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The Future of Platforms & Markets

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Like every year, end of May is the moment when the fabulous and awaited Internet Trends report from Mary Meeker of KPCB gets published. I’ve tried to give a look at this amazing piece of work with a platform perspective: what do the internet trends we’re seeing mean for the future of platform thinking? That was my original question.

This year issue of Meeker’s peek into the state of the internet is characterized by a strongly quantitative analysis: less trends are spotted — most of them are actually recurring ones, such as the key importance of experiences for brand success or the penetration of voice and AI — and more numbers are provided to testify an evident truth.

We’re (almost) all connected.

Indeed, new smartphones sales don’t grow much anymore YoY, while global internet users are growing steadily at 10% rate: this basically means we’re buying less phones in the west (mostly due to Moore’s law slowing down and making new phone buying less frequent) while a bit of growth is still happening in connecting countries such as India or the African continent.

Giant tech companies are set to dominate the Internet of the present-future

In an internet where everyone is connected with anyone else and where we’re getting to the upper limit of attention time available (with screen time approaching 6 hours a day in the US), efficiency in connecting people with products and services is key.

Thanks to their data-centric nature and to the huge network effects that allow them to train machines and algorithms with an insane amount of data, Google and Facebook now dominate — and are on track to monopolize — global advertising, ensuring all of us can easily get connected with the right (?) product and services.

Amazon is continuously growing its footprint to the extent of, eventually, putting out of business, this year, the largest number of retailers in history. We’re finally noticing that retail as we know it (disconnected from the overall — digitally powered — buying experience) is cursed.

Giant tech companies are therefore dominating the business landscape, and the most interesting aspect perhaps is that they are continuously growing their feature base. Here’s the point: huge tech giants have something that other companies don’t have, they’ve network effects (and enormous user bases), and the agility to test and prototype new ideas rapidly.

While a traditional giant company may have the first, it’s likely failing on the latter; while a nimble tech innovation company may have the latter will always have to bounce back to GAFA to be able to leverage on their network effects to distribute and test new ideas to wider markets.

Facebook now encompasses all the aspects of our socially-connected life, Amazon now sells directly under its brands — or child brands — things such as baby wipes, batteries, or bed linen.

Tech innovations such as AI, machine learning and conversational interfaces (all on the rise) will provide GAFA with even more potential to create seemingly personal relationships with customers, increasing their potential to deal with long tails with highly customized services and self-customization tools to let customers make the tweaks that they couldn’t anticipate.

God only knows what will happen when the penetration of IoT and connected devices will really cross the chasm: everything wants to be connected, and when it’s connected, it will be owned by the GAFA.

Soon machines will be able to fake human relationships

We now have an internet made of enormous platform-infrastructures, connecting entities in their huge ecosystems, providing them with the possibility to find each other precisely, and to trade value to an extent we never experienced before.

What’s left?

A few days ago, my good friend and italian digital icon Fabio Lalli, blurted on Facebook that with giants like Facebook now encompassing everything social and spurring new features continuously (at least test-validating them), it’s really hard for entrepreneurs today to think of something new and valuable, and be able to overcome the bullying of the GAFA bringing it to the market.

Similar reflections could probably be made for e-commerce entrants confronting with and Amazon, or business automation innovators facing the market domination of Salesforce, or similar giants.

So what’s left for us to invent in this internet?

The effect of the penetration of the GAFA, in parallel with the everlasting effort of existing incumbents to componentize and digitalize their business through APIs  is leaving modern entrepreneurs with an interesting set of tools to leverage on:

  • the possibility to connect part of existing industrial business processes, through APIs, in more complex value creation models
  • the possibility to easily reach customers thanks to the efficiency of advertising and GAFA distribution
  • the possibility to leverage on abundant open code base and Everything as a Service

Despite “vertical” transaction-based marketplaces such as Airbnb and the likes have demonstrated that a clearly designed strategy and mission can achieve global growth and impact, I’m skeptical there’s still a lot of room for entrepreneurs to come up with a simple idea that can disrupt these transactional markets.

