I’m a big fan of Bret Victor, and have recently decided to read everything on his website again. Here are my notes to his article “How Many Households.” You can read the original here.
Key Takeaways
Show the data, compare the data, let the data guide reader interactions
The essence of data graphics is visual comparisons, even for static ones. Even if completely static, make sure the graphic informs. Add interactivity –judiciously, powerfully, consistently –to subset data space according to readers’ interests
Don’t make interactivity a barrier to information
Full Notes
The essence of data graphics is visual comparisons
Data can’t be compared when there are millions of data points, but only one is displayed at any given time
Many charts (like the one immediately above) require users to interact with a chart to switch between data points, often times through a convoluted navigation scheme with no clear information hierarchy, peek-a-boo menus that appear and disappear, and “actions at a distance” where clicking a menu causes a far-away image to change, and clicking the image causes the menu to change
This type of confused navigation scheme is not even the main problem!
The main problem is that any type of navigation is even needed. Chart readers have to continually interact with these charts, and have to beg for every scrap of info
The essence of data graphics is visual comparisons
An excellent graphic offers its info generously and eagerly, helping and encouraging its reader to answer wide-ranging comparative questions at a glance
An excellent interactive graphic offers many opportunities to explore the data space more precisely, to slice and filter the data, to see the data from different specific perspectives
Interactivity should NOT be a barrier to information
Keep information design and interactive design simple and consistent
Graphics should provide information right off the bat, and that information should inform the readers’ interactions
Every interactive element should be informational (ie bar graphs that display data AND provide a means to filter data; graphs that show distribution and slice the data in specific ways ie constrain a graphic to a specific variable)
There should be no UI buttons/control to clutter the graphic
Each feature should be offered simply by making the graphs consistent with the other graphs (ie click to filter)
Each click should potentially reveal a large amount of new data; this makes each interaction powerful, meaningful, and worthwhile. No gratuitous actions; every click rewarded
Some charts use unusual forms of multiple selection (ie when a category choice also needs a quantity specified, like the one immediately above)
Solution: being informational and consistent
For the second chart (“And who lives with them?”), say we want to see the data for “any children,” but also want to see the data for “1 child,” “2 children,” etc. Show all of the quantities as bar graphs, with the distribution of children expanding out when the “any children” bar is clicked
Interaction-wise, this graphic simply assumes the reader wants to specify one child, and immediately selects it, with the other selections just a click away
Keep designs stable. If something appears after a click, keep it there. Don’t move anything around, don’t make anything disappear.
This lets the reader take in and grasp the layout of the information space, and quickly explore new ideas without “navigation” getting in the way.
“Progressive disclosure,” where charts are introduced one by one, is to prevent the reader from being immediately overwhelmed by the graphic and skipping it (best used for general audiences, not necessary for sophisticated/motivated readers)
Another powerful technique is filtering on rollover instead of click
This allows reader to skim the mouse over a graph and immediately see the data sliced in every way –a huge information payoff for little interaction (can be awkward to integrate with charts like multiple-selection charts, but in general a solid technique)
In 2001, the MIT Technology Review published a special edition of Technology Review: 10 Emerging Technologies That Will Change the World. You can read the original publication here. These are my notes. Some additional takeaways at the end.
Since 2001, MIT Technology Review has done yearly Top 10 Emerging Technologies issues. Notes for the following years will follow. I also plan on separate posts for additional thoughts, updates since issue publication (on both the highlighted technologies and the mentioned researchers), and related topics (ie new breakthroughs, interesting startups, related research) for each publication.
