-year goal was 1250. How did it go so wrong?
Many pundits claim they knew it wouldn’t make its target. Most appeared when the program floundered late in its lifespan. What would it take to predict its future in advance?
Projects often use 1) business case analyses and 2) customer polls to “verify it pencils out.” That works if 1) analysts conceive those cases fairly and 2) buyers convert at or above a target sales figure.
What if we don’t have to rely on those techniques?
To forecast the next 20 years, study the last 20. As B reveals (summing all model types to base versions), the airliner market had a poorly correlated (Adj R^2 0.458) yet statistically significant (P-Value 0.035) Demand Frontier over that period. Airbus’s target was nearly ten standard deviations past it.
The A380 took €25B to develop. It didn’t recoup its investment. Take time to model markets in advance. See what a market did to bound what it will do. You may not like the answers, but it beats losing billions.
https://hypernomics.com/wp-content/uploads/2022/08/a380-1.jpg9001600Doug Howarthhttps://hypernomics.com/wp-content/uploads/2022/07/Hypernomics_Dan_Revised_5.pngDoug Howarth2022-08-15 16:13:132023-09-22 19:34:02Assumptions vs. Observations: The A380
It’s not that I’m so smart, it’s just that I stay with problems longer.
Albert Einstein
There’s something deeply affecting about staying with a problem for over 30 years. Once you get some resolution, part of you wonders why it took so long to get answers. A more forgiving part of you thanks Einstein for the inspiration to carry on. One can only be happy when that ah-ha moment finally arrives.
Such is the case with Hypernomics. After first entertaining the idea at 14, somewhere around 49, I saw the first hints of the practical applications of Hypernomics. 18 years later, we have evidence of its practicability in one of the most complicated markets, that of securities.
In Feb 2020, we made our first investments based entirely on Hypernomics. Far from being perfect, tests suggested that given a market downturn, we would suffer losses. Figure A shows we’ve endured setbacks in 2022. But backtesting supported the idea we would lose less than the competition.
In the longer run, in Figure B, the theory has had a chance to shine. Note the Hypernomics fund is doing more than 2X as well as Berkshire Hathaway and over 3X what the other major indices are doing.
Tesla is here to stay and keep fighting for the electric car revolution.
Elon Musk
How do markets form? What happens when they do? Let’s look.
The modern mass-produced electric car market began in 2009. As Figure A displays, there was a sole entrant then, the Mitsubishi i-MiEV. By 2012, in Figure B, many more entrants came into play. Three years later, with Figure C, prices for most models fell, and they attracted more customers. Producers were able to drop prices because their production lines displayed learning curve effects. They benefited from lower costs from more efficient workers, standardization, economies of scale, and other factors.
Figure D shows us that by 2018, many new models moved into the market. Several models (all Teslas, marked with the yellow dots) combined to form the market’s Demand Frontier. That line is highly correlated (adjusted R2 95.4%, P-value 5.04E-04) and relatively flat, with a slope of -0.36.
In 2019, electric car sales fell about 10% from 2018, despite Tesla’s Model 3 success. Given the flattish Demand Curve, that suggests buyers would be eager for a high-range vehicle with a price lower than the Model 3. All competitors priced less than the cheapest Model 3 have less range than it does.
“Present fears are less than horrible imaginings.”—William Shakespeare
The FAA recently made clear it will impose more stringent requirements on Urban Air Mobility (UAM) vehicles. In response, pundits started screaming about the end of that market even before it began. They think the added costs are insurmountable.
Who told them that?
Yes, there will be significant costs in the added requirements. But many companies have managed to work themselves through FAA regulations and come out with safe and profitable models.
Profitability, of course, is the key. To clear development and certification costs, UAM manufacturers need to know if they can make money with their products. All should note the United Airlines (UA) order from Archer. UA will spend $5M for five-seat Archers, which are only slightly faster than five-seater Robinson R66s, which sell for $1.1M. Oh, and the Archer has about a seventh the range of the R66. The difference is the decibels. Lower noise widens the market for UAMs, which should have comparable or lower costs than traditional helicopters (due to fewer parts) and higher prices.