This may be hard, first because most of those simple markets are now already crowded with exceptional brands, and, furthermore, because it will be easy for GAFA and the likes — including these huge global transactional marketplaces like Airbnb — to jump into an adjacent market by enabling “just another” transaction type among their networks (think of Facebook Marketplace feature or the recent move of Airbnb into travel experiences).

It didn’t take much to Airbnb to move from beds and houses to experiences.

If just 4 percent of Facebook’s 1.7 billion global users turn to Marketplace to buy and sell used cars, Facebook would pass reigning giant Craigslist, as well as Autotrader, Cars.com and eBay Motors.

All these considerations make me think that the upcoming one is really the age of the so-called market-networks. As you may know, the term market-network, was coined by James Currier, efficiently describing something that (in his own words):

  • “Use SaaS workflow software to focus action around longer-term projects, not just a quick transaction”
  • “Promote the service provider as a differentiated individual, helping to build long-term relationships”

Market networks essentially rethink, facilitate and transform all the complex business processes and social workflows that will never be interesting to GAFA (due to the high fragmentation and niche nature of the opportunity) radically improving the overall experience of users and — often — professionals involved.

Early examples of market-networks include the famous houzz.com, Angelist, honeybook as mentioned by James in his seminal blogpost, but also growing brands like lendinvest or opendesk.

GAFA vs Market Networks impact growth model

Differently from GAFA and the likes, that substantially grow their impact by trying to climb the value chain with feature pullulation, still being attached to the idea to be attractive to anyone, market networks start by providing high value to a restricted group of users (more in general to a niche market) and then grow their impact by trying to grow their ecosystem’s size, oftentimes creating multi-national branches.

We can reasonably expect the market networks of the future to be able to leverage more on integrating utilities, telco, retail and other traditional industries through APIs and smart contracts, growing their potential and the value generated for participants.

A bit of foresight — The Infinite Tail

The evolution of the internet infrastructure is pushing the concept of the long tail as we know it even further. We could argue we’re evolving into what could be called an “infinite tail”.

Having everyone connected to anyone else in a shared space of trade, and having enabling technologies at hand to leverage on almost infinite “resources as a service” — increasingly also in the “real” world thanks to API integrations and smart contracts — is going to annihilate the cost of organizing trade among uncoordinated entities.

We can expect then, to evolve into an age where ever-larger global, social and technological infrastructures — soon to be decentralized thanks to technologies like the blockchain — will power small markets in what we could call, indeed, an internet of markets.

In these small markets— be it a small consulting company working with ten key customers, a digitally enabled artisan carefully creating products for her small fanbase or, a music artist living off local shows and special vinyl record sales — reputation will be the key enabler of this infinite tail economy and players will thrive on strong ties and long term relationships, exactly the context that market networks should be set to address, most likely with a decentralized approach (empowering myriads of different small networks) that doesn’t necessarily need network effects to exist and thrive.

reputation will be the key enabler of this “infinite tail” economy made of strong ties and long term relationships, empowering myriads of small networks

Now seemingly alienating technologies like AR or VR will end up helping us bring presence to remoteness, tearing down the last barriers to a world of thriving, relational, infinite small markets. At that moment in time the evolution towards a real global market age will be completed and we’ll be out of the Taylor bathtub forever.

Platforms of the future may be different in shape and strategy but we can be reasonably sure that they will still need to be designed around the idea that relationships play a central role in modern business.

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About the Author

This article was written by Simone Cicero of PlatformDesignToolKit

Startups

The Most Important Tech Job that Doesn’t Exist

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Yesterday I asked a prominent VC a question:

“Why is it that, despite the fact that so many successful startup ideas come from academic research, on the investment side there doesn’t seem to be anyone vetting companies on the basis of whether or not what they’re doing is consistent with the relevant research and best practices from academia?”

His response was that, unlike with startups in other sectors (e.g. biotech, cleantech, etc.), most tech startups don’t come out of academia, but rather are created to fill an unmet need in the marketplace. And that neither he nor many of his colleagues spent much time talking with academics for this reason.

This seems to be the standard thinking across the industry right now. But despite having nothing but respect for this investor, I think the party line here is unequivocally wrong.

Let’s start with the notion that most tech startups don’t come out of academia. While this may be true if you consider only the one-sentence pitch, once you look at the actual design and implementation choices these startups are making there is typically quite a lot to work with.