1. Brain-Machine Interfaces
Miguel Nicolelis is a leader in a competitive and highly significant field, in which there are only about half-a-dozen teams pursuing the same goals: gaining a better understanding of how the mind works and using that knowledge to build implant systems that would make brain control of computers and other machines possible
Nicolelis, working at MIT’s Laboratory for Human and Machine Haptics scored an important first on the HBMI (hybrid brain-machine interface, a term Nicolelis coined) front: sending signals from individual neurons in a monkey to a robot, which used the data to mimic the monkey’s arm movements in real time
Monkey has sockets installed into top of skull that allow measurement of electrical signals from 90 neurons (4 separate areas of her cerebral cortex)
In the long-term, HBMI’s will allow human brains to control artificial devices designed to restore lost sensory and motor functions ie do for the brain what the pacemaker did for the heart
Implants will help shed light on some of the brain’s mysteries: neuroscientists still know very little about how the electrical and chemical signals emitted by the brain’s millions of neurons let humans perceive color or smell, give rise to the precise movements of Brazilian soccer players
“We don’t have a finished model of how the brain words. All we have are first impressions”
Nicolelis’ latest experiments show that by tapping into multiple neurons in different parts of the brain, it is possible to glean enough info to get a general idea of what the brain is up to
For the monkey, it’s enough info to detect the monkey’s intention of making a specific movement a few tenths of a second before it actually happens
Nicolelis’ team succeeded at reliably measuring tens of neurons simultaneously over several moths –previously a key technological barrier –that enabled the remarkable demonstration with robot arm
Remaining challenges: developing electrode devices and surgical methods that will allow safe, long-term recording of neuronal activities
Nicolelis is working on developing a telemetry chip that would collect and transmit data through the skull, without unwieldy sockets and cables
2. Flexible Transistors
Implementations of pervasive computing will require integrated circuits that are both cheap and flexible (tough for today’s silicon technology”
Scientists working on transistors based on organic (carbon-based) molecules or polymers (organic electronics are inexpensive to manufacture and compatible with plastic substrates); however, organics are far slower than silicon cousins
Breakthrough: Cherie Kagan made a compromise: transistors made from materials that combine the charge-shuttling power and speed of inorganics with the affordability and flexibility of organics
Hybrids may be far faster than amorphous silicon, and have a key advantage over silicon-based electronics: it can be dissolved and printed onto paper or plastic like particles of ink
Kagan’s transistors could compete with organic electronics in variety of applications like radio-frequency product ID tags, flat-panel video displays (sharper images for a fraction of the cost) that lead to affordable wall-sized displays or high-quality displays that pop out of your pen (if all goes well, could be used in cheap, flexible displays within 5 years)
Bright displays that fit in your pocket will require portable power: Kagan’s newest interest: cheap, flexible materials for solar cells to liberate pervasive computing from bulky batteries
3. Data Mining
Data mining, also known as knowledge discovery in databases (KDD): a system that can burrow through gigabytes of website visitor logs in search of patterns no one can anticipate in advance to compile a simple recommendation list rather than sorting through a few megabytes of structured data to find answers to specific queries
Usama Fayyad, the pioneer behind data mining, was working at GM compiling a huge database on car repairs that would allow any GM service technician to ask the database questions based on several car characteristics and get a response
Fayyad developed a pattern recognition algorithm to solve this, which was later used at NASA JPL to identify objects, and pursued by everyone from the military to doctors
Fayyad identified a need: companies needed someone to host their databases for them, and provide data-mining services on top, which led to him creating digiMine
Future: wide open as researchers move beyond original focus on highly structured, relational databases
Hot area: text data mining: extracting unexpected relationships from huge collections of free-form text documents.
