by Tim O’Reilly
i’ve spent my career as a tech evangelist, book publisher, conference producer and investor wrestling w question like these. my co, o reilly media, works to id important innovations, and by spreading knowledge about them to amplify their impact and speed their adoption..
it is only when a business becomes profitably self sustaining, rather than subsidized by investors, that we can be sure that it is here to stay
? rather.. self sustaining.. no profit.. no money..
but even when these co’s succeed, they may not be the surest guide to the future. at o reilly media, we learned to tune in to very different signals by watching the innovators who first brought us the internet and the open source software that made it possible. they did what they did out of love and curiosity, not a desire to make a fortune.. we saw that radically new industries don’t start when creative entrepreneurs meet vc’s. they start w people who are infatuated w seemingly impossible futures..
those who change the world are people who are chasing a very diff kind of unicorn.. far more important than the sv bn dollar valuation..
the world today is full of things that once made us say ‘wtf?’ but are already well on their way to being the stuff of daily life..
we are layering on new kinds of magic that are slowly fading into the ordinary..
it is this kind of unicorn that i’ve spent my career in tech pursuing. so what makes a real unicorn of this amazing kind? 1\seems unbelievable at first 2\ changes way the world works 3\ results in an ecosystem of new services, jobs, business models and industries..t
let’s do 1 and 2 and disengage from 3.. ie: sans jobs/bm’s/industries..
for everyone’s sake, we must choose a different path. loss of jobs and economic disruption are not inevitable
not different enough..
history tells us tech kills professions, but does not kill jobs
not diff enough
we must keep asking: *what will new tech let us do that was previously impossible? **will it help us build the kinds of society we want to live in?..t
*listen to and facil 7 bn curiosities everyday.. ie: 2 convos .. as the day..
**yes.. ongoingly.. all of us
i hope to persuade you that understanding the future requires discarding the way you think about the present, *giving up ideas that seem natural and even inevitable..t
making what we do now more productive is just the beginning.. if we let machines put us out of work, it will be because of a failure of imagination and a lack of will to make a better future..t
if we make what we do now more productive.. it will be because of a failure of imagination/will
part 1 – using the right maps
1 – seeing future in present
i think of myself as a mapmaker. i draw a map of present that makes it easier to see possibilities of the future. . any system that helps us see where we are and where we are trying to go.. edwin schlossberg: skill of writing is to create a context in which other people can think’ this book is a map
we use maps simplified abstraction of an underlying reality, which they represent not just in trying to get from one place to another but in every aspect of our lives…
creating the right map is the first challenge we face in making sense of today’s wtf techs..t
perhaps the right map is no map..
what if the deeper enough problem ..we should all work together on first.. isn’t about trying to go anywhere.. rather.. trying to be/become.. what if that takes no map.. no training.. no writing.. just trust..
before we can understand …. we have to make sure we aren’t blinded by old ideas.. we have to see patterns that cross old boundaries..
old ideas: maps; patterns; ..
twain: history doesn’t repeat itself, but it often rhymes’ study history and notice its patterns. this is the first lesson i learned in how to think about the future
perhaps times beg we disengage from rhyming.. ie: good bye cycle.. what part of history are we wanting to rhyme with..? no one’s really going back far enough.. deep enough.. to disengage from the things we keep clinging to.. the things that keep keeping us in bondage..
the lesson is clear: treat curiosity and wonder as a guide to the future..t that sense of wonder may just mean that those crazy enthusiasts are seeing something that you don’t ..yet..
what were the conditions under which giving software away was a better strategy than keeping it proprietary? this question has recurred, ever more broadly, throughout my career: how can a business create more value for society than it captures for itself..
by not focusing on open sources software.. but .. are we open sourcing the right data..? are focusing on data that matters.. to detox ourselves.. ie: self-talk as data
our experience is shaped by the words we use..
having a language for grass helps me to see more deeply
you can’t just read about it.. you have to practice it
2 – toward a global brain
it was becoming clear that the future demanded even more extreme rethinking of what the internet could become as a platform for next gen software applications and content
applications – listening to and facil ing curiosity..
