My candidacy would appeal triply to members of the political “donor class” who aren't psychopaths and/or (enablers of) kleptocrats
Re: said class
From 2021 book Forward: Notes on the Future of Our Democracy, by 2020 presidential candidate Andrew Yang:
The maximum any individual could legally give to a candidate was $2,800. Ordinarily people who give at that level play in politics habitually. My team procured a list of traditional Democratic donors—these kinds of lists are publicly available—and said, “Look, these are people who give, often to several candidates. If you call them and work on them, you can get them to donate to you. They know each other too, so it’s how you start building a network of donors.”
Re: appeal triply
— Summary (details follow) —
Because of my Amazon-/Microsoft-/VC-praised AI-preneurship (mAIP) and my subsequent work that builds on mAIP, I’m uniquely qualified to:
prevent/subdue said threat to many/most people
speed the AI-powered advancement of: 1) human-longevity science, 2) medical research more generally
provide the most popular implementations of particular online-markets that will give rise to many lucrative investment opportunities
Re: #1
ike1952yang2020ruscica2024.substack.com/p/threat-to-many-or-most-people (hereafter referred to as URL)
Re: #2
— Re: human-longevity science —
From 2020 book The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives:
[Google’s] Ray Kurzweil and longevity expert Aubrey de Grey have begun talking about “longevity escape velocity,” or the idea that soon, science will be able to extend our lives by a year for every year we live. In other words, once across this threshold, we’ll literally be staying one step ahead of death. Kurzweil thinks this threshold is about twelve years away, while de Grey puts it thirty years out.
From 2019 book Lifespan: Why We Age―and Why We Don't Have To, by the Harvard geneticist who’s one of Time magazine’s “100 most influential people” of 2014:
“It is not at all extravagant to expect that someday living to 150 will be standard. And if the Information Theory of Aging is sound, there may be no upward limit; we could potentially reset the epigenome in perpetuity.”
“How long will it be before we are able to reset our epigenome, either with molecules we ingest or by genetically modifying our bodies, as my student now does in mice? How long until we can destroy senescent cells, either by drugs or outright vaccination? How long until we can replace parts of organs, grow entire ones in genetically altered farm animals, or create them in a 3D printer? A couple of decades, perhaps. Maybe three. One or all of those innovations is coming well within the ever-increasing lifespans of most of us, though. And when that happens, how many more years will we get? The maximum potential could be centuries . . .”
“If I am wrong, it might be that I was too conservative in my predictions.”
“When technologies go exponential, even experts can be blindsided.”
“We often fail to acknowledge that knowledge is multiplicative and technologies are synergistic.”
Title of a 2020 article on CNBC.com:
The ultra-rich are investing in companies trying to reverse aging. Is it going to work?
From cbinsights.com:
— Re: the AI-powered advancement of human-longevity science —
From 2021 book The Science and Technology of Growing Young: An Insider’s Guide to the Breakthroughs that Will Dramatically Extend Our Lifespan . . . and What You Can Do Right Now (my emphases):
Insilico calls its AI drug discovery tool Generative Tensorial Reinforcement Learning (GENTRL). Once trained, the algorithm starts to “imagine” new molecules with the desired properties. This process not only vastly reduces the time it takes to discover molecular candidates and enables the creation of molecules that do not yet exist in molecular libraries; it does so with a much higher degree of success than conventional trial and error, and at a much lower cost. Insilico has used its AI to find better alternatives to existing medications as well: it developed a precision medicine system called Inclinico that predicts which patients are most likely to respond to a particular drug. It provides this capability as a service to pharmaceutical companies but also ranks the drugs by their predicted ability to target the mother of all diseases—aging itself.
Insilico is not the only biotech company using AI to discover, create, and optimize pharmaceutical treatments. There are already more than two hundred start-ups and multiple big-pharma companies pursuing an ambitious set of goals that will soon completely disrupt the pharmaceutical industry.
From 2020 book Longevity Industry 1.0 (my emphases):
“AI for Longevity is the ‘smart money’ sector of the industry, and can achieve enormous results and accelerated timelines in terms of progress in actual, tangible, real-world Healthy Human Longevity, even with comparatively tiny levels of financing compared to other sectors.”
