Category: Mobility

GitHub’s Top 100 Most Valuable Repositories Out of 96 Million – Hackernoon

GitHub is not just a code hosting service with version control — it’s also an enormous developer network.

The sheer size of GitHub at over 30 million accounts, more than 2 million organizations, and over 96 million repositories translates into one of the world’s most valuable development networks.

How do you quantify the value of this network? And is there a way to get the top repositories?

Here at U°OS, we ran the GitHub network through a simplified version¹ of our reputation algorithm and produced the top 100 most valuable repositories.

The result is as fascinating as it is eclectic in the way that it does feel like a good reflection of our society’s interest in the technology and where it moves.

There are the big proprietary players with open source projects — Google, Apple, Microsoft, Facebook, and even Baidu. And at the same time, there’s a Chinese anti-censorship tool.

There’s Bitcoin for cryptocurrency.

There’s a particle detector for CERN’s Large Hadron Collider.

There are gaming projects like Space Station 13 and Cataclysm: Dark Days Ahead and a gaming engine Godot.

There are education projects like freeCodeCamp, Open edX, Oppia, and Code.org.

There are web and mobile app building projects like WordPress, Joomla, and Flutter to publish your content on.

There are databases to store your content for the web like Ceph and CockroachDB.

And there’s a search engine to navigate through the content — Elasticsearch.

There are also, perhaps unsurprisingly, jailbreak projects like Cydia compatibility manager for iOS and Nintendo 3DS custom firmware.

And there’s a smart home system — Home Assistant.

All in all, it’s really a great outlook for the technology world: we learn, build stuff to broadcast our unique voices, we use crypto, break free from proprietary software on our hardware, and in the spare time we game in our automated homes. And the big companies open-source their projects.

Before I proceed with the list, a result of running the Octoverse through the reputation algorithm also produced a value score for every individual GitHub contributor. So, if you have a GitHub account and curious, you can get your score at https://u.community/github and convert it to a Universal Portable Reputation.

Top 100 projects & repositories

Out of over 96 million repositories

  1. Google Kubernetes
    Container scheduling and management
    Repository: https://github.com/kubernetes/kubernetes
    Website: https://kubernetes.io/
  2. Apache Spark
    A unified analytics engine for large-scale data processing
    Repository: https://github.com/apache/spark
    Website: http://spark.apache.org/
  3. Microsoft Visual Studio Code
    A source-code editor
    Repository: https://github.com/Microsoft/vscode
    Website: https://code.visualstudio.com/
  4. NixOS Package Collection
    A collection of packages for the Nix package manager
    Repository: https://github.com/NixOS/nixpkgs
    Website: https://nixos.org
  5. Rust
    Programming language
    Repository: https://github.com/rust-lang/rust
    Website: https://www.rust-lang.org/
  6. Firehol IP Lists
    Blacklists for Firehol, a firewall builder
    Repository: https://github.com/firehol/blocklist-ipsets
    Website: https://iplists.firehol.org/
  7. Red Hat OpenShift
    A community distribution of Kubernetes optimized for continuous application development and multi-tenant deployment
    Repository: https://github.com/openshift/origin
    Website: https://www.openshift.com/
  8. Ansible
    A deployment automation platform
    Repository: https://github.com/ansible/ansible
    Website: https://www.ansible.com/
  9. Automattic WordPress Calypso
    A JavaScript and API powered front-end for WordPress.com
    Repository: https://github.com/Automattic/wp-calypso
    Website: https://developer.wordpress.com/calypso/
  10. Microsoft .NET CoreFX
    Foundational class libraries for .NET Core
    Repository: https://github.com/dotnet/corefx
    Website: https://docs.microsoft.com/en-us/dotnet/core/
  11. Microsoft .NET Roslyn
    .NET compiler
    Repository: https://github.com/dotnet/roslyn
    Website: https://docs.microsoft.com/en-us/dotnet/csharp/roslyn-sdk/
  12. Node.js
    A JavaScript runtime built on Chrome’s V8 JavaScript engine
    Repository: https://github.com/nodejs/node
    Website: https://nodejs.org/en/
  13. TensorFlow
    Google’s machine learning framework
    Repository: https://github.com/tensorflow/tensorflow
    Website: https://www.tensorflow.org/
  14. freeCodeCamp
    Code learning platform
    Repository: https://github.com/freeCodeCamp/freeCodeCamp
    Website: https://www.freecodecamp.org/
  15. Space Station 13
    A round-based roleplaying game
    Repository: https://github.com/tgstation/tgstation
    Website: https://www.tgstation13.org/
  16. Apple Swift
    Apple’s programming language
    Repository: https://github.com/apple/swift
    Website: https://swift.org/
  17. Elasticsearch
    A search engine
    Repository: https://github.com/elastic/elasticsearch
    Website: https://www.elastic.co/products/elasticsearch
  18. Moby
    An open framework to assemble specialized container systems
    Repository: https://github.com/moby/moby
    Website: https://mobyproject.org/
  19. CockroachDB
    A cloud-native SQL database
    Repository: https://github.com/cockroachdb/cockroach
    Website: https://www.cockroachlabs.com/
  20. Cydia Compatibility Checker
    A compatibility checker for Cydia — a package manager for iOS jailbroken devices
    Repository: https://github.com/jlippold/tweakCompatible
    Website: https://jlippold.github.io/tweakCompatible/
  21. Servo
    A web browser engine
    Repository: https://github.com/servo/servo
    Website: https://servo.org/
  22. Google Flutter
    Google’s mobile app SDK to create interfaces for iOS and Android
    Repository: https://github.com/flutter/flutter
    Website: https://flutter.dev/
  23. macOS Homebrew Package Manager
    Default formulae for the missing package manager for macOS
    Repository: https://github.com/homebrew/homebrew-core
    Website: https://brew.sh/
  24. Home Assistant
    Home automation software
    Repository: https://github.com/home-assistant/home-assistant
    Website: https://www.home-assistant.io/
  25. Microsoft .NET CoreCLR
    Runtime for .NET Core
    Repository: https://github.com/dotnet/coreclr
    Website: https://docs.microsoft.com/en-us/dotnet/core/
  26. CocoaPods Specifications
    Specifications for CocoaPods, a Cocoa dependency manager
    Repository: https://github.com/CocoaPods/Specs
    Website: https://cocoapods.org/
  27. Elastic Kibana
    An analytics and search dashboard for Elasticsearch
    Repository: https://github.com/elastic/kibana
    Website: https://www.elastic.co/products/kibana
  28. Julia Language
    A technical computing language
    Repository: https://github.com/JuliaLang/julia
    Website: https://julialang.org/
  29. Microsoft TypeScript
    A superset of JavaScript that compiles to plain JavaScript
    Repository: https://github.com/Microsoft/TypeScript
    Website: https://www.typescriptlang.org/
  30. Joomla
    A content management system
    Repository: https://github.com/joomla/joomla-cms
    Website: https://www.joomla.org/
  31. DefinitelyTyped
    A repository for TypeScript type definitions
    Repository: https://github.com/DefinitelyTyped/DefinitelyTyped
    Website: http://definitelytyped.org/
  32. Homebrew Cask
    A CLI workflow for the administration of macOS applications distributed as binaries
    Repository: https://github.com/Homebrew/homebrew-cask
    Website: https://brew.sh/
  33. Ceph
    A distributed object, block, and file storage platform
    Repository: https://github.com/ceph/ceph
    Website: https://ceph.com/
  34. Go
    Programming language
    Repository: https://github.com/golang/go
    Website: https://golang.org/
  35. AMP HTML Builder
    A way to build pages for Google AMP
    Repository: https://github.com/ampproject/amphtml
    Website: https://amp.dev/
  36. Open edX
    An online education platform
    Repository: https://github.com/edx/edx-platform
    Website: https://open.edx.org/
  37. Pandas
    A data analysis and manipulation library for Python
    Repository: https://github.com/pandas-dev/pandas
    Website: https://pandas.pydata.org/
  38. Istio
    A platform to manage microservices
    Repository: https://github.com/istio/istio
    Website: https://istio.io/
  39. ManageIQ
    A containers, virtual machines, networks, and storage management platform
    Repository: https://github.com/ManageIQ/manageiq
    Website: http://manageiq.org/
  40. Godot Engine
    A multi-platform 2D and 3D game engine
    Repository: https://github.com/godotengine/godot
    Website: https://godotengine.org/
  41. Gentoo Repository Mirror
    A Gentoo ebuild repository mirror
    Repository: https://github.com/gentoo/gentoo
    Website: https://www.gentoo.org/
  42. Odoo
    A suite of web based open source business apps
    Repository: https://github.com/odoo/odoo
    Website: https://www.odoo.com/
  43. Azure Documentation
    Documentation of Microsoft Azure
    Repository: https://github.com/MicrosoftDocs/azure-docs
    Website: https://docs.microsoft.com/azure
  44. Magento
    An eCommerce platform
    Repository: https://github.com/magento/magento2
    Website: https://magento.com/
  45. Saltstack
    Software to automate the management and configuration of any infrastructure or application at scale
    Repository: https://github.com/saltstack/salt
    Website: https://www.saltstack.com/
  46. AdGuard Filters
    Ad blocking filters for AdGuard
    Repository: https://github.com/AdguardTeam/AdguardFilters
    Website: https://adguard.com/en/welcome.html
  47. Symfony
    A PHP framework
    Repository: https://github.com/symfony/symfony
    Website: https://symfony.com/
  48. CMS Software for the Large Hadron Collider
    Particle detector software components for CERN’s Large Hadron Collider
    Repository: https://github.com/cms-sw/cmssw
    Website: http://cms-sw.github.io/
  49. Red Hat OpenShift
    OpenShift installation and configuration management
    Repository: https://github.com/openshift/openshift-ansible
    Website: https://www.openshift.com/
  50. ownCloud
    Personal cloud software
    Repository: https://github.com/owncloud/core
    Website: https://owncloud.org/
  51. gRPC
    A remote procedure call (RPC) framework
    Repository: https://github.com/grpc/grpc
    Website: https://grpc.io/
  52. Liferay
    An enterprise web platform
    Repository: https://github.com/brianchandotcom/liferay-portal
    Website: https://www.liferay.com/
  53. CommCare HQ
    A mobile data collection platform
    Repository: https://github.com/dimagi/commcare-hq
    Website: https://www.commcarehq.org/
  54. WordPress Gutenberg
    An editor plugin for WordPress
    Repository: https://github.com/WordPress/gutenberg
    Website: https://wordpress.org/gutenberg/
  55. PyTorch
    A Python package for Tensor computation and deep neural networks
    Repository: https://github.com/pytorch/pytorch
    Website: https://pytorch.org/
  56. Kubernetes Test Infrastructure
    A test-infra repository for Kubernetes
    Repository: https://github.com/kubernetes/test-infra
    Website: https://kubernetes.io/
  57. Keybase
    Keybase client repository
    Repository: https://github.com/keybase/client
    Website: https://keybase.io/
  58. Facebook React
    A JavaScript library for building user interfaces
    Repository: https://github.com/facebook/react
    Website: https://reactjs.org/
  59. Code.org
    Code learning resource
    Repository: https://github.com/code-dot-org/code-dot-org
    Website: https://code.org/
  60. Bitcoin Core
    Bitcoin client software
    Repository: https://github.com/bitcoin/bitcoin
    Website: https://bitcoincore.org/
  61. Arm Mbed OS
    A platform operating system for the Internet of Things
    Repository: https://github.com/ARMmbed/mbed-os
    Website: https://www.mbed.com
  62. scikit-learn
    A Python module for machine learning
    Repository: https://github.com/scikit-learn/scikit-learn
    Website: https://scikit-learn.org
  63. Nextcloud
    A self-hosted productivity platform
    Repository: https://github.com/nextcloud/server
    Website: https://nextcloud.com/
  64. Helm Charts
    A curated list of applications for Kubernetes
    Repository: https://github.com/helm/charts
    Website: https://kubernetes.io/
  65. Terraform
    An infrastructure management tool
    Repository: https://github.com/hashicorp/terraform
    Website: https://www.terraform.io/
  66. Ant Design
    A UI design language
    Repository: https://github.com/ant-design/ant-design
    Website: https://ant.design/
  67. Phalcon Framework Documentation
    Documentation for Phalcon, a PHP framework
    Repository: https://github.com/phalcon/docs
    Website: https://docs.phalconphp.com
  68. Documentation for CMS Software for the Large Hadron Collider
    Documentation for CMS Software for CERN’s Large Hadron Collider
    Repository: https://github.com/cms-sw/cms-sw.github.io
    Website: http://cms-sw.github.io/
  69. Apache Kafka Mirror
    A mirror for Apache Kafka, a distributed streaming platform
    Repository: https://github.com/apache/kafka
    Website: https://kafka.apache.org/
  70. Electron
    A framework to write cross-platform desktop applications using JavaScript, HTML and CSS
    Repository: https://github.com/electron/electron
    Website: https://electronjs.org/
  71. Zephyr Project
    A real-time operating system
    Repository: https://github.com/zephyrproject-rtos/zephyr
    Website: https://www.zephyrproject.org/
  72. The web-platform-tests Project
    A cross-browser testsuite for the Web-platform stack
    Repository: https://github.com/web-platform-tests/wpt
    Website: https://www.w3.org/
  73. Marlin Firmware
    Optimized firmware for RepRap 3D printers based on the Arduino platform
    Repository: https://github.com/MarlinFirmware/Marlin
    Website: http://marlinfw.org/
  74. Apache MXNet
    A library for deep learning
    Repository: https://github.com/apache/incubator-mxnet
    Website: https://mxnet.apache.org/
  75. Apache Beam
    A unified programming model
    Repository: https://github.com/apache/beam
    Website: https://beam.apache.org/
  76. Fastlane
    A build and release automaton for iOS and Android apps
    Repository: https://github.com/fastlane/fastlane
    Website: https://fastlane.tools/
  77. Kubernetes Website and Documentation
    A repository for the Kubernetes website and documentation
    Repository: https://github.com/kubernetes/website
    Website: https://kubernetes.io
  78. Ruby on Rails
    A web-application framework
    Repository: https://github.com/rails/rails
    Website: https://rubyonrails.org/
  79. Zulip
    Team chat software
    Repository: https://github.com/zulip/zulip
    Website: https://zulipchat.com/
  80. Laravel
    A web application framework
    Repository: https://github.com/laravel/framework
    Website: https://laravel.com/
  81. Baidu PaddlePaddle
    Baidu’s deep learning framework
    Repository: https://github.com/PaddlePaddle/Paddle
    Website: http://www.paddlepaddle.org/
  82. Gatsby
    A web application framework
    Repository: https://github.com/gatsbyjs/gatsby
    Website: https://www.gatsbyjs.org/
  83. Rust Crate Registry
    Rust’s community package registry
    Repository: https://github.com/rust-lang/crates.io-index
    Website: https://crates.io/
  84. Nintendo 3DS Custom Firmware
    A complete guide to 3DS custom firmware
    Repository: https://github.com/hacks-guide/Guide_3DS
    Website: https://3ds.hacks.guide/
  85. TiDB
    A NewSQL database
    Repository: https://github.com/pingcap/tidb
    Website: https://pingcap.com
  86. Angular CLI
    CLI tool for Angular, a Google web application framework
    Repository: https://github.com/angular/angular-cli
    Website: https://cli.angular.io/
  87. MAPS.ME
    Offline OpenStreetMap maps for iOS and Android
    Repository: https://github.com/mapsme/omim
    Website: https://maps.me/
  88. Eclipse Che
    A cloud IDE for Eclipse
    Repository: https://github.com/eclipse/che
    Website: http://www.eclipse.org/che/
  89. Brave Browser
    A browser with native BAT cryptocurrency
    Repository: https://github.com/brave/browser-laptop
    Website: https://www.brave.com/
  90. Patchwork
    A repository to learn Git
    Repository: https://github.com/jlord/patchwork
    Website: http://jlord.us/patchwork/
  91. Angular Material
    Component infrastructure and Material Design components for Angular, a Google web application framework
    Repository: https://github.com/angular/components
    Website: https://material.angular.io/
  92. Python
    Programming language
    Repository: https://github.com/python/cpython
    Website: https://www.python.org/
  93. Space Station 13
    A round-based roleplaying game
    Repository: https://github.com/vgstation-coders/vgstation13
    Website: http://ss13.moe/
  94. Cataclysm: Dark Days Ahead
    A turn-based survival game
    Repository: https://github.com/CleverRaven/Cataclysm-DDA
    Website: http://cataclysmdda.org/
  95. Material-UI
    React components that implement Google’s Material Design
    Repository: https://github.com/mui-org/material-ui
    Website: https://material-ui.com/
  96. Ionic
    A Progressive Web Apps development framework
    Repository: https://github.com/ionic-team/ionic
    Website: https://ionicframework.com/
  97. Oppia
    A tool for collaboratively building interactive lessons
    Repository: https://github.com/oppia/oppia
    Website: https://www.oppia.org
  98. Alluxio
    A virtual distributed storage system
    Repository: https://github.com/Alluxio/alluxio
    Website: https://www.alluxio.io/
  99. XX Net
    A Chinese web proxy and anti-censorship tool
    Repository: https://github.com/XX-net/XX-Net
    Website: None
  100. Microsoft .NET CLI
    A CLI tool for .NET
    Repository: https://github.com/dotnet/cli
    Website: https://docs.microsoft.com/en-us/dotnet/core/tools/