content – self talk as data
jon udell.. noted that when a website called a backend database to retrieve info.. it encoded the info that ie wanted into the url.. and that this url could be constructed by a program, essentially turning any website into a program callable component
28 – dale dougherty.. one of my earliest employees.. intro’d me to tim berners lee in summer 1992
3 – learning from lyft and uber
as i came to realize, our business was really ‘changing the world by spreading the knowledge of innovators..
google is still one of the key co’s to understand. its search engine is the pervasive neocortex of the info econ, a critical component of the global brain that the internet has become, connection billions of human with the data and documents we collectively create
not helpful if wrong data for our time.. so .. is it really the cortex..? perhaps it’s more the appendix..? i don’t know.. reading gut now too..
i do know that it matters little .. how much we collect and connect.. if it’s the wrong data.. because we aren’t ourselves.. we’re just spinning wheels.. dangerous wheels.. perhaps we disengage.. (from the appendix?).. (i don’t know enough)
maybe less about searching and more about listening
the principles that led me to make google the poster child for web 2.0 are still unfolding as drivers of the future: big data, algos, collective intelligence, software as a service, w the addition of a new focus on machine learning and ai. understanding how algorithmic systems shape not just new services but also society is a central them of this book
again.. begs self-talk as data
the android pone os system pugs google’s services into pockets of billions of people. the co kicked off the race for self driving cars and has been a leader in their development. and it has big ambitious in areas like healthcare, logistics, the design of cities, and robotics. and last but not least, its advertising based business model means that almost every service it creates can be given away for free, w implications we are only beginning to grasp
yeah.. ie: shiny as os
google defining co for info age.. fb defining co of social era.. jeff bezos arguably the greatest entrepreneur of the internet era.. reinventing industry after industry..
most important thing amazon did was to turn its e commerce app into a cloud computing platform on which the bulk of sv startups operate..
over course of next few chapters we will see how lessons from each of these co’s and many others, overlap and come into focus as a map of the future
map of market future.. wtf.. as expletive
internet no longer just something that provides access to media content, but instead unlocks real world services..
what if services (like you describe.. like we have..driving/buying/selling..) aren’t what our souls are craving.. and in fact.. are just perpetuating the toxicity of our intoxication..
uber origin story : whether it’s a ride, a sandwich, or a package, we use tech to give people what they want, when they want it
great idea.. but i don’t think people really want those things.. ie: krishnamurti free will law .. we need a mech to help us figure out what it is we really want/need.. ie: the thing we can’t not do..
in long run.. uber and lyft are not competing w taxicab co’s but w car ownership … replacing ownership w access..
again.. great.. but it matters what we decide everyday.. what we want access to.. otherwise.. we’re like the rats not in ratpark..
uber and lyft are asking their consumer to become the kind of people who expect a car to be available as easily as they had previously come to expect access to online content. they are asking them to redraw their map of how the world works
doesn’t sound like.. sounds like asking them to automate how world already works/is.. which unfortunately.. is sick.. stressed/ineq et al
a platform not just a co.. uber an lyft created a digital platforms to manage and deploy hundreds of thousands of independent drivers, trusting the marketplace itself to ensure that enough of them show up to work and bring their own equip w them.. imagine walmart or macdonald’s didn’t schedule their workers.. but simply offered work, trusted enough people to show up.. and offered higher wages when there weren’t enough workers to meet demand.. this is a radically diff kind of corp org
yes.. that idea.. but diff kind of corp org isn’t what we need .. we need to go more radical.. sans corp/money.. ness.. that crazy/radical dance will only work if we’re all truly free/ourselves.. so.. begs detox..
understanding how algo systems shape our society is a central them of this book.. to have chance at making a better future.. must grasp not only how nature of these algos is changing but also why the algos we have most to fear may not be those of ai but the unexamined algos that rule our econ.. see part 3 of book
ie: market/money ness.. obligation.. exchange.. measuring transactions..
to make the future econ better than the present, find new ways to augment workers, ..t..giving them new skills and access to new opps. as we automate something that humans used to do how can we augment them so that they can do something newly valuable..
to make the future
econ better than the present, find new ways to augment workers free people..
otherwise.. we’re ‘not someone who places high value on humans’.. either.. if we’re still holding the reigns..