“The intensive application of AI to all stages of Longevity and Preventive Medicine R&D has the potential to rapidly accelerate the clinical translation of both validated and experimental diagnostics, prognostics and therapeutics, to empower patients to become the CEOs of their own health through continuous AI-driven monitoring of minor fluctuations in biomarkers . . .”
“AI will come into prominence as the critical and fundamental driver of progress in the industry . . .”
From November 30, 2020 on Google News:
— Re: I’m uniquely qualified to speed the AI-powered advancement of: 1) human-longevity science, 2) medical research more generally —
Details follow, in two parts:
2.1) Re: my Amazon-/Microsoft-/VC-praised AI-preneurship (mAIP)
2.2) Re: mAIP and my subsequent work will speed said AI-powered advancement
Re: #2.1
See the 237-page pdf at URL. Excerpt:
Re: my AI-preneurship
— Summary (some details follow; more below) —
Key goal: owning/operating (OOing) the leading online-market for AI and customized-education (i.e., OOing the Amazon of AICE (AoAICE)).
A key to OOing AoAICE is OOing the most popular implementation of my Amazon-/VC-praised* design that:
will yield a next-gen variant of LinkedIn (NGLI)
fixes the fatal flaw of 2003 “sensation” BlogShares.com
A key to OOing NGLI is providing said disruptive innovations.
* From said 2004 email sent to me by Amazon’s first Director of Personalization:
We thought a lot about reputation systems. We thought a bit about personalized advertising systems. We thought a lot about blogging and social networking systems. . . . [W]e’ve been working a very similar vein to the one you describe . . .
— Name of my planned startup —
The Opportunity Services Group (OSG)
— Re: NGLI —
OSG’s 1.0 implementation of the site/app will feature:
a market for the advertisement spaces on solo-blogger blogs (e.g., portfolio blogs)*
a virtual currency (cash transactions will be supported also)
Prices in OSG’s virtual currency (OVC) will contain/reflect only truthful peer ratings of work samples. Ratings of this kind are a top predictor of work performance, according to a much-cited meta-analysis of 85 years of personnel-selection research (6149 citations as of December 9, 2021)**. Other top predictors of work performance are often unavailable (e.g., test results). So OVC prices will be ideal for ranking people within individual job/skill categories. These rankings will make it much easier for Jane Q. Upwardly-Mobile to identify others who (can) best complement her (ditto for John Q.).
* An ad space sold for OVC will typically be on the homepage (i.e., front page) of the seller’s blog; key reasons: 1) sales of spaces for OVC will occur via weekly auctions, 2) per week, each blogger will be able to sell only one ad space for OVC (which space is sold can vary weekly). Keywords re: said auctions: sealed-bid, second-price; combinatorial auctions via fractional allocations, so each week’s auction will provide a “spot” market and an “up-front” market; traders will make these markets “information-efficient.”
** From 2015 book Work Rules!: Insights from Inside Google That Will Transform How You Live and Lead, by Google’s then head of “People Operations”:
. . .
From the Schmidt-Hunter paper linked-to above:
— Name of OSG’s planned site/app —
Adver-ties
— Re: Adver-ties will be a debugged version of BlogShares.com —
From a 2003 article* on rediff.com:
The latest sensation that’s grabbing the attention of netizens is BlogShares
. . . an online stock market in which you get to speculate on the future of your favourite blogs. . . . Every player gets 500 BlogShare dollars upon signup.
. . . How you play BlogShares depends on what you want from it. For some, the objective is to get their blogs on the Top 100 Index.
. . . At the end of a three-week phase of beta testing, there were a staggering 40,000 listed blogs. Over 5000 active players carry out thousands of transactions every day . . .
* See the References section.
— Re: the fatal flaw of BlogShares —
The price mechanism was easily gamed. From the rediff.com article:
[Inbound] links are the assets that drive valuations.
— Re: bloggers will be able to parlay a high and/or fast-rising ad rate in OVC into cash via: 1) sales of other ad spaces, 2) affiliate-marketing commissions, 3) subscriptions —
KWs [i.e., keywords]: influencer marketing (IM), antidote to the epidemic of IM fraud. Some details follow; more are (linked-to) below.
Izea: 71% of influencers had a blog in 2018; 39% of advertisers sponsored blog posts in 2018; 67% of social-media users in 2020 aspired to be paid social-media influencers.