UCB LINDI system has been used to help geneticists search biomedical literature and produce plausible hypotheses for the function of newly-discovered genes
Hot area: video mining: combining speech recognition, image understanding, and natural language processing to open up the world’s vast video archives to efficient computer searching
CMU Informedia II system is given CNN clips, it produces a computer searchable index by automatically dividing each clip into individual scenes accompanied by scripts and headlines
4. Digital Rights Management
Ranjit Singh, president of ContentGuard, spinoff of Xerox PARC, is on a mission to commercialize content protection in a wired world
Sits at ground zero of what may be bloodiest battle to shape the Internet during the 21st century’s initial decade: IP owners vs internet users (who want content to be freely distributed)
The internet allows perfect and totally frictionless distribution
Digital rights management (DRM) is the catalyst for a revolution in e-content, which will allow content owners to get much wider and deeper distribution than ever before, and see who is passing your content to whom
At its core, DRM amounts to an encryption scheme with a built-in e-business cash register, where content is encoded, and to get the key, a user needs to do something (ie pay money, provide an email address, etc). DRM providers deliver protection tools, whereas the content owners set the conditions
ContentGuard uses a multiple key approach; anyone receiving bootleg content would have to crack into all over again, so even if a hacker cracks a piece of content, he can’t distribute it
DRM isn’t ubiquitous for 2 reasons
Content owners are in the midst of a hard rethink about both pricing and distribution: how do you price 3 listens to a song, or a download of a low-res image that can’t be forwarded to others? They are currently trying out different models for valuing content
The user experience has to hide the complexity of protection technologies. Users have to be able to buy and consume content without jumping through hoops
Analysts don’t believe content can be protected in the Internet era; people want flexible access to content (re: Napster).
Napster is unstoppable, and even if courts stop it, the Internet’s enablement of frictionless distribution of digital content among millions will live on
The more content a business puts online, the faster it will want to put still more content up, because it will see the economic benefits and users will see the benefits of gaining access to more content, leading to a huge explosion (Network Effects)
5. Biometrics
Biometrics: identifying individuals by specific biological traits, has already emerged
Large companies use fingerprint sensors, facial recognition, iris-scanning
Consumers have been reluctant to adopt
Joseph Atick, President/CEO of Visionics (facial recognition), believes that the wireless Web will make consumer hungry for biometrics. PDAs and cell phones are becoming portals to users’ worlds, transaction devices, IDs, and maybe one day passports
With so much personal/financial information in one place, comes great need for security, which will drive biometrics. Security will drive need for biometric systems, other tech developments (increased bandwidth, camera phones, etc) will create infrastructure needed to put biometrics into consumer hands
Visionics is working to let people authenticate any transaction they make over the wireless Web using their own faces
Atick, while heading a lab at Rockefeller University, discovered that the brain deals with visual info much as computer algorithms compress files: because everyone has 2 eyes, a nose, lips, the brain extracts only those features that typically show deviations from the norm (ie bridge of nose, upper cheekbones), filling in the rest
Visionics develops FaceIt, which verifies a person’s ID based on a set of 14 facial features unique to an individual and unaffected by presence of/changes in facial hair/expression
Successfully used to fight crime in England and election fraud in Mexico
Signed merger agreement with Digital Biometrics to build the first line of “biometric network appliances” –computers hooked to the Net with capacity to store/search large databases of facial/biometric info. Appliances with customer ID data can receive queries from companies wanting to authenticate e-transactions. Accessing the system works with PDAs/desktops, but most will come from handheld devices
Also working with companies in Japan/Europe so consumers can capture their own faces and submit encrypted versions over the Net
Future: bringing back an old element of human commerce –restoring confidence that comes with doing business face to face
It will be 2-3 years before PDA and cell phone wielders will use biometrics instead of passwords and PINs
6. Natural Language Processing
New generation of interfaces arising that will allow extended conversation with computers; requires integration of speech recognition, natural language understanding, discourse analysis, world knowledge, reasoning ability, speech generation
DARPA working on interfaces that will ultimately include pointing, gesturing, and other forms of visual communication