[1] The explanation of the calculation of the simplified version is at the U°OS Network GitHub repository.

Source : https://hackernoon.com/githubs-top-100-most-valuable-repositories-out-of-96-million-bb48caa9eb0b

 

Improving the Accuracy of Automatic Speech Recognition Models for Broadcast News – Appen

Sound Waves illustration
In their paper entitled English Broadcast News Speech Recognition by Humans and Machines, the team proposes to identify techniques that close the gap between automatic speech recognition (ASR) and human performance.

Where does the data come from?

IBM’s initial work in the voice recognition space was done as part of the U.S. government’s Defense Advanced Research Projects Agency (DARPA) Effective Affordable Reusable Speech-to-Text (EARS) program, which led to significant advances in speech recognition technology. The EARS program produced about 140 hours of supervised BN training data and around 9,000 hours of very lightly supervised training data from closed captions from television shows. By contrast, EARS produced around 2,000 hours of highly supervised, human-transcribed training data for conversational telephone speech (CTS).

Lost in translation?

Because so much training data is available for CTS, the team from IBM and Appen endeavored to apply similar speech recognition strategies to BN to see how well those techniques translate across applications. To understand the challenge the team faced, it’s important to call out some important differences between the two speech styles:

Broadcast news (BN)

  • Clear, well-produced audio quality
  • Wide variety of speakers with different speaking styles
  • Varied background noise conditions — think of reporters in the field
  • Wide variety of news topics

Conversational telephone speech (CTS)

  • Often poor audio quality with sound artifacts
  • Unscripted
  • Interspersed with moments where speech overlaps between participants
  • Interruptions, sentence restarts, and background confirmations between participants i.e. “okay”, “oh”, “yes

People speaking into a phone
How the team adapted speech recognition models from CTS to BN

The team adapted the speech recognition systems that were so successfully used for the EARS CTS research: Multiple long short-term memory (LSTM) and ResNet acoustic models trained on a range of acoustic features, along with word and character LSTMs and convolutional WaveNet-style language models. This strategy had produced results between 5.1% and 9.9% accuracy for CTS in a previous study, specifically the HUB5 2000 English Evaluation conducted by the Linguistic Data Consortium (LDC). The team tested a simplified version of this approach on the BN data set, which wasn’t human-annotated, but rather created using closed captions.

Instead of adding all the available training data, the team carefully selected a reliable subset, then trained LSTM and residual network-based acoustic models with a combination of n-gram and neural network language models on that subset. In addition to automatic speech recognition testing, the team benchmarked the automatic system against an Appen-produced high-quality human transcription. The primary language model training text for all these models consisted of a total of 350 million words from different publicly available sources suitable for broadcast news.