Mediakix: 44% of advertisers considered blogs to be among the social-media channels that were most important for IM in 2020 (Facebook: 45%; Twitter: 33%; LinkedIn: 19%); 50% of advertisers considered fraud the #1 drawback of IM in 2020.
— Re: a high and/or fast-rising ad rate in OVC will be achievable partly via OSG’s prediction markets (OPMs) —
High prices/rankings in OPMs will serve as PageRank-like pointers to high-quality blogs. Details are linked-to below. Keywords: OPM prices denominated in OVC.
From 2018 book Prediction Machines: The Simple Economics of Artificial Intelligence, published by Harvard Business School Press:
AI is a prediction technology . . .
. . . What will new AI technologies make so cheap? Prediction.
. . . When prediction is cheap, there will be more prediction and more complements to prediction.
— More precedents for Adver-ties —
Google’s PageRank search algorithm (first use of hyperlinks to inform search results)
peer assessments associated with popular MOOCs (massively open online courses)
LinkExchange.com
GitHub.com
PageRank 1.0 was based on insights from social-network analysis that were decades old when PageRank was conceived. (Similarly, LinkedIn et al. could’ve productized said 85-years-of-personnel-selection-research long ago.)
From a 1998 paper co-authored by Google’s founders:
There has been a great deal of work on academic-citation analysis [Gar[19]95]. Goffman [Gof71] . . .
Number of commercial search-engines launched before Google: 20.
From 2013 paper “Tuned Models of Peer Assessment in MOOCs,” co-authored by several employees of MOOC provider Coursera ($447M raised):
Peer assessment—which has been historically used for logistical, pedagogical, metacognitive, and affective benefits . . .—offers a promising solution that can scale the grading of complex assignments in courses with tens or even hundreds of thousands of students.
From the 1998 article in The Wall Street Journal titled “Microsoft Buys LinkExchange For About $250 Million in Stock”:
LinkExchange . . . places ad banners on about 400,000 Web sites, though many of those sites are obscure personal homepages [e.g., blogs] . . .
LinkExchange, founded in 1996, has taken a unique approach that has allowed it to grow its network of sites very quickly. The company allows member Web sites to advertise for free on other sites throughout the LinkExchange network—provided they agree to return the favor.
From a 2016 article on the website of Harvard Business Review (my emphases):
How can companies get a better idea of which skills employees and job candidates have? . . . One potential model is GitHub.
. . . Ideally, this [desired variant of GitHub] would also be a social network and e-portfolio, allowing an employer to see samples of work and trust that the skills presented had been validated by others. (The social component of GitHub is important to underscore because other developers validate and consume another developer’s work. This contrasts starkly with the “skills”— if we can call them that—that users can tag so quickly on LinkedIn, such as “higher education” or even “ninja.”)
— More re: the business case for Adver-ties —
LinkedIn was acquired by Microsoft for $26.2 billion in 2016.
Title of a 2018 article on TechRepublic.com:
Why Linkedin + GitHub profiles could be the hidden gem in $7.5B Microsoft acquisition [of GitHub]
— Re: popularizing Adver-ties will be foundational for popularizing OSG’s market for AI & CE —
Outputs from activity at and around Adver-ties (e.g., prices) will be inputs to OPMs. After Adver-ties catalyzes the popularization of 1.0 OPMs, outputs from activity at and around the OPMs (e.g., 2.0 OPMs) will be inputs to Adver-ties (i.e., the popularization of Adver-ties and OPMs will become mutually reinforcing). Both sets of said outputs will be inputs to OSG’s market for AI/CE (e.g., the outputs will help/enable consumers of AI/CE to feel confident that they’re receiving value for their expenditures).
Precedent for said dependencies between markets
financial-capital markets (e.g., prices output by an equities market are inputs to an equity-derivatives market)
Re: outputs from Adver-ties being inputs to OSG’s AI/CE market
From the 2015 article in The New York Times titled “Finding a Career Track in LinkedIn Profiles”:
[M]uch of what we need to know about the changing labor market is crowdsourced in real time. And many of those digital breadcrumbs end up in LinkedIn profiles.
From a 2015 interview of Michael Horn, co-author of 2008 book Disrupting Class: How Disruptive Innovation Will Change the Way the World Learns:
[W]e’re really in the early beginnings of the dramatic revolution that we’ve seen in a lot of other technology sectors where really smart recommendation engines come in and assist the student in picking and choosing their unique path. . . .