IBM/Microsoft want a speech enabled “intelligent environment” where every object big enough to hold a chip actually has one; speech recognition necessary because they will each be too small to have a keyboard
Karen Jensen, chief of NLP at MSFT Research previously at IBM and contributed to MSFT’s Encarta encyclopedia and grammar checker, is now focused on MindNet, a system for automatically extracting a massively hyperlinked web of concepts from something like a standard dictionary
Let’s say a dictionary defines a motorist as “a person who drives a car”
MindNet uses automatic parsing tech to find definition’s underlying logical structure, identifying “motorist” as a kind of person, “drives” as a verb taking motorist as a subject and car as an object
Wants a conceptual networking tying together all of human understanding in words; show how 2 sentences said differently can mean the same thing
MindNet has proved to be great for translation –have 2 separate conceptual networks for English and a second language, then align the webs so English logical forms match other language equivalents –then annotate matched logical forms with data from English-other language translation so translation proceeds in either direction
7. Microphotonics
Photonic crystals are on the cutting edge of microphotonics: tech for directing light on a microscopic scale that will make a major impact on telecommunications
Goal: replace electronic switches with faster, miniature optical devices
None have the technical elegance and widespread utility of photonic crystals
Photonic crystals provide means to create optical circuits and other small, inexpensive, low-power devices that can carry, route, process data at the speed of light
Trend: make light do as many things as possible, won’t completely replace electronics though
Photonic crystals are to photons what semiconductors are to electrons: offering an excellent medium for controlling the flow of light
Crystals admit or reflect specific photons depending on wavelength and crystal design
MIT Prof John Joannopoulos suggested that defects in crystals’ regular structure could provide an effective and efficient method to trap the light or route it through the crystal
Mold the flow of light by confining light and figuring out different ways to make light bend, go straight, split, come back together in the smallest possible space
Breakthrough: Explained how crystal filters could pick out specific streams of light from the flood of beams in wavelength division multiplexing (WDM), tech used to increase amount of data carried per fiber
Helped set the stage for the world’s smallest laser and electromagnetic cavity, key components in building integrated optical circuits
Even with an all-optical Internet, other problems loom:
Advancements due to improving fibers and tricks like WDM, but in 5-10 years, experts fear it won’t be possible to squeeze any more data into fiber optics
“Perfect mirror” photonic crystals may be the solution: reflect specific wavelengths of light from every angle with extraordinary efficiency. Hollow fibers with this reflector could carry up to 1000x more data than current fiber optics. It doesn’t absorb/scatter light like glass, so it could also eliminate expensive signal amplifiers needed every 60-80km for today’s optical networks
What are the theoretical limits of photonic crystals?? How much smaller can they be made? How can they be integrated into optical chips for telecom/computers?
Once you start being able to play with light, a whole new world opens up
8. Untangling Code
Gregor Kiczales, scientist at Xerox PARC, champions “aspect-oriented programming,” a technique that will allow software writers to debug code as easily as it is used by laymen
“Crosscutting” refers to capabilities, like logging and security and synchronization, that are the same kind of shortcuts those in other professions have been using for a while
Logging: ability to trace and record every operation an application performs; only works if people remember to follow it
Security and Synchronization: ability to make sure that 2 users don’t try to access the same data at the same time; requires programmers to write the same functionality into many different areas of the application
Keeping track of crosscutting concerns is error-prone; forget to upgrade just a few instances, and bugs start to pile up
Kiczales proposes a new category in a language called an “aspect,” which allows programmers to write, view, and edit a crosscutting concern as a separate entity
Meaning less buggy upgrades, shorter product cycles, better/cheaper software
Many firms already have a version, but Kiczales is the first to take it to the real world by incorporating it into a new extension of Java
Northeastern: adaptive programming; IBM: subjective programming; University of Twente: composition filtering; Elsewhere: multidimensional separation of concerns
Major changes in programming methodology can take 30 years for acceptance, aspect could cut that cycle down by 15-20 years.
Crosscutting concerns aren’t actually hard to work with, once you have the proper programming support
9. Robot Design
Big obstacle: expensive to design and make robots smart enough to adapt readily to different tasks and physical environments the way human being do. How do builders build more complexity into robots without custom-tailoring each one?