Getting down to business

In the first set of experiments the team separately tested the LSTM and ResNet models in conjunction with the n-gram and FF-NNLM before combining scores from the two acoustic models in comparison with the results obtained on the older CTS evaluation. Unlike results observed on original CTS testing, no significant reduction in the word error rate (WER) was achieved after scores from both the LSTM and ResNet models were combined. The LSTM model with an n-gram LM individually performs quite well and its results further improve with the addition of the FF-NNLM.

For the second set of experiments, word lattices were generated after decoding with the LSTM+ResNet+n-gram+FF-NNLM model. The team generated n-best lists from these lattices and rescored them with the LSTM1-LM. LSTM2-LM was also used to rescore word lattices independently. Significant WER gains were observed after using the LSTM LMs. This led the researchers to hypothesize that the secondary fine-tuning with BN-specific data is what allows LSTM2-LM to perform better than LSTM1-LM.

The results

Our ASR results have clearly improved state-of-the-art performance, and significant progress has been made compared to systems developed over the last decade. When compared to the human performance results, the absolute ASR WER is about 3% worse. Although the machine and human error rates are comparable, the ASR system has much higher substitution and deletion error rates.

Looking at the different error types and rates, the research produced interesting takeaways:

  • There’s a significant overlap in the words that ASR and humans delete, substitute, and insert.
  • Humans seem to be careful about marking hesitations: %hesitation was the most inserted symbol in these experiments. Hesitations seem to be important in conveying meaning to the sentences in human transcriptions. The ASR systems, however, focus on blind recognition and were not successful in conveying the same meaning.
  • Machines have trouble recognizing short function words: theandofathat and these get deleted the most. Humans on the other hand, seem to catch most of them. It seems likely that these words aren’t fully articulated so the machine fails to recognize them, while humans are able to infer these words naturally.

Silhouette of person speaking on phone
Conclusion

The experiments show that speech ASR techniques can be transferred across domains to provide highly accurate transcriptions. For both acoustic and language modeling, the LSTM- and ResNet-based models proved effective and human evaluation experiments kept us honest. That said, while our methods keep improving, there is still a gap to close between human and machine performance, demonstrating a continued need for research on automatic transcription for broadcast news.

Source : https://appen.com/blog/improving-the-accuracy-of-automatic-speech-recognition-models-for-broadcast-news/

 

Which New Business Models Will Be Unleashed By Web 3.0? – Fabric

The forthcoming wave of Web 3.0 goes far beyond the initial use case of cryptocurrencies. Through the richness of interactions now possible and the global scope of counter-parties available, Web 3.0 will cryptographically connect data from individuals, corporations and machines, with efficient machine learning algorithms, leading to the rise of fundamentally new markets and associated business models.

The future impact of Web 3.0 makes undeniable sense, but the question remains, which business models will crack the code to provide lasting and sustainable value in today’s economy?

A history of Business Models across Web 1.0, Web 2.0 and Web 3.0

We will dive into native business models that have been and will be enabled by Web 3.0, while first briefly touching upon the quick-forgotten but often arduous journeys leading to the unexpected & unpredictable successful business models that emerged in Web 2.0.

To set the scene anecdotally for Web 2.0’s business model discovery process, let us not forget the journey that Google went through from their launch in 1998 to 2002 before going public in 2004:

  • In 1999, while enjoying good traffic, they were clearly struggling with their business model. Their lead investor Mike Moritz (Sequoia Capital) openly stated “we really couldn’t figure out the business model, there was a period where things were looking pretty bleak”.
  • In 2001, Google was making $85m in revenue while their rival Overture was making $288m in revenue, as CPM based online advertising was falling away post dot-com crash.
  • In 2002, adopting Overture’s ad model, Google went on to launch AdWords Select: its own pay-per-click, auction-based search-advertising product.
  • Two years later, in 2004, Google hits 84.7% of all internet searches and goes public with a valuation of $23.2 billion with annualised revenues of $2.7 billion.

After struggling for 4 years, a single small modification to their business model launched Google into orbit to become one of the worlds most valuable companies.

Looking back at the wave of Web 2.0 Business Models

Content

The earliest iterations of online content merely involved the digitisation of existing newspapers and phone books … and yet, we’ve now seen Roma (Alfonso Cuarón) receive 10 Academy Awards Nominations for a movie distributed via the subscription streaming giant Netflix.

Marketplaces

Amazon started as an online bookstore that nobody believed could become profitable … and yet, it is now the behemoth of marketplaces covering anything from gardening equipment to healthy food to cloud infrastructure.

Open Source Software

Open source software development started off with hobbyists and an idealist view that software should be a freely-accessible common good … and yet, the entire internet runs on open source software today, creating $400b of economic value a year and Github was acquired by Microsoft for $7.5b while Red Hat makes $3.4b in yearly revenues providing services for Linux.

SaaS

In the early days of Web 2.0, it might have been inconceivable that after massively spending on proprietary infrastructure one could deliver business software via a browser and become economically viable … and yet, today the large majority of B2B businesses run on SaaS models.

Sharing Economy

It was hard to believe that anyone would be willing to climb into a stranger’s car or rent out their couch to travellers … and yet, Uber and AirBnB have become the largest taxi operator and accommodation providers in the world, without owning any cars or properties.

Advertising

While Google and Facebook might have gone into hyper-growth early on, they didn’t have a clear plan for revenue generation for the first half of their existence … and yet, the advertising model turned out to fit them almost too well, and they now generate 58% of the global digital advertising revenues ($111B in 2018) which has become the dominant business model of Web 2.0.

Emerging Web 3.0 Business Models

Taking a look at Web 3.0 over the past 10 years, initial business models tend not to be repeatable or scalable, or simply try to replicate Web 2.0 models. We are convinced that while there is some scepticism about their viability, the continuous experimentation by some of the smartest builders will lead to incredibly valuable models being built over the coming years.

By exploring both the more established and the more experimental Web 3.0 business models, we aim to understand how some of them will accrue value over the coming years.

  • Issuing a native asset
  • Holding the native asset, building the network:
  • Taxation on speculation (exchanges)
  • Payment tokens
  • Burn tokens
  • Work Tokens
  • Other models

Issuing a native asset:

Bitcoin came first. Proof of Work coupled with Nakamoto Consensus created the first Byzantine Fault Tolerant & fully open peer to peer network. Its intrinsic business model relies on its native asset: BTC — a provable scarce digital token paid out to miners as block rewards. Others, including Ethereum, Monero and ZCash, have followed down this path, issuing ETH, XMR and ZEC.

These native assets are necessary for the functioning of the network and derive their value from the security they provide: by providing a high enough incentive for honest miners to provide hashing power, the cost for malicious actors to perform an attack grows alongside the price of the native asset, and in turn, the added security drives further demand for the currency, further increasing its price and value. The value accrued in these native assets has been analysed & quantified at length.

Holding the native asset, building the network:

Some of the earliest companies that formed around crypto networks had a single mission: make their respective networks more successful & valuable. Their resultant business model can be condensed to “increase their native asset treasury; build the ecosystem”. Blockstream, acting as one of the largest maintainers of Bitcoin Core, relies on creating value from its balance sheet of BTC. Equally, ConsenSys has grown to a thousand employees building critical infrastructure for the Ethereum ecosystem, with the purpose of increasing the value of the ETH it holds.

While this perfectly aligns the companies with the networks, the model is hard to replicate beyond the first handful of companies: amassing a meaningful enough balance of native assets becomes impossible after a while … and the blood, toil, tears and sweat of launching & sustaining a company cannot be justified without a large enough stake for exponential returns. As an illustration, it wouldn’t be rational for any business other than a central bank — i.e. a US remittance provider — to base their business purely on holding large sums of USD while working on making the US economy more successful.

Taxing the Speculative Nature of these Native Assets:

The subsequent generation of business models focused on building the financial infrastructure for these native assets: exchanges, custodians & derivatives providers. They were all built with a simple business objective — providing services for users interested in speculating on these volatile assets. While the likes of Coinbase, Bitstamp & Bitmex have grown into billion-dollar companies, they do not have a fully monopolistic nature: they provide convenience & enhance the value of their underlying networks. The open & permissionless nature of the underlying networks makes it impossible for companies to lock in a monopolistic position by virtue of providing “exclusive access”, but their liquidity and brands provide defensible moats over time.

Payment Tokens:

With The Rise of the Token Sale, a new wave of projects in the blockchain space based their business models on payment tokens within networks: often creating two sided marketplaces, and enforcing the use of a native token for any payments made. The assumptions are that as the network’s economy would grow, the demand for the limited native payment token would increase, which would lead to an increase in value of the token. While the value accrual of such a token model is debated, the increased friction for the user is clear — what could have been paid in ETH or DAI, now requires additional exchanges on both sides of a transaction. While this model was widely used during the 2017 token mania, its friction-inducing characteristics have rapidly removed it from the forefront of development over the past 9 months.

Burn Tokens:

Revenue generating communities, companies and projects with a token might not always be able to pass the profits on to the token holders in a direct manner. A model that garnered a lot of interest as one of the characteristics of the Binance (BNB) and MakerDAO (MKR) tokens was the idea of buybacks / token burns. As revenues flow into the project (from trading fees for Binance and from stability fees for MakerDAO), native tokens are bought back from the public market and burned, resulting in a decrease of the supply of tokens, which should lead to an increase in price. It’s worth exploring Arjun Balaji’s evaluation (The Block), in which he argues the Binance token burning mechanism doesn’t actually result in the equivalent of an equity buyback: as there are no dividends paid out at all, the “earning per token” remains at $0.

Work Tokens:

One of the business models for crypto-networks that we are seeing ‘hold water’ is the work token: a model that focuses exclusively on the revenue generating supply side of a network in order to reduce friction for users. Some good examples include Augur’s REP and Keep Network’s KEEP tokens. A work token model operates similarly to classic taxi medallions, as it requires service providers to stake / bond a certain amount of native tokens in exchange for the right to provide profitable work to the network. One of the most powerful aspects of the work token model is the ability to incentivise actors with both carrot (rewards for the work) & stick (stake that can be slashed). Beyond providing security to the network by incentivising the service providers to execute honest work (as they have locked skin in the game denominated in the work token), they can also be evaluated by predictable future cash-flows to the collective of service providers (we have previously explored the benefits and valuation methods for such tokens in this blog). In brief, such tokens should be valued based of the future expected cash flows attributable to all the service providers in the network, which can be modelled out based on assumptions on pricing and usage of the network.

