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jerryking : self-deception   5

10,000 Hours with Reid Hoffman: What I Learned | Ben Casnocha
16 Lessons Learned (Among Many!)
1. People are complicated and flawed. Root for their better angels.
2. The best way to get a busy person’s attention: Help them.
3. Keep it simple and move fast w...
advice  Ben_Casnocha  career  culture  entrepreneurship  lessons_learned  networking  productivity  psychology  Reid_Hoffman  self-deception  self-delusions  speak_truth_to_power  success  thought_experiments  via:enochko 
august 2018 by jerryking
Empty talk on innovation is killing Canada’s economic prosperity
Mar. 19, 2017 | Globe & Mail | by JIM BALSILLIE.

Immigration, traditional infrastructure such as roads and bridges, tax policy, stable banking regulation and traditional trade agreements are all 19th- and 20th-century economic levers that advance Canada’s traditional industries, but they have little impact on 21st-century productivity.

The outdated economic orthodoxy behind our discourse on innovation is causing the steady erosion of our national prosperity.

Over the past 30 years, commercialization of intellectual property (IP) became the primary driver of new wealth. The structure of the 21st-century company shifted and IP became the most valuable corporate asset. IP is an intangible good that requires policy infrastructure that’s completely different than the infrastructure required to get traditional tangible goods to market. IP relies on a tightly designed ecosystem of highly technical interlocking policies focused on scaling companies, which are “agents” of innovation outputs.....Canada doesn’t have valuable IP to sell to the world so we continue exporting low-margin resource and agricultural goods while importing high-margin IP. If our leaders want to create sustainable economic growth, Canada’s growth strategy must focus on creating high-margin IP-based exports that the world wants and must pay for.........IP ownership is the competitive driver in the new global economy, not exchange rates that adjust production costs. That’s why despite the strong U.S. dollar, U.S. company valuations and exports are soaring – IP-intensive industries added $6.6-trillion (U.S.) to the U.S. economy in 2014. So what is Canada’s strategy to increase our ownership of valuable IP assets and commercialize them globally? Supply chains in the innovation economy are different than in traditional economies because IP operates on a winner-take-all economic principle with zero marginal production costs. IP is traded differently than tangible goods because IP moves across borders on the principle of restriction, not free trade. Trade liberalization increases competition and reduces prices, but increased IP protection does the exact opposite. The economy for intangible goods is fundamentally different than the one for tangible goods. Productivity in the global innovation economy is driven by new ideas that generate new revenue for new markets. What Canada needs is a strategy to turn its new ideas into new revenue.....The Growth Council missed our overriding priority for growth: a national strategy to generate IP that Canadian companies can commercialize to scale globally.

We urgently need sophisticated strategies to drive the commercialization of Canadian ideas through our most innovative companies.
innovation  Jim_Balsillie  happy_talk  intellectual_property  scaling  tax_codes  winner-take-all  productivity  intangibles  digital_economy  ideas  self-deception  patents  commercialization  national_strategies  global_economy  property_rights  protocols  borderless 
march 2017 by jerryking
How to pick startup ideas
Slava Akhmechet: cofounder of RethinkDB — an open-source distributed database designed to help developers and operations teams work with unstructured data to build real-time applications.

How to pick startup ideas

25 Feb 2015
ideas  howto  self-deception  storytelling  unstructured_data  competitive_advantage  competition  entrepreneur  start_ups 
november 2015 by jerryking
Diversity and Dishonesty - NYTimes.com
APRIL 12, 2014

Continue reading the main story
[Ross Douthat]

both cases illustrate, with their fuzzy rhetoric masking ideological pressure, is a serious moral defect at the heart of elite culture in America.

The defect, crucially, is not this culture’s bias against social conservatives, or its discomfort with stinging attacks on non-Western religions. Rather, it’s the refusal to admit — to others, and to itself — that these biases fundamentally trump the commitment to “free expression” or “diversity” affirmed in mission statements and news releases.

This refusal, this self-deception, means that we have far too many powerful communities (corporate, academic, journalistic) that are simultaneously dogmatic and dishonest about it — that promise diversity but only as the left defines it, that fill their ranks with ideologues and then claim to stand athwart bias and misinformation, that speak the language of pluralism while presiding over communities that resemble the beau ideal of Sandra Y. L. Korn.

Ross Douthat
diversity  freedom_expression  free_speech  Colleges_&_Universities  self-deception  censorship  Ivy_League  Mozilla  Brandeis  controversies  academic_freedom  Ayaan_Hirsi_Ali  discomforts 
april 2014 by jerryking
What Data Can’t Do - NYTimes.com
By DAVID BROOKS
Published: February 18, 2013

there are many things big data does poorly. Let’s note a few in rapid-fire fashion:

* Data struggles with the social. Your brain is pretty bad at math (quick, what’s the square root of 437), but it’s excellent at social cognition. People are really good at mirroring each other’s emotional states, at detecting uncooperative behavior and at assigning value to things through emotion.
* Data struggles with context. Human decisions are embedded in contexts. The human brain has evolved to account for this reality...Data analysis is pretty bad at narrative and emergent thinking.
* Data creates bigger haystacks. This is a point Nassim Taleb, the author of “Antifragile,” has made. As we acquire more data, we have the ability to find many, many more statistically significant correlations. Most of these correlations are spurious and deceive us when we’re trying to understand a situation.
* Big data has trouble with big (e.g. societal) problems.
* Data favors memes over masterpieces. Data analysis can detect when large numbers of people take an instant liking to some cultural product. But many important (and profitable) products are hated initially because they are unfamiliar. [The unfamiliar has to accomplish behavioural change / bridge cultural divides]
* Data obscures hidden/implicit value judgements. I recently saw an academic book with the excellent title, “ ‘Raw Data’ Is an Oxymoron.” One of the points was that data is never raw; it’s always structured according to somebody’s predispositions and values. The end result looks disinterested, but, in reality, there are value choices all the way through, from construction to interpretation.

This is not to argue that big data isn’t a great tool. It’s just that, like any tool, it’s good at some things and not at others. As the Yale professor Edward Tufte has said, “The world is much more interesting than any one discipline.”
massive_data_sets  David_Brooks  data_driven  decision_making  data  Nassim_Taleb  contrarians  skepticism  new_graduates  contextual  risks  social_cognition  self-deception  correlations  value_judgements  haystacks  narratives  memes  unfamiliarity  naivete  hidden  Edward_Tufte  emotions  antifragility  behavioral_change  new_products  cultural_products  masterpieces  EQ  emotional_intelligence 
february 2013 by jerryking

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