Robots stuck in commercial niche doing simple, repetitive jobs (ie assembly line, mass production of toys, etc)
Promising approach: automate the design and manufacture of robotics by deploying computers to conceive, test, and even build the configurations of each robotic system
Jordan Pollack of Brandeis, directed a computer to design a moving creature using a limited set of simple plastic parts: plastic rods, ball joints, small motors, and a “brain” (neural network)
The computer –using an algorithm inspired by biological evolution –evolved hundreds of generations of potential designs, killing off the sluggish and refining the strong; bringing to life the strongest with a rapid-prototyping machine
Important point of coevolutionary design and automated manufacturing for robotics is to get small-quantity production to be economical (expects first cheap industrial robots to be 5-10 years away)
Before robots reach out into everyday world of businesses and households, they need their own version of Moore’s Law: becoming dramatically more affordable and powerful over time
Designing even relatively simple robots is a painstaking task: Honda has spent 14+ years building a humanoid robot able to walk, open doors, navigate stairs
10. Microfluidics
Microfluidics: a promising new branch of biotechnology with the idea that once you master fluids at the microscale, you can automate key experiments for genomics and pharmaceutical development, perform instant diagnosis tests, even build implantable drug-delivery devices –all on mass-produced chips
Microfluidics will do for biotech what the transistor did for electronics
Problems: developing general tech that can be used for a broad range of applications with several functions to be integrated into a single chip. Manufacturing, particularly silicon micromanaging, is so expensive that experts question if the products using these techniques can ever be economical to manufacture
Stephen Quake’s group (at CalTech), unveiled a set of microfabricated valves and pumps –a critical first step in developing tech general enough to work for any microfluidics application
To make microfluidics cheaper, Quake is casting them out of soft silicone rubber in reusable molds (“soft lithography”)
Potential for mass-produced, disposable microfluidic chips that make possible everything from drug discovery on a massive scale to at-home tests for common infections
First was a microscale DNA analyzer that operates faster and on different principles than conventional full-sized version, then a miniature cell sorter, and most recently, those valves and pumps
Quake finished bachelors and masters at Stanford in physics in 4 years, got bored, and started focusing on “the physics of biology” and was hired at CalTech as the first interdisciplinary Professor, and gained tenure at just 31 years old
Quake founded a startup called Mycometrix, which has licensed all of Quakes microfluidics patents from Caltech, and is planning to deliver its first microfluidic devices to researchers soon (HP and Motorola are trying as well, but only Mycometrix has actually brought a product to market)
Quake more interested in basic biology questions: How do the proteins that control gene expression work? How can you do studies that cut across the entire genome?
Now that Quake has some neat tools, he’s looking to do some science with them
Quake is the prototypical innovator: he has ability to work in all areas, from basic research to hot commercial markets.
Some Additional Thoughts:
It’s been almost 19 years since this publication came out: it’s interesting to see that many of the scientists predictions in terms of time have been off. It seems like they ran into science vs engineering problems (separate post on this later).
Researchers back then said breakthroughs were 5-10 years away. One has to wonder which breakthroughs scientists are saying are right around the corner are actually right around the corner, and which are still far off dreams (re:autonomous driving).
Many of the technologies highlighted are being highlighted today; Not sure if they went through a winter and are currently going through a resurgence, or if they have been hot his entire time. Are technology hype cycles 10 years? 20 years?
Good mix of university researchers and startups. Today hot startups/companies working on these technologies include:
Natural Language Processing: IBM, Bytedance, Facebook
Do researchers make good entrepreneurs? At the very least, most of the startups mentioned here seem to be out of business or now part of other companies. But is this due to natural life cycle of tech companies or researcher competency?
What kind of breakthroughs need to happen to actually make technologies reality? We know neural networks in their current form (backwards propogation) have been around since the 1980s. Advanced sensors and chips are what led to the massive collection of data and explosion in computing power that has driven the AI boom of the last decade (Thanks Nvidia, Thanks Google).
What are the key drivers of a technology?
When will the catalyst(s) that propel them forward occur?
Who is leading the charge for each field?
Why do these technologies even matter? Do they even matter?
Where will these breakthroughs happen? Where geographically? Which disciplines? Cross border? Interdisciplinary?
How will these innovations change the way we live? Will they help us thrive, or just survive?
On 5/7/2013, Stanley Druckenmiller and Geoff Canada gave a presentation at Bowdoin College titled: Generational Theft. You can watch the entire thing here. These are my notes. Additional thoughts/takeaways at the end.