A wide array of other models are being explored and worth touching upon:

  • Dual token model such as MKR/DAI & SPANK/BOOTY where one asset absorbs the volatile up- & down-side of usage and the other asset is kept stable for optimal transacting.
  • Governance tokens which provide the ability to influence parameters such as fees and development prioritisation and can be valued from the perspective of an insurance against a fork.
  • Tokenised securities as digital representations of existing assets (shares, commodities, invoices or real estate) which are valued based on the underlying asset with a potential premium for divisibility & borderless liquidity.
  • Transaction fees for features such as the models BloXroute & Aztec Protocol have been exploring with a treasury that takes a small transaction fee in exchange for its enhancements (e.g. scalability & privacy respectively).
  • Tech 4 Tokens as proposed by the Starkware team who wish to provide their technology as an investment in exchange for tokens — effectively building a treasury of all the projects they work with.
  • Providing UX/UI for protocols, such as Veil & Guesser are doing for Augur and Balance is doing for the MakerDAO ecosystem, relying on small fees or referrals & commissions.
  • Network specific services which currently include staking providers (e.g. Staked.us), CDP managers (e.g. topping off MakerDAO CDPs before they become undercollateralised) or marketplace management services such as OB1 on OpenBazaar which can charge traditional fees (subscription or as a % of revenues)
  • Liquidity providers operating in applications that don’t have revenue generating business models. For example, Uniswap is an automated market maker, in which the only route to generating revenues is providing liquidity pairs.

With this wealth of new business models arising and being explored, it becomes clear that while there is still room for traditional venture capital, the role of the investor and of capital itself is evolving. The capital itself morphs into a native asset within the network which has a specific role to fulfil. From passive network participation to bootstrap networks post financial investment (e.g. computational work or liquidity provision) to direct injections of subjective work into the networks (e.g. governance or CDP risk evaluation), investors will have to reposition themselves for this new organisational mode driven by trust minimised decentralised networks.

When looking back, we realise Web 1.0 & Web 2.0 took exhaustive experimentation to find the appropriate business models, which have created the tech titans of today. We are not ignoring the fact that Web 3.0 will have to go on an equally arduous journey of iterations, but once we find adequate business models, they will be incredibly powerful: in trust minimised settings, both individuals and enterprises will be enabled to interact on a whole new scale without relying on rent-seeking intermediaries.

Today we see 1000s of incredibly talented teams pushing forward implementations of some of these models or discovering completely new viable business models. As the models might not fit the traditional frameworks, investors might have to adapt by taking on new roles and provide work and capital (a journey we have already started at Fabric Ventures), but as long as we can see predictable and rational value accrual, it makes sense to double down, as every day the execution risk is getting smaller and smaller

Source : https://medium.com/fabric-ventures/which-new-business-models-will-be-unleashed-by-web-3-0-4e67c17dbd10

Why are Machine Learning Projects so Hard to Manage? – Lukas Biewald

I’ve watched lots of companies attempt to deploy machine learning — some succeed wildly and some fail spectacularly. One constant is that machine learning teams have a hard time setting goals and setting expectations. Why is this?

1. It’s really hard to tell in advance what’s hard and what’s easy.

Is it harder to beat Kasparov at chess or pick up and physically move the chess pieces? Computers beat the world champion chess player over twenty years ago, but reliably grasping and lifting objects is still an unsolved research problem. Humans are not good at evaluating what will be hard for AI and what will be easy. Even within a domain, performance can vary wildly. What’s good accuracy for predicting sentiment? On movie reviews, there is a lot of text and writers tend to be fairly clear about what they think and these days 90–95% accuracy is expected. On Twitter, two humans might only agree on the sentiment of a tweet 80% of the time. It might be possible to get 95% accuracy on the sentiment of tweets about certain airlines by just always predicting that the sentiment is going to be negative.

Metrics can also increase a lot in the early days of a project and then suddenly hit a wall. I once ran a Kaggle competition where thousands of people competed around the world to model my data. In the first week, the accuracy went from 35% to 65% percent but then over the next several months it never got above 68%. 68% accuracy was clearly the limit on the data with the best most up-to-date machine learning techniques. Those people competing in the Kaggle competition worked incredibly hard to get that 68% accuracy and I’m sure felt like it was a huge achievement. But for most use cases, 65% vs 68% is totally indistinguishable. If that had been an internal project, I would have definitely been disappointed by the outcome.

My friend Pete Skomoroch was recently telling me how frustrating it was to do engineering standups as a data scientist working on machine learning. Engineering projects generally move forward, but machine learning projects can completely stall. It’s possible, even common, for a week spent on modeling data to result in no improvement whatsoever.

2. Machine Learning is prone to fail in unexpected ways.

Machine learning generally works well as long as you have lots of training data *and* the data you’re running on in production looks a lot like your training data. Humans are so good at generalizing from training data that we have terrible intuitions about this. I built a little robot with a camera and a vision model trained on the millions of images of ImageNet which were taken off the web. I preprocessed the images on my robot camera to look like the images from the web but the accuracy was much worse than I expected. Why? Images off the web tend to frame the object in question. My robot wouldn’t necessarily look right at an object in the same way a human photographer would. Humans likely not even notice the difference but modern deep learning networks suffered a lot. There are ways to deal with this phenomenon, but I only noticed it because the degradation in performance was so jarring that I spent a lot of time debugging it.

Much more pernicious are the subtle differences that lead to degraded performance that are hard to spot. Language models trained on the New York Times don’t generalize well to social media texts. We might expect that. But apparently, models trained on text from 2017 experience degraded performance on text written in 2018. Upstream distributions shift over time in lots of ways. Fraud models break down completely as adversaries adapt to what the model is doing.

3. Machine Learning requires lots and lots of relevant training data.

Everyone knows this and yet it’s such a huge barrier. Computer vision can do amazing things, provided you are able to collect and label a massive amount of training data. For some use cases, the data is a free byproduct of some business process. This is where machine learning tends to work really well. For many other use cases, training data is incredibly expensive and challenging to collect. A lot of medical use cases seem perfect for machine learning — crucial decisions with lots of weak signals and clear outcomes — but the data is locked up due to important privacy issues or not collected consistently in the first place.

Many companies don’t know where to start in investing in collecting training data. It’s a significant effort and it’s hard to predict a priori how well the model will work.

What are the best practices to deal with these issues?

1. Pay a lot of attention to your training data.
Look at the cases where the algorithm is misclassifying data that it was trained on. These are almost always mislabels or strange edge cases. Either way you really want to know about them. Make everyone working on building models look at the training data and label some of the training data themselves. For many use cases, it’s very unlikely that a model will do better than the rate at which two independent humans agree.

2. Get something working end-to-end right away, then improve one thing at a time.
Start with the simplest thing that might work and get it deployed. You will learn a ton from doing this. Additional complexity at any stage in the process always improves models in research papers but it seldom improves models in the real world. Justify every additional piece of complexity.

Getting something into the hands of the end user helps you get an early read on how well the model is likely to work and it can bring up crucial issues like a disagreement between what the model is optimizing and what the end user wants. It also may make you reassess the kind of training data you are collecting. It’s much better to discover those issues quickly.

3. Look for graceful ways to handle the inevitable cases where the algorithm fails.
Nearly all machine learning models fail a fair amount of the time and how this is handled is absolutely crucial. Models often have a reliable confidence score that you can use. With batch processes, you can build human-in-the-loop systems that send low confidence predictions to an operator to make the system work reliably end to end and collect high-quality training data. With other use cases, you might be able to present low confident predictions in a way that potential errors are flagged or are less annoying to the end user.

What’s Next?

The original goal of machine learning was mostly around smart decision making, but more and more we are trying to put machine learning into products we use. As we start to rely more and more on machine learning algorithms, machine learning becomes an engineering discipline as much as a research topic. I’m incredibly excited about the opportunity to build completely new kinds of products but worried about the lack of tools and best practices. So much so that I started a company to help with this called Weights and Biases. If you’re interested in learning more, check out what we’re up to.

Source : https://medium.com/@l2k/why-are-machine-learning-projects-so-hard-to-manage-8e9b9cf49641

Open Source Software – Investable Business Model or Not? – Natallia Chykina

Open-source software (OSS) is a catalyst for growth and change in the IT industry, and one can’t overestimate its importance to the sector. Quoting Mike Olson, co-founder of Cloudera, “No dominant platform-level software infrastructure has emerged in the last ten years in closed-source, proprietary form.”

Apart from independent OSS projects, an increasing number of companies, including the blue chips, are opening their source code to the public. They start by distributing their internally developed products for free, giving rise to widespread frameworks and libraries that later become an industry standard (e.g., React, Flow, Angular, Kubernetes, TensorFlow, V8, to name a few).

Adding to this momentum, there has been a surge in venture capital dollars being invested into the sector in recent years. Several high profile funding rounds have been completed, with multimillion dollar valuations emerging (Chart 1).

But are these valuations justified? And more importantly, can the business perform, both growth-wise and profitability-wise, as venture capitalists expect? OSS companies typically monetize with a business model based around providing support and consulting services. How well does this model translate to the traditional VC growth model? Is the OSS space in a VC-driven bubble?

In this article, I assess the questions above, and find that the traditional monetization model for OSS companies based on providing support and consulting services doesn’t seem to lend itself well to the venture capital growth model, and that OSS companies likely need to switch their pricing and business models in order to justify their valuations.

OSS Monetization Models

By definition, open source software is free. This of course generates obvious advantages to consumers, and in fact, a 2008 study by The Standish Group estimates that “free open source software is [saving consumers] $60 billion [per year in IT costs].”

While providing free software is obviously good for consumers, it still costs money to develop. Very few companies are able to live on donations and sponsorships. And with fierce competition from proprietary software vendors, growing R&D costs, and ever-increasing marketing requirements, providing a “free” product necessitates a sustainable path to market success.

As a result of the above, a commonly seen structure related to OSS projects is the following: A “parent” commercial company that is the key contributor to the OSS project provides support to users, maintains the product, and defines the product strategy.