In order to really go towards adaptive learning, you need huge numbers of students on your platform . . .
We need platforms that can collect the data we need and can make better use of data so that we can figure out different ways to serve different learners.
Disrupting Class was co-authored by the late Harvard Business School professor Clayton Christensen, originator of the canonical models of disruptive innovation.
From 2016 book Thank You for Being Late: An Optimist’s Guide to Thriving in the Age of Accelerations:
At the high end of the labor ladder, professionals already have a global intelligent algorithm to draw on: LinkedIn, the career professional social networking site. But its founders now want to extend that intelligent algorithm to the whole world of work by creating a global “economic graph.” Here is how LinkedIn’s CEO, Jeff Weiner, describes it on his company blog:
Reid Hoffman and the other founders of LinkedIn initially created a platform to help people tap the value of their professional networks, and developed an infrastructure that could map those relationships up to three degrees. In doing so, they provided the foundation for what would eventually become the world’s largest professional graph.
Our current long-term vision at LinkedIn is to extend this professional graph into an economic graph by digitally manifesting every economic opportunity [i.e., job] in the world (full-time and temporary); the skills required to obtain those opportunities; the profiles for every company in the world offering those opportunities; the professional profiles for every one of the roughly 3.3 billion people in the global workforce; and subsequently overlay the professional knowledge of those individuals and companies onto the “graph” [so that individual professionals could share their expertise and experience with anyone].
Anyone will be able to access intelligent networks such as LinkedIn’s global graph, see what skills are in demand or available, and even offer up online courses. You might teach knitting or editing or gardening or plumbing or engine repair. So many more people will be incentivized to offer their expertise to others, and the market for it will be vastly expanded.
Added Weiner:
With the existence of an economic graph, we could look at where the jobs are in any given locality, identify the fastest growing jobs in that area, the skills required to obtain those jobs, the skills of the existing aggregate workforce there, and then quantify the size of the gap. Even more importantly, we could then provide a feed of that data to local vocational training facilities, junior colleges, etc., so they could develop a just-in-time curriculum that provides local job seekers the skills they need to obtain the jobs that are and will be, and not just the jobs that once were.
Separately, we could provide current college students the ability to see the career paths of all of their school’s alumni by company, geography, and functional role.
From 2018 book A New U: Faster + Cheaper Alternatives to College, by a VC whose focus is education:
“LinkedIn CEO Jeff Weiner’s vision for an ‘economic graph’ is the clearest expression by any technology company of the competency-marketplace future.”
“[T]echnological developments will complete the faster + cheaper revolution. The resulting ‘competency marketplaces’ . . .”
“The historic disconnect between higher education and employer needs is a data problem. . . .
Technology has begun to change this . . . first via the increasing availability of competency data: e-portfolios . . .”
— End of: 1) excerpt from said pdf, 2) Re: #2.1 —
Re: #2.2
From an earlier write-up of mine:
— Re: OSG’s offerings a/o clones will advance LS [i.e., life-science research] —
Summary (details follow)
Many/most advances of LS will derive at least partly from the “garage biotech” ecosystem (i.e., from (very) small biotech-firms).
In many cases, these firms (LS-GBFs) will be co-founded by specialists who leverage Adver-ties a/o clones to find each other.
In many/most cases, LS-GBFs will:
post-founding, leverage Adver-ties a/o clones to recruit specialists (e.g., employees, contractors)
use a LOT of AI
Many LS-GBFs will seek equity-crowdfunding (i.e., will want to be showcased in/on SCs [i.e., “startup comedies” produced by OSG; for details, see said pdf]).
Many of said specialists will enter their field via CE, not least because OSG will:
race to provide a loan program for CE consumers (i.e., loans that will be variants of today’s “private” student loans)
learn continuously as a means of:
lowering the interest rates of CE loans
providing alternatives to loans (e.g., income-share agreements, livelihood insurance)
improving these alternatives
LS-BigCos will benefit also from OSG’s offerings a/o clones.
Re: garage biotech
From 2011 book Biopunk: Solving Biotech’s Biggest Problems in Kitchens and Garages:
Schloendorn told me his new company had just received a half-million dollar investment . . . To raise money, he needed to show he could create the right conditions for a white blood cell to kill a cancer cell. . . . [Schloendorn says:] “To blow up the first cancer cell—that’s the risk. And so we just went with the minimal equipment needed to blow up a cancer cell. And we could do that at the kitchen table.”