For example, there is a startup I recently looked at that works to match mentors with mentees. Though one might not be aware of it, there is actually a wealth of research into best practices:

  • What factors should be used when matching mentors with mentees?
  • How should the relationship between the mentor and mentee be structured?
  • What kind of training, if any, should be given to the participants?

That’s not to say that a startup that’s doing something outside the research, or even contraindicated by the research, is in any way suspect. But it does raise some questions: Does the startup have a good reason for what they’re doing? Are they aware of the relevant research? Is there something they know that we don’t?

If the entrepreneurs have good answers to these questions then it’s all the more reason to take them seriously. But if they don’t then this should raise a few red flags. And it’s not only niche startups in wonky areas where this is an issue.

For example, I rarely post to Facebook anymore, but people who follow me can still get a good idea of what I’m up to. Why? Because Facebook leverages the idea of behavioral residue to figure out what I’m doing (and let my friends know) without me having to explicitly post updates. It does this by using both interior behavioral residue, e.g. what I’m reading and clicking on within the site, and exterior behavioral residue, e.g. photos of me taken outside of Facebook.

To understand why leveraging behavioral residue is so important for social networks, consider that of people who visit the typical website only about 10% will make an account. Of those about 10% will make at least one content contribution, and of those about 10% will become core contributors. So if you consider your typical user with a couple hundred friends, this translates into seeing content from only a tiny handful of other people on a regular basis.

In contrast with Facebook, one of the reason why FourSquare has yet to succeed is due to significant problems with their initial design decisions:

  • The only content on the site comes from users who manually check into locations and post updates. This means that of my 150 or so friends, I’m only seeing what one or two of them are actually doing, so what’s the value?
  • The heavy use of extrinsic motivation (e.g. badges) has been shown time and again that extrinsic motivation undermines intrinsic motivation.

The latter especially is a good example of why investing on traction alone is problematic: many startups that leverage extrinsic rewards are able to get a good amount of initial traction, but almost none of them are able to retain users or cross the chasm into the mainstream. Why isn’t it anyone’s job to know this, even though the research is readily available for any who wants to read it? And why is it so hard to go to any major startup event without seeing VCs showering money on these sorts of startups that are so contraindicated by the research that they have almost no realistic chance of succeeding?

This same critique of investors applies equally to the startups themselves. You probably wouldn’t hire an attorney who wasn’t willing to familiarize himself with the relevant case law before going to court. So why is it that the vast majority of people hired as community managers and growth marketers have never read Robert Kraut? And the vast majority of people hired to create mobile apps have never heard of Mizuko Ito?

A lot of people associate the word design with fonts, colors, and graphics, but what the word actually means is fate — in the most existential sense of the word. That is, good design literally makes it inevitable that the user will take certain actions and have certain subjective experiences. While good UX and graphic design are essential, they’re only valuable to the extent that the person doing them knows how to create an authentic connection with the users and elicit specific emotional and social outcomes. So why are we hiring designers mainly on their Photoshop skills and maybe knowing a few tricks for optimizing conversions on landing pages? What a waste.

Of all the social sciences, the following seem to be disproportionately valuable in terms of creating and evaluating startups:

  • Psychology / Social Psychology
  • Internet Psychology / Computer Mediated Communication
  • Cognitive Development / Early Childhood Education
  • Organizational Behavior
  • Sociology
  • Education Research
  • Behavioral Economics

And yet not only is no one hiring for this, but having expertise in these areas likely won’t even get you so much as a nominal bonus. I realize that traction and team will always be the two biggest factors in determining which startups get funded, but have we really become so myopic as to place zero value on knowing whether or not a startup is congruent or contraindicated by the last 80+ years of research?

So should you invest in (or work for) the startup that sends text messages to people reminding them to take their medicine? How about the one that lets you hire temp laborers using cell phones? Or the app for club owners that purports to increase the amount of money spent on drinks? In each of these cases there is a wealth of relevant literature that can be used to help figure out whether or not the founders have done their homework and how likely they are to succeed. And it seems like if you don’t have someone whose willing to invest a few hours to read the literature then you’re playing with a significant handicap.