Latched on to this are the monetization strategies, the most common being the following:

  • Extra charge for enterprise services, support, and consulting. The classic model targeted at large enterprise clients with sophisticated needs. Examples: MySQL, Red Hat, Hortonworks, DataStax
  • Freemium. (advanced features/products/add-ons) A custom licensed product on top of the OSS might generate a lavish revenue stream, but it requires a lot of R&D costs and time to build. Example: Cloudera, which provides the basic version for free and charges the customers for Cloudera Enterprise
  • SaaS/PaaS business model: The modern way to monetize the OSS products that assumes centrally hosting the software and shifting its maintenance costs to the provider. Examples: Elastic, GitHub, Databricks, SugarCRM

Historically, the vast majority of OSS projects have pursued the first monetization strategy (support and consulting), but at their core, all of these models allow a company to earn money on their “bread and butter” and feed the development team as needed.

Influx of VC Dollars

An interesting recent development has been the huge inflows of VC/PE money into the industry. Going back to 2004, only nine firms producing OSS had raised venture funding, but by 2015, that number had exploded to 110, raising over $7 billion from venture capital funds (chart 2).

Underpinning this development is the large addressable market that OSS companies benefit from. Akin to other “platform” plays, OSS allows companies (in theory) to rapidly expand their customer base, with the idea that at some point in the future they can leverage this growth by beginning to tack-on appropriate monetization models in order to start translating their customer base into revenue, and profits.

At the same time, we’re also seeing an increasing number of reports about potential IPOs in the sector. Several OSS commercial companies, some of them unicorns with $1B+ valuations, have been rumored to be mulling a public markets debut (MongoDB, Cloudera, MapR, Alfresco, Automattic, Canonical, etc.).

With this in mind, the obvious question is whether the OSS model works from a financial standpoint, particularly for VC and PE investors. After all, the venture funding model necessitates rapid growth in order to comply with their 7-10 year fund life cycle. And with a product that is at its core free, it remains to be seen whether OSS companies can pin down the correct monetization model to justify the number of dollars invested into the space.

Answering this question is hard, mainly because most of these companies are private and therefore do not disclose their financial performance. Usually, the only sources of information that can be relied upon are the estimates of industry experts and management interviews where unaudited key performance metrics are sometimes disclosed.

Nevertheless, in this article, I take a look at the evidence from the only two public OSS companies in the market, Red Hat and Hortonworks, and use their publicly available information to try and assess the more general question of whether the OSS model makes sense for VC investors.

Case Study 1: Red Hat

Red Hat is an example of a commercial company that pioneered the open source business model. Founded in 1993 and going public in 1999 right before the Dot Com Bubble, they achieved the 8th biggest first-day gain in share price in the history of Wall Street at that time.

At the time of their IPO, Red Hat was not a profitable company, but since then has managed to post solid financial results, as detailed in Table 1.

Instead of chasing multifold annual growth, Red Hat has followed the “boring” path of gradually building a sustainable business. Over the last ten years, the company increased its revenues tenfold from $200 million to $2 billion with no significant change in operating and net income margins. G&A and marketing expenses never exceeded 50% of revenue (Chart 3).

The above indicates therefore that OSS companies do have a chance to build sustainable and profitable business models. Red Hat’s approach of focusing primarily on offering support and consulting services has delivered gradual but steady growth, and the company is hardly facing any funding or solvency problems, posting decent profitability metrics when compared to peers.

However, what is clear from the Red Hat case study is that such a strategy can take time—many years, in fact. While this is a perfectly reasonable situation for most companies, the issue is that it doesn’t sit well with venture capital funds who, by the very nature of their business model, require far more rapid growth profiles.

More troubling than that, for venture capital investors, is that the OSS model may in and of itself not allow for the type of growth that such funds require. As the founder of MySQL Marten Mickos put it, MySQL’s goal was “to turn the $10 billion a year database business into a $1 billion one.”

In other words, the open source approach limits the market size from the get-go by making the company focus only on enterprise customers who are able to pay for support, and foregoing revenue from a long tail of SME and retail clients. That may help explain the company’s less than exciting stock price performance post-IPO (Chart 4).

If such a conclusion were true, this would spell trouble for those OSS companies that have raised significant amounts of VC dollars along with the funds that have invested in them.

Case Study 2: Hortonworks

To further assess our overarching question of OSS’s viability as a venture capital investment, I took a look at another public OSS company: Hortonworks.

The Hadoop vendors’ market is an interesting one because it is completely built around the “open core” idea (another comparable market being the NoSQL databases space with MongoDB, Datastax, and Couchbase OSS).

All three of the largest Hadoop vendors—Cloudera, Hortonworks, and MapR—are based on essentially the same OSS stack (with some specific differences) but interestingly have different monetization models. In particular, Hortonworks—the only public company among them—is the only player that provides all of its software for free and charges only for support, consulting, and training services.

At first glance, Hortonworks’ post-IPO path appears to differ considerably from Red Hat’s in that it seems to be a story of a rapid growth and success. The company was founded in 2011, tripled its revenue every year for three consecutive years, and went public in 2014.

Immediate reception in the public markets was strong, with the stock popping 65% in the first few days of trading. Nevertheless, the company’s story since IPO has turned decisively sour. In January 2016, the company was forced to access the public markets again for a secondary public offering, a move that prompted a 60% share price fall within a month (Chart 5).

Underpinning all this is that fact that despite top-line growth, the company continues to incur substantial, and growing, operating losses. It’s evident from the financial statements that its operating performance has worsened over time, mainly because of operating expenses growing faster than revenue leading to increasing losses as a percent of revenue (Table 2).

Among all of the periods in question, Hortonworks spent more on sales and marketing than it earns in revenue. Adding to that, the company incurred significant R&D and G&A as well (Table 2).

On average, Hortonworks is burning around $100 million cash per year (less than its operating loss because of stock-based compensation expenses and changes in deferred revenue booked on the Balance Sheet). This amount is very significant when compared to its $630 million market capitalization and circa $350 million raised from investors so far. Of course, the company can still raise debt (which it did, in November 2016, to the tune of a $30 million loan from SVB), but there’s a natural limit to how often it can tap the debt markets.

All of this might of course be justified if the marketing expense served an important purpose. One such purpose could be the company’s need to diversify its customer base. In fact, when Hortonworks first launched, the company was heavily reliant on a few major clients (Yahoo and Microsoft, the latter accounting for 37% of revenues in 2013). This has now changed, and by 2016, the company reported 1000 customers.

But again, even if this were to have been the reason, one cannot ignore the costs required to achieve this. After all, marketing expenses increased eightfold between 2013 and 2015. And how valuable are the clients that Hortonworks has acquired? Unfortunately, the company reports little information on the makeup of its client base, so it’s hard to assess other important metrics such as client “stickyness”. But in a competitive OSS market where “rival developers could build the same tools—and make them free—essentially stripping the value from the proprietary software,” strong doubts loom.

With all this in mind, returning to our original question of whether the OSS model makes for good VC investments, while the Hortonworks growth story certainly seems to counter Red Hat’s—and therefore sustain the idea that such investments can work from a VC standpoint—I remain skeptical. Hortonworks seems to be chasing market share at exorbitant and unsustainable costs. And while this conclusion is based on only two companies in the space, it is enough to raise serious doubts about the overall model’s fit for VC.

Why are VCs Investing in OSS Companies?

Given the above, it seems questionable that OSS companies make for good VC investments. So with this in mind, why do venture capital funds continue to invest in such companies?

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Good Fit for a Strategic Acquisition

Apart from going public and growing organically, an OSS company may find a strategic buyer to provide a good exit opportunity for its early stage investors. And in fact, the sector has seen several high profile acquisitions over the years (Table 3).

What makes an OSS company a good target? In general, the underlying strategic rationale for an acquisition might be as follows:

  • Getting access to the client base. Sun is reported to have been motivated by this when it acquired MySQL. They wanted to access the SME market and cross-sell other products to smaller clients. Simply forking the product or developing a competing technology internally wouldn’t deliver the customer base and would have made Sun incur additional customer acquisition costs.
  • Getting control over the product. The ability to influence further development of the product is a crucial factor for a strategic buyer. This allows it to build and expand its own product offering based on the acquired products without worrying about sudden substantial changes in it. Example: Red Hat acquiring Ansible, KVM, Gluster, Inktank (Ceph), and many more
  • Entering adjacent markets. Acquiring open source companies in adjacent market segments, again, allows a company to expand the product offering, which makes vendor lock-in easier, and scales the business further. Example: Citrix acquiring XenSource
  • Acquiring the team. This is more relevant for smaller and younger projects than for larger, more well-established ones, but is worth mentioning.

What about the financial rationale? The standard transaction multiples valuation approach completely breaks apart when it comes to the OSS market. Multiples reach 20x and even 50x price/sales, and are therefore largely irrelevant, leading to the obvious conclusion that such deals are not financially but strategically motivated, and that the financial health of the target is more of a “nice to have.”

With this in mind, would a strategy of investing in OSS companies with the eventual aim of a strategic sale make sense? After all, there seems to be a decent track-record to go off of.

My assessment is that this strategy on its own is not enough. Pursuing such an approach from the start is risky—there are not enough exits in the history of OSS to justify the risks.

A Better Monetization Model: SaaS

While the promise of a lucrative strategic sale may be enough to motivate VC funds to put money to work in the space, as discussed above, it remains a risky path. As such, it feels like the rationale for such investments must be reliant on other factors as well. One such factor could be returning to basics: building profitable companies.

But as we have seen in the case studies above, this strategy doesn’t seem to be working out so well, certainly not within the timeframes required for VC investors. Nevertheless, it is important to point out that both Red Hat and Hortonworks primarily focus on monetizing through offering support and consulting services. As such, it would be wrong to dismiss OSS monetization prospects altogether. More likely, monetization models focused on support and consulting are inappropriate, but others may work better.

In fact, the SaaS business model might be the answer. As per Peter Levine’s analysis, “by packaging open source into a service, […] companies can monetize open source with a far more robust and flexible model, encouraging innovation and ongoing investment in software development.”

Why is SaaS a better model for OSS? There are several reasons for this, most of which are applicable not only to OSS SaaS, but to SaaS in general.