From 2010 book Biology Is Technology: The Promise, Peril and New Business of Engineering Life, published by Harvard University Press:
Biotech . . . technology is changing so rapidly that, within just a few years, the power of today’s elite academic and industrial laboratories will be affordable and available to individuals. . . . It is thus no surprise . . . that garage hacking—that garage innovation—has come to biology.
Re: many/most LS advances (partly) via LS-GBFs
From a 2018 article on CNBC.com:
[P]int-size ventures are driving pharma innovation. The majority of drugs approved in recent years originated at smaller outfits—63 percent of them over the last five years, according to HBM Partners, a health-care investing firm.
Re: LS-GBFs leveraging Adver-ties a/o a clone (part 1 of 2)
From a 2011 article in The Atlantic:
The Rise of Backyard Biotech
Powered by social networking, file sharing, and e-mail, a new cottage industry is bringing niche drugs to market.
. . . FerroKin is seven employees who work from home, and a collection of about 60 vendors and contractors who supply all the disparate pieces of the drug-development process. Rienhoff, a physician and former venture capitalist, founded it in 2007 as a start-up, a virtual biotech company. Since then, his team has picked up talent and resources as needed, raising $27 million and seeing a drug from development into Phase 2 clinical trials.
. . . The small industries and biotech freelancers springing up are, in some ways, like the divisions of the old behemoth drug company, but connected only by the tendrils of the Internet, and the relationships that grow so easily there. Rienhoff is contemporary biotech’s answer to the lost Renaissance man. He pulls the renaissance effect out of the network around him, using . . . the global community to fight the terrible complexity of disease.
Re: LS-GBFs using a LOT of AI
From 2020 book Longevity Industry 1.0 (my emphases):
AI for Longevity is the “smart money” sector of the industry, and can achieve enormous results and accelerated timelines in terms of progress in actual, tangible, real-world Healthy Human Longevity, even with comparatively tiny levels of financing compared to other sectors.
From The Future Is Faster Than You Think:
[T]he speed of drug development is accelerating, not only because biotechnology is progressing at an exponential rate, but because artificial intelligence . . .
Re: LS-GBFs leveraging Adver-ties a/o a clone (part 2 of 2)
From Longevity Industry 1.0:
The fourth pillar, the one with the greatest potential to create real-world effects on human Longevity in short time-frames, and the one with the highest ratios of cost to effectiveness, is the application of AI and Data Science to Longevity.
. . . What the fourth pillar needs for this to become reality, however, is intelligent coordination and harmonization of experts and industry stakeholders (AI specialists, longevity scientists and entrepreneurs, investors [my emphasis] . . .) . . .
Re: GBFs wanting to be showcased in/on SCs
From a 2015 article titled “Biotech in the Garage”:
Sites like Experiment offer nontraditional sources of funding for researchers in the early stages. One example of what the future might hold is a project aimed at discovering a new treatment for Ebola, which recently raised $140,000 in crowdfunding. Before launching the $140,000 campaign, they first launched a $5,000 fundraising campaign, which gave them enough capital to prove some assumptions cheaply by running experiments through Science Exchange. Only after getting positive results did they decide to raise more for the next phase. This lean setup—raising a small amount to test assumptions cheaply then repeating the process with increasing amounts of capital—will likely become more common over the next decade. This mirrors what happens now with software but has only recently become possible in biotech because of the trends mentioned above.
Re: LS BigCos will benefit from OSG’s offerings a/o clones
From a 2019 article in Outsourcing Pharma:
The demand for AI technologies and AI talent is growing in the pharma and healthcare industries and driving the formation of a new interdisciplinary field— data-driven drug discovery/healthcare. Acquiring the best AI startups will dominate the biopharma industry [my emphasis].
From 2011 book The New Players in Life Sciences Innovation, published by the Financial Times Press:
Rapid advances in science and its applications, together with changes in market conditions, are forcing a transformation of business models within the life science industries. . . . Perhaps the most obvious change in business models is the gradual demise of the large fully integrated pharma company (FIPCO) and its gradual replacement with the virtually integrated one (VIPCO) . . . based on complex systems of partnerships, both with academia and scientific institutions and with contract research, manufacturing, and sales organizations (CRO, CMO, CSO).