Next time someone tells you the sky (or UAM market) is falling, see what they’ve shorted.
https://hypernomics.com/wp-content/uploads/2022/05/faa.jpg312820Doug Howarthhttps://hypernomics.com/wp-content/uploads/2022/07/Hypernomics_Dan_Revised_5.pngDoug Howarth2022-05-22 16:33:532023-01-27 13:19:51Call Off the Panic
The good thing about science is that it’s true whether or not you believe in it – Neil deGrasse Tyson
What would it be like to do astronomy without a telescope, biology sans a microscope, or defend air raids without radar? We don’t have to live without these visualization aids in the modern world. We needn’t rely on Stone Age tech in the Age of Information.
But you are very likely working from a like disadvantage in market analysis. While we proved 4D data science works in fields as varied as beef production, package delivery, and spaceships, up till 2020, we had not taken a run at the stock market.
Then we did.
As shown below, the principles of Hypernomics have been applied successfully for picking securities, as it has for us for the last 26 months, with actual monies and stellar returns. This fund is yet another story about how we applied the tool profitably. We believe the fund will be to Hypernomics as books were to Amazon. Eleven years after we started, we’re looking for partners.
Yeah. Beethoven was deaf. Helen Keller was blind. I think Rocky’s got a good chance.
Adrian, ‘Rocky’
Funny thing about entering a game late. People think you can’t play just because you haven’t been on the field.
Make no mistake. We’re a late entrant. Many might think of us as a world-weary veteran reliever, coming in the bottom of the ninth to get out the last batter.
We see ourselves more as an untested first-round draft pick sitting on the bench. And we’ve been studying the game – and we think we’ve figured a few things out. Our fund reflects that.
We founded Hypernomics, Inc. (yes, it’s official, we were formerly MEE Inc.) to offer training, software, and consulting for the field we discovered, which, of course, is Hypernomics. We still do that, and that’s what’s kept the lights on.
More and more, though, our advisors and we are seeing the potential of this fund. We’re not open to the public, but we think one or more firms could benefit from licensing our analytics. Just as we didn’t know about Hypernomics until we discovered it, we don’t know what to do or where to go exactly.
https://hypernomics.com/wp-content/uploads/2022/02/hedge_fund.webp432964Doug Howarthhttps://hypernomics.com/wp-content/uploads/2022/07/Hypernomics_Dan_Revised_5.pngDoug Howarth2022-02-23 16:40:422023-09-22 19:54:36The Little Fund That Could – And Did And Does
It was 20 years ago today/Sergeant Pepper taught the band to play.
The Beatles
Tim is always with me.
That’s not a metaphor. It is a biological fact.
You see, 50 years ago, I was diagnosed with kidney disease. Back then, such a condition was often a death sentence. I knew kidneys excreted waste products and looked for ways to help mine out.
So, I started running – 30 to 50 miles a week. For decades. In the middle of that, in the early 1980s, I met Tim Schreiner while at Lockheed Martin, and we became friends. He ran with me too, and we liked the same bands and the cool stuff we helped build at LM.
Some 21+ years after my diagnosis, the kidneys failed, and I went on dialysis for a year and a half. Then I got a cadaveric kidney transplant, but it never worked right, and after another 6+ years, I was once again on the verge of kidney failure.
Enter Tim. Ten people offered to give me their kidneys. Tim demanded to be put at the front of the line. He tested, matched, and decided to save my life. I knew life would be better with a good kidney, but I never knew how much. Tim offered me so much; I can only hope to repay society a fraction of what he did for me. 20 years later, we’re both doing fine.
Financial Cat Scans
Cost, Price, and The Space Between
This month, we’ll study the difference between cost and price, why it matters, and how knowing how both behave in tandem is the key to success.
I know from our analytics that most of you are in the business of working out costs and prices. For many, it is hard to separate the two, especially if you work in or with the government. Today’s analysis directs itself to commercial operations. In a future newsletter, we’ll look at how to adapt this framework to the public sector.