First, SaaS opens the market for the long tail of SME clients. Smaller companies usually don’t need enterprise support and on-premises installation, but may already have sophisticated needs from a technology standpoint. As a result, it’s easier for them to purchase a SaaS product and pay a relatively low price for using it.

Citing MongoDB’s VP of Strategy, Kelly Stirman, “Where we have a suite of management technologies as a cloud service, that is geared for people that we are never going to talk to and it’s at a very attractive price point—$39 a server, a month. It allows us to go after this long tail of the market that isn’t Fortune 500 companies, necessarily.”

Second, SaaS scales well. SaaS creates economies of scale for clients by allowing them to save money on infrastructure and operations through aggregation of resources and a combination and centralization of customer requirements, which improves manageability.

This, therefore, makes it an attractive model for clients who, as a result, will be more willing to lock themselves into monthly payment plans in order to reap the benefits of the service.

Finally, SaaS businesses are more difficult to replicate. In the traditional OSS model, everyone has access to the source code, so the support and consulting business model hardly has protection for the incumbent from new market entrants.

In the SaaS OSS case, the investment required for building the infrastructure upon which clients rely is fairly onerous. This, therefore, builds bigger barriers to entry, and makes it more difficult for competitors who lack the same amount of funding to replicate the offering.

Success Stories for OSS with SaaS

Importantly, OSS SaaS companies can be financially viable on their own. GitHub is a good example of this.

Founded in 2008, GitHub was able to bootstrap the business for four years without any external funding. The company has reportedly always been cash-flow positive (except for 2015) and generated estimated revenues of $100 million in 2016. In 2012, they accepted $100 million in funding from Andreessen Horowitz and later in 2015, $250 million from Sequoia with an implied $2 billion valuation.

Another well-known successful OSS company is DataBricks, which provides commercial support for Apache Spark, but—more importantly—allows its customers to run Spark in the cloud. The company has raised $100 million from Andreessen Horowitz, Data Collective, and NEA. Unfortunately, we don’t have a lot of insight into their profitability, but they are reported to be performing strongly and had more than 500 companies using the technology as of 2015 already.

Generally, many OSS companies are in one way or another gradually drifting towards the SaaS model or other types of cloud offerings. For instance, Red Hat is moving to PaaS over support and consulting, as evidenced by OpenShift and the acquisition of AnsibleWorks.

Different ways of mixing support and consulting with SaaS are common too. We, unfortunately, don’t have detailed statistics on Elastic’s on-premises vs. cloud installation product offering, but we can see from the presentation of its closest competitor Splunk that their SaaS offering is gaining scale: Its share in revenue is expected to triple by 2020 (chart 6).

Investable Business Model or Not?

To conclude, while recent years have seen an influx of venture capital dollars poured into OSS companies, there are strong doubts that such investments make sense if the monetization models being used remain focused on the traditional support and consulting model. Such a model can work (as seen in the Red Hat case study) but cannot scale at the pace required by VC investors.

Of course, VC funds may always hope for a lucrative strategic exit, and there have been several examples of such transactions. But relying on this alone is not enough. OSS companies need to innovate around monetization strategies in order to build profitable and fast-growing companies.

The most plausible answer to this conundrum may come from switching to SaaS as a business model. SaaS allows one to tap into a longer-tail of SME clients and improve margins through better product offerings. Quoting Peter Levine again, “Cloud and SaaS adoption is accelerating at an order of magnitude faster than on-premise deployments, and open source has been the enabler of this transformation. Beyond SaaS, I would expect there to be future models for open source monetization, which is great for the industry”.

Whatever ends up happening, the sheer amount of venture investment into OSS companies means that smarter monetization strategies will be needed to keep the open source dream alive

Source : https://www.toptal.com/finance/venture-capital-consultants/open-source-software-investable-business-model-or-not

Industrial tech may not be sexy, but VCs are loving it – John Tough

There are nearly 300 industrial-focused companies within the Fortune 1,000. The medium revenue for these economy-anchoring firms is nearly $4.9 billion, resulting in over $9 trillion in market capitalization.

Due to the boring nature of some of these industrial verticals and the complexity of the value chain, venture-related events tend to get lost within our traditional VC news channels. But entrepreneurs (and VCs willing to fund them) are waking up to the potential rewards of gaining access to these markets.

Just how active is the sector now?

That’s right: Last year nearly $6 billion went into Series A, B & C startups within the industrial, engineering & construction, power, energy, mining & materials, and mobility segments. Venture capital dollars deployed to these sectors is growing at a 30 percent annual rate, up from ~$750 million in 2010.

And while $6 billion invested is notable due to the previous benchmarks, this early stage investment figure still only equates to ~0.2 percent of the revenue for the sector and ~1.2 percent of industry profits.

The number of deals in the space shows a similarly strong growth trajectory. But there are some interesting trends beginning to emerge: The capital deployed to the industrial technology market is growing at a faster clip than the number of deals. These differing growth trajectories mean that the average deal size has grown by 45 percent in the last eight years, from $18 to $26 million.

Detail by stage of financing

Median Series A deal size in 2018 was $11 million, representing a modest 8 percent increase in size versus 2012/2013. But Series A deal volume is up nearly 10x since then!

Median Series B deal size in 2018 was $20 million, an 83 percent growth over the past five years and deal volume is up about 4x.

Median Series C deal size in 2018 was $33 million, representing an enormous 113 percent growth over the past five years. But Series C deals have appeared to reach a plateau in the low 40s, so investors are becoming pickier in selecting the winners.

These graphs show that the Series A investors have stayed relatively consistent and that the overall 46 percent increase in sector deal size growth primarily originates from the Series B and Series C investment rounds. With bigger rounds, how are valuation levels adjusting?

Above: Growth in pre-money valuation particularly acute in later stage deals

The data shows that valuations have increased even faster than the round sizes have grown themselves. This means management teams are not feeling any incremental dilution by raising these larger rounds.

  • The average Series A round now buys about 24 percent, slightly less than five years ago
  • The average Series B round now buys about 22 percent of the company, down from 26 percent five years ago
  • The average Series C round now buys approximately 20 percent, down from 23 percent five years ago.

Some conclusions

  • Dollars invested as a portion of industry revenue and profit allows for further capital commitments.
  • There is a growing appreciation for the industrial sales cycle. Investor willingness to wait for reduced risk to deploy even more capital in the perceived winners appears to be driving this trend.
  • Entrepreneurs that can successfully de-risk their enterprise through revenue, partnerships, and industry hires will gain access to outsized capital pools. The winners in this market tend to compound as later customers look to early adopters
  • Uncertainty still remains about exit opportunities for technology companies that serve these industries. While there are a few headline-grabbing acquisitions (PlanGrid, Kurion, OSIsoft), we are not hearing about a sizable exit from this market on a weekly or monthly cadence. This means we won’t know for a few years about the returns impact of these rising valuations. Grab your hard hat!

Source : https://venturebeat.com/2019/01/22/industrial-tech-may-not-be-sexy-but-vcs-are-loving-it/

Money Out of Nowhere: How Internet Marketplaces Unlock Economic Wealth – Bill Gurley

In 1776, Adam Smith released his magnum opus, An Inquiry into the Nature and Causes of the Wealth of Nationsin which he outlined his fundamental economic theories. Front and center in the book — in fact in Book 1, Chapter 1 — is his realization of the productivity improvements made possible through the “Division of Labour”:

It is the great multiplication of the production of all the different arts, in consequence of the division of labour, which occasions, in a well-governed society, that universal opulence which extends itself to the lowest ranks of the people. Every workman has a great quantity of his own work to dispose of beyond what he himself has occasion for; and every other workman being exactly in the same situation, he is enabled to exchange a great quantity of his own goods for a great quantity, or, what comes to the same thing, for the price of a great quantity of theirs. He supplies them abundantly with what they have occasion for, and they accommodate him as amply with what he has occasion for, and a general plenty diffuses itself through all the different ranks of society.

Smith identified that when men and women specialize their skills, and also importantly “trade” with one another, the end result is a rise in productivity and standard of living for everyone. In 1817, David Ricardo published On the Principles of Political Economy and Taxation where he expanded upon Smith’s work in developing the theory of Comparative Advantage. What Ricardo proved mathematically, is that if one country has simply a comparative advantage (not even an absolute one), it still is in everyone’s best interest to embrace specialization and free trade. In the end, everyone ends up in a better place.

There are two key requirements for these mechanisms to take force. First and foremost, you need free and open trade. It is quite bizarre to see modern day politicians throw caution to the wind and ignore these fundamental tenants of economic science. Time and time again, the fact patterns show that when countries open borders and freely trade, the end result is increased economic prosperity. The second, and less discussed, requirement is for the two parties that should trade to be aware of one another’s goods or services. Unfortunately, either information asymmetry or physical distances and the resulting distribution costs can both cut against the economic advantages that would otherwise arise for all.

Fortunately, the rise of the Internet, and specifically Internet marketplace models, act as accelerants to the productivity benefits of the division of labour AND comparative advantage by reducing information asymmetry and increasing the likelihood of a perfect match with regard to the exchange of goods or services. In his 2005 book, The World Is Flat, Thomas Friedman recognizes that the Internet has the ability to create a “level playing field” for all participants, and one where geographic distances become less relevant. The core reason that Internet marketplaces are so powerful is because in connecting economic traders that would otherwise not be connected, they unlock economic wealth that otherwise would not exist. In other words, they literally create “money out of nowhere.”

EXCHANGE OF GOODS MARKETPLACES

Any discussion of Internet marketplaces begins with the first quintessential marketplace, ebay(*). Pierre Omidyarfounded AuctionWeb in September of 1995, and its rise to fame is legendary. What started as a web site to trade laser pointers and Beanie Babies (the Pez dispenser start is quite literally a legend), today enables transactions of approximately $100B per year. Over its twenty-plus year lifetime, just over one trillion dollars in goods have traded hands across eBay’s servers. These transactions, and the profits realized by the sellers, were truly “unlocked” by eBay’s matching and auction services.