steve jobs: everything around you was made up by people no smarter than you.. you can change it
supposed to’s are killing us
4 – there isn’t just one future
real breakthroughs come when an entrepreneur doesn’t just use new tech to duplicate what went before or to fine tune the way the world works now, but to reimagine how it ought to work
why do you keep saying this.. w no ie’s to model it..?
let’s reimagine sans made up moneys/measures
we can facil rev of everyday life for 7bn plus
part 2 – platform thinking
5 – networks and the nature of the firm
Esko Kilpi’s the future of the firm – apps can do what managers used to do
or.. we can just not do what managers used to do..
this is a central pattern of the internet age: more freedom leads to more growth
perhaps 100% freedom means we no longer have to be addicted/blindsighted to/from growth
in 73.. bob kahn and vint cerf realized that the right way to solve the interoperability problem was to take the intelligence out of the network and to make the network endpoints responsible for reassembling the packets and requesting retransmission if any packets had been lost.. seemingly paradoxically, they had figured out that the best way to make the network more reliable was to have it do less…t
the coordination is all in the design of the system itself..
indeed.. let’s design deep/simple/open enough for all of us
6 – thinking in promises
7 – govt as platform
open data had become a key talking point of the new admin.. but most people only thought of it as a tool of govt transparency and accountability. a handful saw that there was a real opp to make data much more useful to citizen and society..t
how about by changing what data we (as govt/common ness) focus on .. ie: self-talk as data.. that would make it real useful.. to all 7 bn of us.. everyday..
as we’ve discussed, creating a thick marketplace is the first requirement of any platform
what is the equivalent for govt? for ‘thick marketplace’ read ‘flourishing econ’… we like to think that ‘the market’ is a natural phenom, but the fact that there are poor countries w abundant natural resources and large populations and rich countries w neither abundanct resources nor large populations teaches us that there is an art to creating a flourishing econ..
getting out of the way? letting go..?
getting a robust marketplace off the ground and keeping it often requires a stron ggovt intervention
rather.. holmgren indigenous law..
the rule of law in platforms and in govt is not just about justice and peace, it enables commerce; peopel dont’ do business where they can’t trely on the rules being enforced..
Jen – worked w child welfare before – say failures of tech projects to the chldren in care of state.. whose software and systems worked so poorly.. making harder to take care of children… wanted to do something about descrepancy between (web 2.0 and govt 2.0).. so .. code for american.. aimed to bring govt’s tech competence up to apr w that of the conusmer tech world..
3 realizations: 1\ simply putting a digital front end on a broken b sysem often only makes the problem worse.. replicates exisign processes w/o rethinking them from ground up
ie: money; corps; firms; war; B; b; ..
just stopping there.. that’s enough.. to change everything..
gds principles: 1\ start w needs – user nreeds not govt needs..
2\ do less
ie: 2 convos.. 33 min a day.. that much less..
do the hard work to make it simple
choose we hear that word again. the future depends on what we choose
we have an opp to reinvent govt. we must not let the opp slip by..t
more dangerous than trump.. is us not being imaginative enough.. to truly reinvent..
part 3 – a world ruled by algorithms
8 – managing a workforce of djinns
big data doesn’t just mean a larger scale version os of a relational data base like oracle.. it is something profoundly different.
instead of building ever more complex language models, researchers began to ‘make use of the best ally we have: the unreasonable effectiveness of data.’ complex rule based models were not the path to language understanding; they should just use that statistical analysis and let the data itself tell them what the model should be..t
idio jargon.. as the day/way
google’s core search service: ‘simple models and a lot of data trump more elaborate models based on less data’
how about 2 convos (simple) from 7 bn everyday (lots) …
djinns.. spirits from arabian mythology who can be coerced into fulfilling wishes, but who so often artfully reinterpret the wish to their master’s max disadvantage..
one way to evaluate a change is short term user response .. what are users clicking on.. another is long term user response: do they come back for more.. another is talking to actual users one on one and asking them what they think
fb also measure clicks, just like google, but the clicks they value most are not the one s that send people away, but the ones that keep them on the site, and searching for more like what they just saw
using machine learning .. developer starts out w hypothesis.. just like before.. but instead of producing a handcrafted algo to process the data, she collects a set of training data reflecting that hypoth, then feeds the data into a program that outputs a model – a math rep of features to be looked for in the data. this cycle is repeated again and again, w the program making minute adjustments to the model, gradually modifying the hypoth using a technique such as gradient descent until it more perfectly matches the data.. in short, the refined model is learned from the data..