All too often in business, someone comes up with a seemingly great idea and gets fellow workers excited about it. It gets pushed into production. Producers then wait to see what the market will bear for it, often falling short of projections.
What if you could change the paradigm?
Suppose you could see market openings and limits and test sample specifications and sales targets before you commit resources to a configuration. That would improve your chances of success.
You’ll have to work to enable this vision, but you will find it worthwhile.
When we at Hypernomics look at a market, we begin with Demand. As shown below as the red plane, that means finding the ordered pairs for Quantity and Price. We create a series of price bins (either equally spaced or binned by geometric or Fibonacci methods) and determine the ordered pairs (as the purple hexagons) representing each bin’s average price and total Quantity. Then we run a regression curve through them, which represents Aggregate Market Demand.
To the left of that curve, we find the Demand Frontier, a regression through the outermost points on the Demand Plane. This curve shows the limit of the products this market can absorb over time. As markets mature, the Aggregate Market Demand and Demand Frontier slopes often approximate one another.
If we examine the points closely, we’ll notice a price gap. Using its midpoint, we would find the 1) Quantity limit the market will support at that price (the vertical red line coming down from the Demand Frontier) and our 2) Target Price (the horizontal red line originating from the Demand Frontier).
To support that price, we’ll need to offer our customers something they like, here as Features A and B, which show up as the green Value Space at left, with the target Price as the horizontal red plane. We’ll need to figure out the Value Surface that the combinations of Features A and B command (the points for which we excluded from this view, for clarity). As seen on the left, there are Cost Surfaces for one or 500 units below the Value Surface. If we further bound our potential offering with Constraints (the vertical orange planes), we now have a region restricted on all sides. Conceptually, this expanse is not different than a like delimited region, such as your head.
Now, if you suspected that you had a deviated septum, your ear, nose, and throat doctor might order a CT scan, in which the doctor would develop section cut views of your head.
We can do the same thing in markets, using Financial CAT scans. Thus, after carefully setting up a 4D arrangement and taking cuts in both the Sections A and B directions, we can predict the 1) maximum Quantity Sold (reducing the 4D problem to one in 3D). Then we selected 2) the Price (dropping the remainder of undetermined dimensions to 2), 3) Feature A (the distance of the black plane from the origin, reducing the problem to 1 dimension), and 4) Feature B (the Vertical Profit Line, the final dimension). The per-unit profit line on the left times the number of units on the Demand Plane gives the projected profit.
In the process, we reduced a 4D problem to a single objective of maximum potential profit.
To complete the analysis, we’d examine all open price points and all viable combinations of the Features considering risk as well, searching for the best potential configuration.
Watch this video to see the analytical steps in action:
Help Your Partner; Help Yourself
Upper Demand Frontiers form in every market outside of commodities. You can prove this by plotting shares for all S&P 500 companies on the horizontal axis against their sales prices on the vertical (best seen in log-log space). For any given day, you’ll find an Upper Demand Frontier takes shape.
Frontiers limit sales. How do you work around them?
Hypernomics enables us to see how interconnected markets work. In (A), the Boeing 787 Business Jet was, for a period, pushed up against its Demand Frontier (the dashed blue line). One of its compatible engines, the GE Genx-1B, found itself in a like condition, hard up against its Demand Frontier (in B, the dashed orange line). What to do?
If GE, whose engines make up about a quarter of the B787 cost, finds their Learning Curve (recurring costs, as the solid orange line) below their price limit, they could drop their prices and make more profits. That would enable Boeing to lower their B787 business jet price and do the same. Knowing your partner’s place in the market is key to making them and you more profits.
#hypernomics #profits #markets #partner
Announcing The Hypernomics YouTube Channel
Here’s a question with a seemingly obvious answer: How many stocks are part of the S&P 500? If you guessed 500, you’d be close, as there are 504 companies listed there today.
You likely know that not all S&P companies have issued the same number of shares, nor do all share price match. Too obvious? Not really.