Entitlements:
Websters: A right you have under the Law
Actual: A benefit that people have under the Law that they don’t have to provide a current service for
Income Supplement (lots of Press, but not really anything)
Unemployment insurance claims
Food stamps
Three Main Buckets (big big money) – Primarily for the Elderly
Medicaid: Income based (70% goes to elderly)
Social Security
Medicare
Druckenmiller started worrying about entitlements in 1994
In 2011, the baby boomers, the front end of what was going to turn 65, and there would be a huge surge in entitlement payments
Whats the demographic issue?
Chart 1: Federal Government Entitlement Transfers as a Percentage of Federal Budget Outlays
NATION OF TAKERS
Services/Benefits people get while not providing a current service
1960: 28% of Federal Outlays
2010: 68% of Federal Outlays
Used to be 1/3 of Defense, now much more
Biggest increase took place in Nixon, Ford, GWB admins (NOT a partisan increase)
Elderly taking a bigger and bigger share even BEFORE baby boomers reach that age
Chart 2: Benefit per oldster/GDP per capita
THE LEVEL OF GENEROSITY TO THE ELDERLY HAS ALMOST DOUBLED IN THE LAST 40 YEARS, UNSUSTAINABLE
Benefit/Oldster has grown MUCH quicker than GDP per capita since the 70s
1970: 41%
2010: 72%
Chart 3: Average Consumption by Age Group
YOUNG’S CONSUMPTION IS UP 38% IN 30 YEARS, WHILE THE OLDS CONSUMPTION IS UP 164%
People receiving outlays are about to explode
1960s: 20s consumed much more than people in their 70s
1990s: 70s consumer much much more than people in the 90s
AARP taking money from you and giving to the elderly
Chart 4: US Poverty Rates by Age Group
POVERTY RATE FOR THE ELDERLY HAVE COLLAPSED, NOT SO FOR CHILDREN
Elderly: 35% -> 9%
Children: 25% -> 21%
Minority Children: 31% -> 35%
Massive wealth transfer from young to elderly
Chart 5: Average Children per woman
FERTILITY RATES HAVE GONE DOWN DRAMATICALLY
Fertility Rates:
1950s: 3.7
2011: 2.06
In 1957, there were 100MM less people in the US, and they were having more babies than we are today
Those babies are about to become seniors, who have been taking more and more, and there are a lot of these babies about to become seniors
Chart 6: US Life Expectancy at Birth (Years)
US LIFE EXPECTANCY HAS GONE UP SHARPLY
1900 to Now
Men: 47.9 -> 74.9
Women: 50.7 -> 79.9
Not only will there be MORE oldsters, they will be around for a much longer period of time
Seniors:
A) Bigger Share
B) More of them
C) Taking for a longer period of time
Chart 7: Federal Spending by Entitlement’s Program (% of GDP)
THE ENTITLEMENTS PROBLEM IS JUST BEGINNING
Percentage of GDP
3 Buckets: MMSS
Now to 2050: 10% to >20%
Historically, tax revenues as % of GDP have a ceiling around 20%
Entire tax revenues will be going to people who don’t even provide a service
Chart 8: US Population Ratio: Working Age to 65 and Over
DEPENDENCY RATIO IS JUST STARTING TO FALL
By 2030, there will be HALF the amount of young to support the old
Working age: 18-64: Grow 17% from 2010-2050
Elders: 65+: Grow 102% from 2010-2050
4.8 workers supporting elders
2030: 2.9
2050: 2.4
Huge recipient pool, less people giving money to
US Centenarians (Thousands) are the fastest growing age population
Oldsters growing at 102%
85+ growing at 343%
100+ growing at >1000%
Spend A LOT more on medicare than people in 60s and 70s
Chart 9: The True “Fiscal Gap”
IF TAXES RATES AND THE LEVEL OF GENEROSITY IN ENTITLEMENTS PROGRAMS REMAIN THE SAME, WE HAVE A MASSIVE PROBLEM AHEAD. EITHER TAX RATES RISE OR GENEROSITY FALLS. THERE IS NO ALTERNATIVE
Total Debt (On the Books): 11Trillion
Fiscal Gap: 211Trillion
Expected tax revenues – present value of benefits promised
Labor: Pay a payroll tax: supposedly to pay social security and future medicare: but it doesn’t pay for you, it pays for the current elders
All you have is a promise you’ll have some