matters little if wrong data.. ie: from people who aren’t themselves..
deep learning via yann lecun: pattern recognition system.. show man ie’s.. keep adjusting knobs a bit each time.. eventually machine gets right answer every time.. deep learning uses layers of recognizers
sounds more like a pavlovian mastery.. not deep learning..
so .. not only.. not deep learning.. but could never work w humans or anything else that is alive.. and always changing..
computers can do same thing or slight variations of same thing over an dover again very fast
that doesn’t sound like what we want in life..
this is called supervised learning.. because while google photos hasn’t seen your photos before, it has seen a lot of other photos.. in particular it’s seen what’s called a training set..
exactly.. not deep learning.. supervised (not even learning) memorizing/documenting
the holy grail in ai is unsupervised learning, in which an ai learns on its own, w/o being carefully trained.. popular excitement was inflamed by deepmind’s creator’s claim that their algos ‘are capable of learning for themselves directly from raw experience or data’
data not raw .. nor experienced.. again.. assuming people aren’t themselves.. and.. assuming we’re talking about a world for humanity.. rather than for ie: info/data/math
yann lecun: we need to solve the unsupervised learning problem before we can even think of getting to true ai..
hint: it’s not about unsupervised machines.. it’s about unsupervised/free people..
how to make sure data sets w which we train them are not inherently biases.. cathy o neil: if to train machine learning model for predictive policing on a data set of arrest records..
that’s inherently biased thinking that that is what matters to .. free .. people
perhaps it matters to incarcerated/oppressed people.. but that’s not the deeper problem that will truly free them (all of us)
the characteristics of the training data are much more important to the result than the algo
the characteristics..? or what data we’re collecting ..? ie: times beg we focus on self-talk as data.. use machine/tech/mech just to listen w/o judgement in order to match us..
peter norvig: the creativity comes in picking a model architecture and tuning hyperparameters, not so much in feature selection
architecture of the day: 2 convos
architecture of the tech/mech: simple enough mech
perhaps the most important qeustion for machine learning as for every new tech.. which problems we should choose to tackle in the first place
exactly.. (for life.. for machine use.. not for machine learning).. has to be deep enough for 7 bn people to resonate with today.. gives plenty of data.. and input is self sustaining..
next three ie’s in health care.. ie: radiology imaging & dagnoses & immune system to battle cancer
super.. but if we free 7 bn people first.. those health issues may become irrelevant.. have to go deeper.. times beg we go deeper.. incredible opportunity for a leap just now..
if specified broadly and clearly, those laws can stand the test of time..
yeah.. let’s be deep/broad enough.. to just have two.. for all of us.. let’s just focus on that.. everything else.. ongoingly changing..
on google regulations.. and bad actors.. taking advantage of a vacuum in the absence of proactive management
let’s try this as proactive (not management): gershenfeld sel
we need to find more ways to make the consequences of bad action systemic
we need to find more ways to make bad action irrelevant
there are some important lessons from tech platforms.. the fitness function of those algos is usually simple: does the user find this info relevant, as evidenced by their propensity to click on it and then go away.. a matter of creating right incentives
i think we have that all wrong..
it is said that ‘that govt is best which governs least’ unfortunately, evidence shows that this isn’t true.. w/o rule of law, capircious power sets the rules, usually to the benefit of a powerful few..
evidence doesn’t show that.. we’ve never practiced/modeled free people w least govt..
what people really mean by ‘governe least’ is that the rules are aligned w their ienterest..
govt is best that governs for most
rather.. for all
reputation systems are one way that regulation is built into the design of online platforms
anything where we’re measuring transactions/each-other.. is not gonna work.. same song
evan williams: the current system causes increasing amounts of misinformation.. and pressure to put out more content more cheaply – depth, originality or quality be damned. it’s unsustainable and unsatisfying for producers and consumers alike.. we need a new model
(after concluding that the broken system is ad driven media itself).. ev admits he doesn’t know what the new model looks like.. but he’s convinced that it’s essential to search for it..t
a nother way.. wish you could be quiet enough to hear
2011 – occupy.. 99 and 1
99 and 1 ness
every day we teach the global brain new skills..