In 1999, Jack Ma created Alibaba, a Chinese-based B2B marketplace for connecting small and medium enterprise with potential export opportunities. Four years later, in May of 2003, they launched Taobao Marketplace, Alibaba’s answer to eBay. By aggressively launching a free to use service, Alibaba’s Taobao quickly became the leading person-to-person trading site in China. In 2018, Taobao GMV (Gross Merchandise Value) was a staggering RMB2,689 billion, which equates to $428 billion in US dollars.

There have been many other successful goods marketplaces that have launched post eBay & Taobao — all providing a similar service of matching those who own or produce goods with a distributed set of buyers who are particularly interested in what they have to offer. In many cases, a deeper focus on a particular category or vertical allows these marketplaces to distinguish themselves from broader marketplaces like eBay.

  • In 2000, Eric Baker and Jeff Fluhr founded StubHub, a secondary ticket exchange marketplace. The company was acquired by ebay in January 2007. In its most recent quarter, StubHub’s GMV reached $1.4B, and for the entire year 2018, StubHub had GMV of $4.8B.
  • Launched in 2005, Etsy is a leading marketplaces for the exchange of vintage and handmade items. In its most recent quarter, the company processed the exchange of $923 million of sales, which equates to a $3.6B annual GMV.
  • Founded by Michael Bruno in Paris in 2001, 1stdibs(*) is the world’s largest online marketplace for luxury one-of-a-kind antiques, high-end modern furniture, vintage fashion, jewelry, and fine art. In November 2011, David Rosenblatt took over as CEO and has been scaling the company ever since. Over the past few years dealers, galleries, and makers have matched billions of dollars in merchandise to trade buyers and consumer buyers on the platform.
  • Poshmark was founded by Manish Chandra in 2011. The website, which is an exchange for new and used clothing, has been remarkably successful. Over 4 million sellers have earned over $1 billion transacting on the site.
  • Julie Wainwright founded The Real Real in 2011. The company is an online marketplace for authenticated luxury consignment. In 2017, the company reported sales of over $500 million.
  • In 2015, Eddy Lu and Daishin Sugano launched GOAT, a marketplace for the exchange of sneakers. Despite this narrow focus, the company has been remarkably successful. The estimated annual GMV of GOAT and its leading competitor Stock X is already over $1B per year (on a combined basis).

SHARING ECONOMY MARKETPLACES

With the launch of Airbnb in 2008 and Uber(*) in 2009, these two companies established a new category of marketplaces known as the “sharing economy.” Homes and automobiles are the two most expensive items that people own, and in many cases the ability to own the asset is made possible through debt — mortgages on houses and car loans or leases for automobiles. Despite this financial exposure, for many people these assets are materially underutilized. Many extra rooms and second homes are vacant most of the year, and the average car is used less than 5% of the time. Sharing economy marketplaces allow owners to “unlock” earning opportunities from these underutilized assets.

Airbnb was founded by Joe Gebbia and Brian Chesky in 2008. Today there are over 5 million Airbnb listings in 81,000 cities. Over two million people stay in an Airbnb each night. In November of this year, the company announced that it had achieved “substantially” more than $1B in revenue in the third quarter. Assuming a marketplace rake of something like 11%, this would imply gross room revenue of over $9B for the quarter — which would be $36B annualized. As the company is still growing, we can easily guess that in 2019-2020 time frame, Airbnb will be delivering around $50B per year to home-owners who were previously sitting on highly underutilized assets. This is a major “unlocking.”

When Garrett Camp and Travis Kalanick founded Uber in 2009, they hatched the industry now known as ride-sharing. Today over 3 million people around the world use their time and their underutilized automobiles to generate extra income. Without the proper technology to match people who wanted a ride with people who could provide that service, taxi and chauffeur companies were drastically underserving the potential market. As an example, we estimate that ride-sharing revenues in San Francisco are well north of 10X what taxis and black cars were providing prior to the launch of ride-sharing. These numbers will go even higher as people increasingly forgo the notion of car ownership altogether. We estimate that the global GMV for ride sharing was over $100B in 2018 (including Uber, Didi, Grab, Lyft, Yandex, etc) and still growing handsomely. Assuming a 20% rake, this equates to over $80B that went into the hands of ride-sharing drivers in a single year — and this is an industry that did not exist 10 years ago. The matching made possible with today’s GPS and Internet-enabled smart phones is a massive unlocking of wealth and value.

While it is a lesser known category, using your own backyard and home to host dog guests as an alternative to a kennel is a large and growing business. Once again, this is an asset against which the marginal cost to host a dog is near zero. By combining their time with this otherwise unused asset, dog sitters are able to offer a service that is quite compelling for consumers. Rover.com (*) in Seattle, which was founded by Greg Gottesman and Aaron Easterly in 2011, is the leading player in this market. (Benchmark is an investor in Rover through a merger with DogVacay in 2017). You may be surprised to learn that this is already a massive industry. In less than a decade since the company started, Rover has already paid out of half a billion dollars to hosts that participate on the platform.

EXCHANGE OF LABOR MARKETPLACES

While not as well known as the goods exchanges or sharing economy marketplaces, there is a growing and exciting increase in the number of marketplaces that help match specifically skilled labor with key opportunities to monetize their skills. The most noteworthy of these is likely Upwork(*), a company that formed from the merger of Elance and Odesk. Upwork is a global freelancing platform where businesses and independent professionals can connect and collaborate remotely. Popular categories include web developers, mobile developers, designers, writers, and accountants. In the 12 months ended June 30, 2018, the Upwork platform enabled $1.56 billion of GSV (gross services revenue) across 2.0 million projects between approximately 375,000 freelancers and 475,000 clients in over 180 countries. These labor matches represent the exact “world is flat” reality outlined in Friedman’s book.

Other noteworthy and emerging labor marketplaces:

  • HackerOne(*) is the leading global marketplace that coordinates the world’s largest corporate “bug bounty” programs with a network of the world’s leading hackers. The company was founded in 2012 by Michiel PrinsJobert AbmaAlex Rice and Merijn Terheggen, and today serves the needs of over 1,000 corporate bug bounty programs. On top of that, the HackerOne network of over 300,000 hackers (adding 600 more each day) has resolved over 100K confirmed vulnerabilities which resulted in over $46 million in awards to these individuals. There is an obvious network effect at work when you bring together the world’s leading programs and the world’s leading hackers on a single platform. The Fortune 500 is quickly learning that having a bug bounty program is an essential step in fighting cyber crime, and that HackerOne is the best place to host their program.
  • Wyzant is a leading Chicago-based marketplace that connects tutors with students around the country. The company was founded by Andrew Geant and Mike Weishuhn in 2005. The company has over 80,000 tutors on its platform and has paid out over $300 million to these professionals. The company started matching students with tutors for in-person sessions, but increasingly these are done “virtually” over the Internet.
  • Stitch Fix (*) is a leading provider of personalized clothing services that was founded by Katrina Lake in 2011. While the company is not primarily a marketplace, each order is hand-curated by a work-at-home “stylist” who works part-time on their own schedule from the comfort of their own home. Stitch Fix’s algorithms match the perfect stylist with each and every customer to help ensure the optimal outcome for each client. As of the end of 2018, Stitch Fix has paid out well over $100 million to their stylists.
  • Swing Education was founded in 2015 with the objective of creating a marketplace for substitute teachers. While it is still early in the company’s journey, they have already established themselves as the leader in the U.S. market. Swing is now at over 1,200 school partners and has filled over 115,000 teacher absence days. They have helped 2,000 substitute teachers get in the classroom in 2018, including 400 educators who earned permits, which Swing willingly financed. While it seems obvious in retrospect, having all substitutes on a single platform creates massive efficiency in a market where previously every single school had to keep their own list and make last minute calls when they had vacancies. And their subs just have to deal with one Swing setup process to get access to subbing opportunities at dozens of local schools and districts.
  • RigUp was founded by Xuan Yong and Mike Witte in Austin, Texas in March of 2014. RigUp is a leading labor marketplace focused on the oilfield services industry. “The company’s platform offers a large network of qualified, insured and compliant contractors and service providers across all upstream, midstream and downstream operations in every oil and gas basin, enabling companies to hire quickly, track contractor compliance, and minimize administrative work.” According to the company, GMV for 2017 was an impressive $150 million, followed by an astounding $600 million in 2018. Often, investors miss out on vertically focused companies like RigUp as they find themselves overly anxious about TAM (total available market). As you can see, that can be a big mistake.
  • VIPKid, which was founded in 2013 by Cindy Mi, is a truly amazing story. The idea is simple and simultaneously brilliant. VIPKid links students in China who want to learn English with native English speaking tutors in the United States and Canada. All sessions are done over the Internet, once again epitomizing Friedman’s very flat world. In November of 2018, the company reported having 60,000 teachers contracted to teach over 500,000 students. Many people believe the company is now well north of a US$1B run rate, which implies that around $1B will pass hands from Chinese parents to western teachers in 2019. That is quite a bit of supplemental income for U.S.-based teachers.

These vertical labor marketplaces are to LinkedIn what companies like Zillow, Expedia, and GrubHub are to Google search. Through a deeper understanding of a particular vertical, a much richer perspective on the quality and differentiation of the participants, and the enablement of transactions — you create an evolved service that has much more value to both sides of the transaction. And for those professionals participating in these markets, your reputation on the vertical service matters way more than your profile on LinkedIn.

NEW EMERGING MARKETPLACES

Having been a fortunate investor in many of the previously mentioned companies (*), Benchmark remains extremely excited about future marketplace opportunities that will unlock wealth on the Internet. Here are an example of two such companies that we have funded in the past few years.

The New York Times describes Hipcamp as “The Sharing Economy Visits the Backcountry.” Hipcamp(*) was founded in 2013 by Alyssa Ravasio as an engine to search across the dozens and dozens of State and National park websites for campsite availability. As Hipcamp gained traction with campers, landowners with land near many of the National and State parks started to reach out to Hipcamp asking if they could list their land on Hipcamp too. Hipcamp now offers access to more than 350k campsites across public and private land, and their most active private land hosts make over $100,000 per year hosting campers. This is a pretty amazing value proposition for both land owners and campers. If you are a rural landowner, here is a way to create “money out of nowhere” with very little capital expenditures. And if you are a camper, what could be better than to camp at a unique, bespoke campsite in your favorite location.