Consider what you were undoubtedly told if you ever took an economics class. According to Paul Samuelson (Economics, 9th Ed., p. 63), “the equilibrium price, i.e., the only price that can last…must be at the intersection point of supply and demand curves.” Samuelson would have you believe markets have but one equilibrium point.
But we know that is nonsense: 504 stocks in the S&P 500 form 504 quantity and price pairs. While they are viable, all, in the language of Hypernomics, enjoy sustainable disequilibrium as their stock prices exceed their costs.
What’s really going on? It turns out the value of products goes up as producers add features customers like. At the same time, as prices go up, quantities sold fall. To see this phenomenon, one must employ Hypernomics.
To find out how this works with as many as 8 dimensions, go to our new Hypernomics YouTube channel here:
https://www.youtube.com/channel/UCYsso5Yf0OFY3k78u5c30LQ
#hypernomics #marketanalysis #prices #demand
Assumptions vs. Observations: The A380
Launched in 2000, the Airbus ceased its A380 (A) production in December 2021, as the 251st unit rolled off the line. That’s lots of big jets. But, their 20-year goal was 1250. How did it go so wrong?
Assumptions are what we don’t know we are making – Douglas Adams
Launched in 2000, the Airbus ceased its A380 (A) production in December 2021, as the 251st unit rolled off the line. That’s lots of big jets. But, their 20-year goal was 1250. How did it go so wrong?
Many pundits claim they knew it wouldn’t make its target. Most appeared when the program floundered late in its lifespan. What would it take to predict its future in advance?
Projects often use 1) business case analyses and 2) customer polls to “verify it pencils out.” That works if 1) analysts conceive those cases fairly and 2) buyers convert at or above a target sales figure.
What if we don’t have to rely on those techniques?
To forecast the next 20 years, study the last 20. As B reveals (summing all model types to base versions), the airliner market had a poorly correlated (Adj R^2 0.458) yet statistically significant (P-Value 0.035) Demand Frontier over that period. Airbus’s target was nearly ten standard deviations past it.
The A380 took €25B to develop. It didn’t recoup its investment. Take time to model markets in advance. See what a market did to bound what it will do. You may not like the answers, but it beats losing billions.
#A380 #demandfrontier #hypernomics
Long Time Coming
There’s something deeply affecting about staying with a problem for over 30 years. Once you get some resolution, part of you wonders why it took so long to get answers. A more forgiving part of you thanks Einstein for the inspiration to carry on. One can only be happy when that ah-ha moment finally arrives.
Such is the case with Hypernomics. After first entertaining the idea at 14, somewhere around 49, I saw the first hints of the practical applications of Hypernomics. 18 years later, we have evidence of its practicability in one of the most complicated markets, that of securities.
In Feb 2020, we made our first investments based entirely on Hypernomics. Far from being perfect, tests suggested that given a market downturn, we would suffer losses. Figure A shows we’ve endured setbacks in 2022. But backtesting supported the idea we would lose less than the competition.
In the longer run, in Figure B, the theory has had a chance to shine. Note the Hypernomics fund is doing more than 2X as well as Berkshire Hathaway and over 3X what the other major indices are doing.
#innovation #markets #investments #hypernomics
Market And Demand Formation
How do markets form? What happens when they do? Let’s look.
The modern mass-produced electric car market began in 2009. As Figure A displays, there was a sole entrant then, the Mitsubishi i-MiEV. By 2012, in Figure B, many more entrants came into play. Three years later, with Figure C, prices for most models fell, and they attracted more customers. Producers were able to drop prices because their production lines displayed learning curve effects. They benefited from lower costs from more efficient workers, standardization, economies of scale, and other factors.
Figure D shows us that by 2018, many new models moved into the market. Several models (all Teslas, marked with the yellow dots) combined to form the market’s Demand Frontier. That line is highly correlated (adjusted R2 95.4%, P-value 5.04E-04) and relatively flat, with a slope of -0.36.
In 2019, electric car sales fell about 10% from 2018, despite Tesla’s Model 3 success. Given the flattish Demand Curve, that suggests buyers would be eager for a high-range vehicle with a price lower than the Model 3. All competitors priced less than the cheapest Model 3 have less range than it does.