Deficit: 10% of GDP, actually 15% of GDP
Chart 10: Federal Taxes/Expenditures
THE COST OF WAITING IS ENORMOUS
The increase in federal taxes and expenditures needed to cover fiscal gap today is much lower than in 40 years
Increase in all federal taxes:
Today: 64%
20 years: 77%
40 years: 93%
Decrease in all federal expenditures (defense, entitlements, etc)
Today: 40%
20 years: 46%
40 years: 53%
Chart 11: Raising taxes on the rich is not the solution
10%, 1000bp Rise is Millionaire Tax Rate: $92B
2010 Deficit: 1.3Trillion (not including off balance sheet)
At 50%, every day you work until June 30th, all your income goes to someone else
You don’t get anything till July 1st
If you tax the rich, they:
Stop working
Move
Even if you get the money, it doesn’t move the needle
Chart 12: 2011 US Defense spending exceeded the 13 next highest defense budgets combined
USA: 711B
China: 130B
Entitlements growing MORE than 711B, so if you cut 100%, still screwed
Chart 13: Net worth by age of household head in 2007
LOWER CAPITAL GAINS AND DIVIDENDS IS A DIRECT TRANSFER FROM YOUNG TO OLD
20-34: 100K
65-74: 1MM
Chart 14: US Consumption by age (ratio to labor income ages 30-49)
THE BIASED GROWTH OF THE WELFARE STATE
1960:
X-axis: Age
Y-axis: What the average 30-49yr old makes in
90yr old is spending 138% what 40 year olds makes
Spending 2x what 30 year olds spend
Lots to keep elders alive an extra 5 days
Chart 15: National Health Spending across countries
AN ADDITIONAL PROBLEM OF INEFFICIENCY. US not even close to any other country
US: 17.6
Average: 9.5
Chart 16: Despite spending 2x average country, health outcomes are bad
Spending the most, yet near the bottom for health outcomes
Why? Screwed up system: Malpractice insurance scam of an industry
Lawyers taking a bigger bite from physician income – NOT the biggest problems
Incentivizes hospitals to run insane (10-15 tests) that studies have shown don’t actually make an impact on end result
CYA policy (cover your ass)
Medical bills all done through insurance – people have no idea what they pay
HAVE TO force people to shop (aka know the prices), otherwise things won’t improve
Chart 17: US National Saving and Domestic Investment Rates (%)
AGING HAS COME WITH MORE CONSUMPTION AND LESS INVESTMENT
Some Additional Thoughts:
As of this post, it has been 6 years since Druckenmiller went on his college campus tour. Unfortunately, it appears that he was unable to inspire the youth to go out and fight for their future.
Interestingly, the Medicare and Medicaid have been core issues that politicians have been campaigning around. However, there has yet to be a convincing payment proposal. The wealth tax in particular has been a central focus for the Democratic Party in the run-up to the 2020 election.
It is increasingly clear that the ElderTech market will be far bigger than anyone imagined. However, there has yet to be a majorly-funded ElderTech startup, nor the establishment of any ElderTech venture capital funds/teams.
Historically, America has hidden (or solved, depending on who you ask) its demographic issues by taking in immigrants. However, since the 2016 US Presidential Election, relying on immigrants is becoming less and less likely, whether that be for blue collar jobs or white collar jobs.
America’s demographics seem to be following in the footsteps of Japan, Korea, the Scandinavian countries, and many other countries: less babies, less immigration. Will this lead to a stagnating US economy?
Given the increasing Elder population and shrinking Youth population, it is more important now than ever to invest in our children. Currently seeking out children/education-focused startups.
Note: Thought of the Malthusian Trap and a running out of resources. Similar to the industrial revolution, the implementation of AI will likely increase human productivity by leaps and bounds. But will it be enough?