well.. not really.. more like saying you’re teaching how to ie: ride a bike.. when really teaching how to ie: draw a butterfly
most of what you are writing .. is irrelevant if we believe most people are not themselves.. let’s work on that first..
mistaking what is good for financial market s for what is goof for jobs, wages, and the lives of actual people is a fatal flaw in so many of the eoconomic choices business leaders, policy makers and politicians make
mistaking the problem we should be focusing on for anything other than what it means to be human/alive/being ie: maté basic needs .. is a fatal flaw for all of us.. all of earth..
part 4 – it’s up to us
12 – rewriting the rules
most people unthinkingly use the term the market to refer to these two very different markets. (real goods/services where supply /demand set price and financial markets where hope/greed set price) recognizing that they are not the same is the first step toward solving the problem..
disengaging from market thinking (that there is any price/measurement/judgment) is the first step toward solving our problem..
future economic historians may look back wryly at this period when we worshipped the divine right of capital while looking down on our ancestors who believe in the divine right of kings..
perhaps future econ historians won’t ever exist.. if we play our cards right.. in this moment of opp that we have..
because they are shaped by rules crafted by our imperfect understanding, entire economies can go awry..
because shaped by economic thinking.. humanity can (is-going/has-gone) awry
i found myself thinking/saying thing that hadn’t occurred to me before. rob reich said to me afterward.. ‘i thought going into this that martin was the radical. but i realized that you’re the real radical.. you’re saying the ubi is just a software patch on the existing system.. we need a complete reboot..t
when imagining the future, it’s best if you stretch out your view of the possible by postulating extreme futures.. so.. then goes into negative taxing
ugh.. not really radical.. not really extreme future.. same patch man
asking the right questions
i’m not an economist/politician/financier.. equipped w quick answers as to why things can or can’t change. i’m a technologist and entrepreneur who is used to noticing discrepancies between the way things are and the way they could be, and asking questions whose answer might point the way to better futures..
why do we have lower taxes… ? why do we tax labor.. ? when economists… ? why do we treat purely financial investments..? why do ..?
all about money.. ? i think you’re missing it .. big time..
it’s time to rewrite the rules. we need to play the game of business as if people matter..
13 – supermoney
if we are working from a new map, in which our objective is to value human effort, not to dispense with it, we surely must start by assigning an economic value to caregiving..
human touch.. becoming the source of competitive advantage..
fight for something we haven’t seen yet..t – Jen
yeah.. let’s try that.. no money/measure.. imagine that..
we don’t just need ‘more’ ed or free ed.. we need a radically diff kind of ed..t
if equity is everyone getting a go every day.. redefining public education becomes revolution of everyday life.. aka: global equity
let’s facil that
we need new mechs to support ed and retraining throughout life..
once you have curiosity, the internet has provided powerful new ways to feed it
before she could learn how to make dessert by watching youtube.. she had to know how to use an ipad.. how to search youtube.. she had to know what a world of conteet was there for the taking. at oreilly we cal this structural literacy
ugh.. no train needed..
Thanks, @doctorow, for the forgotten memory of that P2P working group meeting, and for the best review of my book that I’ve read yet. It’s wonderful to read a review that is not a rehash of the content, but a vaulting forward of the argument! https://t.co/XOK78PyMhD
Original Tweet: https://twitter.com/timoreilly/status/938066073693196288
A book that tells us how to keep the technology baby and throw out the Big Tech bathwater
can love the sin hate the sinner
WTF? is a book about technology as it was, as it is, and *as it could be. It is told from the perspective of someone who has been personally present at the most important moments in the fast-paced history of tech, and who played a significant role in those moments. It’s a rare and important piece of criticism that inspires even as it dissects.
*this is what i found most frustrating about the book.. i think we’re missing what it could be.. big time.. and perhaps.. precisely because we’ve still got that hierarchical format for listening.. ie: you can’t hear me.. we need tech to listen to all voices (of alive people.. the mech has to also wake/detox us up) w/o judgment.. and facil that
we’re missing tech as it could be..