. . . Lilly is quickly transforming itself from a vertically integrated pharmaceutical company into a fully integrated pharmaceutical network that outsources most functions. Merck has chosen to close many of its R&D labs in Europe and relies on a network of collaborative partnerships that include R&D, drug development, and technology licensing.
Re: #3
Two parts.
— Part #1 —
From said pdf:
Re: CE-for-AI will be to the AI economy what oil has been to the industrial economy
— Re: CE-for-AI —
KWs: customized bundles of data, software and services, purchased to: 1) launch each buyer’s “1.0” AI, 2) augment buyers’ AI (e.g., software purchased to add features to a 1.0 AI, data that a 2.0 AI can learn from).
From 2015 book Superforecasting: The Art and Science of Prediction, co-authored by University of Pennsylvania professor of psychology and political science Philip Tetlock:
Doug knows that when people read for pleasure they naturally gravitate to the like-minded. So he created a database containing hundreds of information sources—from The New York Times to obscure blogs—that are tagged by their ideological orientation, subject matter, and geographical origin, then wrote a program that selects what he should read next using criteria that emphasize diversity. Thanks to Doug’s simple invention, he is sure to constantly encounter different perspectives.
From 2018 book Superminds: The Surprising Power of People and Computers Thinking Together, by the MIT professor who’s the Director of MIT’s Center for Collective Intelligence:
What if each participant [in a market] has his or her own “stable” of [AI-powered software ro]bots? Then participants will compete to create smarter and smarter bots [my emphasis]. If your bots are better than mine at making accurate predictions, then you will make more money than I will.
. . . Today’s financial markets are leading the way, with investment managers increasingly relying on quantitative, often AI-based, trading algorithms.
— Re: CE-for-AI will be the oil of the AI economy —
From Nobel laureate economist Paul Romer’s entry on Economic Growth in the 2008 edition of The Concise Encyclopedia of Economics:
[T]he country that takes the lead in the twenty-first century will be the one that implements an innovation that more effectively supports the production of new ideas in the private sector [e.g., AI-produced ideas].
Re: Romer (“Re: AI-produced ideas” follows)
From 2006 book Knowledge and the Wealth of Nations:
This book tells the story of a single technical paper in economics [Romer (1990): Endogenous Technical Change] . . .
. . . Romer won a race of sorts, a race within the community of university-based research economists to make sense of the process of globalization at the end of the twentieth century and to say something practical and new about how to encourage economic development . . .
From 2004 book The Mystery of Economic Growth, by a Harvard economist (my emphases):
Interest in growth theory abruptly revived . . . in the 1980s. The two key papers were by Romer (1986) and Lucas (1988). . . . Romer also initiated the second wave of research on the “new” growth theory.
. . . A more detailed study of the U.S. economy is provided by [Stanford economist Charles] Jones (2002). He found that between 1950 and 1993 improvements in educational attainments, which amounted to an increase of four years of schooling on average, explain about 30 percent of growth of output per hour. The remaining 70 percent is attributable to the rise in the stock of ideas that was produced in the United States, France, West Germany, the United Kingdom, and Japan.
. . .
Re: “seed-investors . . . gain part of a Rockefeller-ian fortune”
— Re: Rockefeller-ian —
John D. Rockefeller’s Standard Oil made him the richest person since the Industrial Revolution.
From said entry of Romer’s on Economic Growth:
Perhaps the most important ideas of all are meta-ideas—ideas about how to support the production and transmission of other ideas. . . . North Americans invented the modern research university . . .
From 2014 book SuperIntelligence: Paths, Dangers, Strategies, published by Oxford University Press:
From 2020 book The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives (my emphases):
In the 1990s, Ray Kurzweil, the director of engineering at Google . . . discovered that once a technology becomes digital—that is, once it can be programmed in the ones and zeros of computer code—it hops on the back of Moore’s Law and begins accelerating exponentially.
. . . The technologies now accelerating at this rate include some of the most potent innovations we have yet dreamed up: quantum computers, artificial intelligence . . .
. . . [F]ormerly independent waves of exponentially accelerating technology are beginning to converge . . . For example, the speed of drug development is accelerating, not only because biotechnology is progressing at an exponential rate, but because artificial intelligence . . .