Investors often wait months before investing in order to let a little more information surface, during which time the valuation can (and often does) increase by literally millions. Given that the cost of doing the extra research for each deal would be nominal in the grand scheme of things, and given the fact that this research can benefit not only the investors but also the portfolio companies themselves, does it really make sense to be so confident that there’s nothing of value here?

What makes the web special is that it’s not just a technology or a place, but a set of values. That’s what we were all originally so excited about. But as startups become more and more prosaic, these values are largely becoming lost. As Howard Rheingold once said, “The ‘killer app’ of tomorrow won’t be software or hardware devices, but the social practices they make possible.” You can’t step in the same river twice, but I think there’s something to be said for startups that make possible truly novel and valuable social practices, and for creating a larger ecosystem that enables them.

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This article was written by Alex Krupp. see more.

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Entrepreneurship

How Google’s AI Mastered All Chess Knowledge in Just 4 Hours

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Chess isn’t an easy game, by human standards. But for an artificial intelligence powered by a formidable, almost alien mindset, the trivial diversion can be mastered in a few spare hours.

In a new paper, Google researchers detail how their latest AI evolution, AlphaZero, developed “superhuman performance” in chess, taking just four hours to learn the rules before obliterating the world champion chess program, Stockfish.

In other words, all of humanity’s chess knowledge – and beyond – was absorbed and surpassed by an AI in about as long as it takes to drive from New York City to Washington, DC.

After being programmed with only the rules of chess (no strategies), in just four hours AlphaZero had mastered the game to the extent it was able to best the highest-rated chess-playing program Stockfish.

In a series of 100 games against Stockfish, AlphaZero won 25 games while playing as white (with first mover advantage), and picked up three games playing as black. The rest of the contests were draws, with Stockfish recording no wins and AlphaZero no losses.

“We now know who our new overlord is,” said chess researcher David Kramaley, the CEO of chess science website Chessable.

“It will no doubt revolutionise the game, but think about how this could be applied outside chess. This algorithm could run cities, continents, universes.”

Developed by Google’s DeepMind AI lab, AlphaZero is a tweaked, more generic version of AlphaGo Zero, which specialises in playing the Chinese board game, Go.

DeepMind has been refining this AI for years, in the process besting a series of human champions who fell like dominoes before the indomitable, “Godlike” neural network.

That victory streak culminated in a startling success in October, in which a new fully autonomous version of the AI – which only learns by playing itself, never facing humans – bested all its former incarnations.

By contrast, AlphaGo Zero’s predecessors partly learned how to play the game by watching moves made by human players.

That effort was intended to assist the fledgling AI in learning strategy, but it seems it may have actually been a handicap, since AlphaGo Zero’s fully self-reliant learning proved devastatingly more effective in one-on-one competition.

“It’s like an alien civilisation inventing its own mathematics,” computer scientist Nick Hynes from MIT told Gizmodo in October.

“What we’re seeing here is a model free from human bias and presuppositions. It can learn whatever it determines is optimal, which may indeed be more nuanced that our own conceptions of the same.”

But things are moving so fast in this field that already the October accomplishment may have been outmoded.

In their new paper, the team outlines how the very latest AlphaZero AI takes the self-playing reliance – called reinforcement learning – and applies it with a much more generalised streak that gives it a broader focus to problem solving.

That broader focus means AlphaZero doesn’t just play chess. It also plays Shogi (aka Japanese chess) and Go too – and, perhaps unsurprisingly, it only took two and eight hours respectively to master those games as well.

For now, Google and DeepMind’s computer scientists aren’t commenting publicly on the new research, which hasn’t as yet been peer-reviewed.

But from what we can tell so far, this algorithm’s dizzying ascent to the pinnacle of artificial intelligence is far from over, and even chess grandmasters are bewildered by the spectacle before them.

“I always wondered how it would be if a superior species landed on Earth and showed us how they played chess,” grandmaster Peter Heine Nielsen told the BBC.

“Now I know.”

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This article was produced by Grendz. Grendz is the definitive place for new mind-blowing technology trends, science breakthroughs and green and positive ideas and news. Sign up is Free and special services are available. see more.

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