#demand #marketanalysis #marketformation #demandformation #demandplanning #curve
Call Off the Panic
The FAA recently made clear it will impose more stringent requirements on Urban Air Mobility (UAM) vehicles. In response, pundits started screaming about the end of that market even before it began. They think the added costs are insurmountable.
Who told them that?
Yes, there will be significant costs in the added requirements. But many companies have managed to work themselves through FAA regulations and come out with safe and profitable models.
Profitability, of course, is the key. To clear development and certification costs, UAM manufacturers need to know if they can make money with their products. All should note the United Airlines (UA) order from Archer. UA will spend $5M for five-seat Archers, which are only slightly faster than five-seater Robinson R66s, which sell for $1.1M. Oh, and the Archer has about a seventh the range of the R66. The difference is the decibels. Lower noise widens the market for UAMs, which should have comparable or lower costs than traditional helicopters (due to fewer parts) and higher prices.
Next time someone tells you the sky (or UAM market) is falling, see what they’ve shorted.
#urbanairmobility #UAM #markets #innovation #FAA
Life’s Easier With Enhanced Vision
What would it be like to do astronomy without a telescope, biology sans a microscope, or defend air raids without radar? We don’t have to live without these visualization aids in the modern world. We needn’t rely on Stone Age tech in the Age of Information.
But you are very likely working from a like disadvantage in market analysis. While we proved 4D data science works in fields as varied as beef production, package delivery, and spaceships, up till 2020, we had not taken a run at the stock market.
Then we did.
As shown below, the principles of Hypernomics have been applied successfully for picking securities, as it has for us for the last 26 months, with actual monies and stellar returns. This fund is yet another story about how we applied the tool profitably. We believe the fund will be to Hypernomics as books were to Amazon. Eleven years after we started, we’re looking for partners.
Who wants to join us?
#datascience #hypernomics #innovation #marketanalysis
The Little Fund That Could – And Did And Does
Funny thing about entering a game late. People think you can’t play just because you haven’t been on the field.
Make no mistake. We’re a late entrant. Many might think of us as a world-weary veteran reliever, coming in the bottom of the ninth to get out the last batter.
We see ourselves more as an untested first-round draft pick sitting on the bench. And we’ve been studying the game – and we think we’ve figured a few things out. Our fund reflects that.
We founded Hypernomics, Inc. (yes, it’s official, we were formerly MEE Inc.) to offer training, software, and consulting for the field we discovered, which, of course, is Hypernomics. We still do that, and that’s what’s kept the lights on.
More and more, though, our advisors and we are seeing the potential of this fund. We’re not open to the public, but we think one or more firms could benefit from licensing our analytics. Just as we didn’t know about Hypernomics until we discovered it, we don’t know what to do or where to go exactly.
If you have some thoughts, please share them.
#hypernomics, #stockmarkets, #innovation, #stockanalysis
Love, Luck, And Technology
Tim is always with me.
That’s not a metaphor. It is a biological fact.
You see, 50 years ago, I was diagnosed with kidney disease. Back then, such a condition was often a death sentence. I knew kidneys excreted waste products and looked for ways to help mine out.
So, I started running – 30 to 50 miles a week. For decades. In the middle of that, in the early 1980s, I met Tim Schreiner while at Lockheed Martin, and we became friends. He ran with me too, and we liked the same bands and the cool stuff we helped build at LM.
Some 21+ years after my diagnosis, the kidneys failed, and I went on dialysis for a year and a half. Then I got a cadaveric kidney transplant, but it never worked right, and after another 6+ years, I was once again on the verge of kidney failure.
Enter Tim. Ten people offered to give me their kidneys. Tim demanded to be put at the front of the line. He tested, matched, and decided to save my life. I knew life would be better with a good kidney, but I never knew how much. Tim offered me so much; I can only hope to repay society a fraction of what he did for me. 20 years later, we’re both doing fine.
#transplant #love #luck #technology