From Disrupting Class: How Disruptive Innovation Will Change the Way the World Learns:
Students need customized pathways and paces to learn.
. . . The second [phase of the disruption of standardized education] will be the emergence of a user network, whose analogues in other industries would be eBay . . .
From 2015 book The End of College: Creating the Future of Learning and the University of Everywhere:
“I asked Michael [Staton, a partner in a VC firm focused on education and technology] to introduce me to some of the startups that he found most exciting . . .
[Clayton] Christensen was cited ad nauseum by everyone we met.”
“The University of Everywhere will solve the basic problem that has bedeviled universities since they were first invented over a millennium ago: how to provide a personalized, individual education to large numbers of people at a reasonable price.”
2007 book StrengthsFinder 2.0:
primes readers to seek out CE for themselves, their children, et al.
is #2 on Amazon.com’s list of best-selling books of 2016, #5 on the 2015 list, #1 on 2014 and 2013, #5 on 2012, #4 on 2011, #7 on 2010, #6 on 2009, #9 on 2008 and 2007, #25 on 2017, #78 on 2018 and #70 on 2019
— Part #2 —
A key to popularizing OSG’s markets will be facilitating the build-out of complements.
OSG’s facilitating will center on advancing “hyper-specialization,” for reasons explained by complexity science (e.g., OSG’s “1.0” facilitating will center on speeding the complexification of the business ecosystem that centers on Adver-ties).
— Re: hyper-specialization —
From a 2011 article in Harvard Business Review:
Much of the prosperity our world now enjoys comes from the productivity gains of dividing work into ever smaller tasks performed by ever more specialized workers. Today, thanks to the rise of knowledge work and communications technology, this subdivision of labor has advanced to a point where the next difference in degree will constitute a difference in kind. We are entering an era of hyper-specialization . . .
. . . [W]e will now see knowledge-worker jobs—salesperson, secretary, engineer—atomize into complex networks of people [my emphasis] all over the world performing highly specialized tasks . . .
— Re: OSG’s markets can be expected to advance hyper-specialization —
Activity in a market generates new kinds of knowledge. This knowledge typically increases specialization.
From 2017 book Machine, Platform, Crowd: Harnessing Our Digital Future, co-authored by MIT economist Erik Brynjolfsson:
The Magic of Markets, the Purest Crowds of All
Large collections of information like libraries and the web are obviously valuable because we can consult and learn from them. Many crowd-created collections have another benefit: as they accumulate the contributions of many people, they spontaneously generate new kinds of knowledge. This is a kind of magic that actually happens, all the time.
From 2014 book Complexity: A Very Short Introduction:
Niche formation through co-evolution
. . . When we look at realistic niches, whether they be market niches . . . we see a complicated recirculation of resources and signals [e.g., price signals] . . .
How did this complex network of interactions evolve?
The short answer is co-evolution through recombination of building blocks . . . Cascades of increasingly specialized agents result [my emphasis]. As is nicely described by Samuelson in his classic text Economics, there is a multiplier effect in cascades . . . The multiplier effect in a typical cascade may be 4 (or more), indicating that the initial payment has the effect of four separate injections of cash . . .
The multiplier effect that accompanies the re-use of resources in a cascade typically drives the occupants of a niche to increasing specialization.
From Machine, Platform, Crowd (my emphases):
The first person to clearly point out this benefit [i.e., new knowledge via activity in markets], and thus to become a kind of patron saint of the crowd, was the Austrian economist Friedrich Hayek in his 1945 article “The Uses of Knowledge in Society.”
. . . Hayek’s paper, which anticipated many of the ideas of what would coalesce into complexity theory later in the twentieth century . . .
As seen above, many/most of the niches around OSG’s markets will provide investors with lucrative/LUCRATIVE opportunities.
Re: presentation-errors above
From 2012 book APE: Author, Publisher, Entrepreneur—How to Publish a Book, co-authored by Guy Kawasaki, a former chief evangelist at Apple:
Every time I turn in the “final” copy of a book [Kawasaki has (co-)authored twelve books], I believe that it’s perfect. In APE’s case, upward of seventy-five people reviewed the manuscript, and [co-author] Shawn [Welch] and I read it until we were sick of it. Take a wild guess at how many errors our copy editor found. The answer is 1,500. [APE is 410 pages.]
And, of course, I’m preoccupied with preventing/subduing said threat to many/most people . . .