Instawork(*) is an on-demand staffing app for gig workers (professionals) and hospitality businesses (partners). These working professionals seek economic freedom and a better life, and Instawork gives them both — an opportunity to work as much as they like, but on their own terms with regard to when and where. On the business partner side, small business owners/managers/chefs do not have access to reliable sources to help them with talent sourcing and high turnover, and products like  LinkedIn are more focused on white-collar workers. Instawork was cofounded by Sumir Meghani in San Franciso and was a member of the 2015 Y-Combinator class. 2018 was a break-out year for Instawork with 10X revenue growth and 12X growth in Professionals on the platform. The average Instawork Professional is highly engaged on the platform, and typically opens the Instawork app ten times a day. This results in 97% of gigs being matched in less than 24 hours — which is powerfully important to both sides of the network. Also noteworthy, the Professionals on Instawork average 150% of minimum wage, significantly higher than many other labor marketplaces. This higher income allows Instawork Professionals like Jose, to begin to accomplish their dreams.

THE POWER OF THESE PLATFORMS

As you can see, these numerous marketplaces are a direct extension of the productivity enhancers first uncovered by Adam Smith and David Ricardo. Free trade, specialization, and comparative advantage are all enhanced when we can increase the matching of supply and demand of goods and services as well as eliminate inefficiency and waste caused by misinformation or distance. As a result, productivity naturally improves.

Specific benefits of global internet marketplaces:

    1. Increase wealth distribution (all examples)
    2. Unlock wasted potential of assets (Uber, AirBNB, Rover, and Hipcamp)
    3. Better match of specific workers with specific opportunities (Upwork, WyzAnt, RigUp, VIPKid, Instawork)
    4. Make specific assets reachable and findable (Ebay, Etsy, 1stDibs, Poshmark, GOAT)
    5. Allow for increased specialization (Etsy, Upwork, RigUp)
    6. Enhance supplemental labor opportunities (Uber, Stitch Fix, SwingEducation, Instawork, VIPKid), where the worker is in control of when and where they work
    7. Reduces forfeiture by enhancing utilization (mortgages, car loans, etc) (Uber, AirBnb, Rover, Hipcamp)

Source : http://abovethecrowd.com/2019/02/27/money-out-of-nowhere-how-internet-marketplaces-unlock-economic-wealth/

Digital Transformation of Business and Society: Challenges and Opportunities by 2020 – Frank Diana

At a recent KPMG Robotic Innovations event, Futurist and friend Gerd Leonhard delivered a keynote titled “The Digital Transformation of Business and Society: Challenges and Opportunities by 2020”. I highly recommend viewing the Video of his presentation. As Gerd describes, he is a Futurist focused on foresight and observations — not predicting the future. We are at a point in history where every company needs a Gerd Leonhard. For many of the reasons presented in the video, future thinking is rapidly growing in importance. As Gerd so rightly points out, we are still vastly under-estimating the sheer velocity of change.

With regard to future thinking, Gerd used my future scenario slide to describe both the exponential and combinatorial nature of future scenarios — not only do we need to think exponentially, but we also need to think in a combinatorial manner. Gerd mentioned Tesla as a company that really knows how to do this.

Our Emerging Future

He then described our current pivot point of exponential change: a point in history where humanity will change more in the next twenty years than in the previous 300. With that as a backdrop, he encouraged the audience to look five years into the future and spend 3 to 5% of their time focused on foresight. He quoted Peter Drucker (“In times of change the greatest danger is to act with yesterday’s logic”) and stated that leaders must shift from a focus on what is, to a focus on what could be. Gerd added that “wait and see” means “wait and die” (love that by the way). He urged leaders to focus on 2020 and build a plan to participate in that future, emphasizing the question is no longer what-if, but what-when. We are entering an era where the impossible is doable, and the headline for that era is: exponential, convergent, combinatorial, and inter-dependent — words that should be a key part of the leadership lexicon going forward. Here are some snapshots from his presentation:

  • Because of exponential progression, it is difficult to imagine the world in 5 years, and although the industrial era was impactful, it will not compare to what lies ahead. The danger of vastly under-estimating the sheer velocity of change is real. For example, in just three months, the projection for the number of autonomous vehicles sold in 2035 went from 100 million to 1.5 billion
  • Six years ago Gerd advised a German auto company about the driverless car and the implications of a sharing economy — and they laughed. Think of what’s happened in just six years — can’t imagine anyone is laughing now. He witnessed something similar as a veteran of the music business where he tried to guide the industry through digital disruption; an industry that shifted from selling $20 CDs to making a fraction of a penny per play. Gerd’s experience in the music business is a lesson we should learn from: you can’t stop people who see value from extracting that value. Protectionist behavior did not work, as the industry lost 71% of their revenue in 12 years. Streaming music will be huge, but the winners are not traditional players. The winners are Spotify, Apple, Facebook, Google, etc. This scenario likely plays out across every industry, as new businesses are emerging, but traditional companies are not running them. Gerd stressed that we can’t let this happen across these other industries
  • Anything that can be automated will be automated: truck drivers and pilots go away, as robots don’t need unions. There is just too much to be gained not to automate. For example, 60% of the cost in the system could be eliminated by interconnecting logistics, possibly realized via a Logistics Internet as described by economist Jeremy Rifkin. But the drive towards automation will have unintended consequences and some science fiction scenarios could play out. Humanity and technology are indeed intertwining, but technology does not have ethics. A self-driving car would need ethics, as we make difficult decisions while driving all the time. How does a car decide to hit a frog versus swerving and hitting a mother and her child? Speaking of science fiction scenarios, Gerd predicts that when these things come together, humans and machines will have converged:
  • Gerd has been using the term “Hellven” to represent the two paths technology can take. Is it 90% heaven and 10% hell (unintended consequences), or can this equation flip? He asks the question: Where are we trying to go with this? He used the real example of Drones used to benefit society (heaven), but people buying guns to shoot them down (hell). As we pursue exponential technologies, we must do it in a way that avoids negative consequences. Will we allow humanity to move down a path where by 2030, we will all be human-machine hybrids? Will hacking drive chaos, as hackers gain control of a vehicle? A recent Jeep recall of 1.4 million jeeps underscores the possibility. A world of super intelligence requires super humanity — technology does not have ethics, but society depends on it. Is this Ray Kurzweil vision what we want?
  • Is society truly ready for human-machine hybrids, or even advancements like the driverless car that may be closer to realization? Gerd used a very effective Video to make the point
  • Followers of my Blog know I’m a big believer in the coming shift to value ecosystems. Gerd described this as a move away from Egosystems, where large companies are running large things, to interdependent Ecosystems. I’ve talked about the blurring of industry boundaries and the movement towards ecosystems. We may ultimately move away from the industry construct and end up with a handful of ecosystems like: mobility, shelter, resources, wellness, growth, money, maker, and comfort
  • Our kids will live to 90 or 100 as the default. We are gaining 8 hours of longevity per day — one third of a year per year. Genetic engineering is likely to eradicate disease, impacting longevity and global population. DNA editing is becoming a real possibility in the next 10 years, and at least 50 Silicon Valley companies are focused on ending aging and eliminating death. One such company is Human Longevity Inc., which was co-founded by Peter Diamandis of Singularity University. Gerd used a quote from Peter to help the audience understand the motivation: “Today there are six to seven trillion dollars a year spent on healthcare, half of which goes to people over the age of 65. In addition, people over the age of 65 hold something on the order of $60 trillion in wealth. And the question is what would people pay for an extra 10, 20, 30, 40 years of healthy life. It’s a huge opportunity”
  • Gerd described the growing need to focus on the right side of our brain. He believes that algorithms can only go so far. Our right brain characteristics cannot be replicated by an algorithm, making a human-algorithm combination — or humarithm as Gerd calls it — a better path. The right brain characteristics that grow in importance and drive future hiring profiles are:
  • Google is on the way to becoming the global operating system — an Artificial Intelligence enterprise. In the future, you won’t search, because as a digital assistant, Google will already know what you want. Gerd quotes Ray Kurzweil in saying that by 2027, the capacity of one computer will equal that of the human brain — at which point we shift from an artificial narrow intelligence, to an artificial general intelligence. In thinking about AI, Gerd flips the paradigm to IA or intelligent Assistant. For example, Schwab already has an intelligent portfolio. He indicated that every bank is investing in intelligent portfolios that deal with simple investments that robots can handle. This leads to a 50% replacement of financial advisors by robots and AI
  • This intelligent assistant race has just begun, as Siri, Google Now, Facebook MoneyPenny, and Amazon Echo vie for intelligent assistant positioning. Intelligent assistants could eliminate the need for actual assistants in five years, and creep into countless scenarios over time. Police departments are already capable of determining who is likely to commit a crime in the next month, and there are examples of police taking preventative measures. Augmentation adds another dimension, as an officer wearing glasses can identify you upon seeing you and have your records displayed in front of them. There are over 100 companies focused on augmentation, and a number of intelligent assistant examples surrounding IBM Watson; the most discussed being the effectiveness of doctor assistance. An intelligent assistant is the likely first role in the autonomous vehicle transition, as cars step in to provide a number of valuable services without completely taking over. There are countless Examples emerging
  • Gerd took two polls during his keynote. Here is the first: how do you feel about the rise of intelligent digital assistants? Answers 1 and 2 below received the lion share of the votes
  • Collectively, automation, robotics, intelligent assistants, and artificial intelligence will reframe business, commerce, culture, and society. This is perhaps the key take away from a discussion like this. We are at an inflection point where reframing begins to drive real structural change. How many leaders are ready for true structural change?
  • Gerd likes to refer to the 7-ations: Digitization, De-Materialization, Automation, Virtualization, Optimization, Augmentation, and Robotization. Consequences of the exponential and combinatorial growth of these seven include dependency, job displacement, and abundance. Whereas these seven are tools for dramatic cost reduction, they also lead to abundance. Examples are everywhere, from the 16 million songs available through Spotify, to the 3D printed cars that require only 50 parts. As supply exceeds demand in category after category, we reach abundance. As Gerd put it, in five years’ time, genome sequencing will be cheaper than flushing the toilet and abundant energy will be available by 2035 (2015 will be the first year that a major oil company will leave the oil business to enter the abundance of the renewable business). Other things to consider regarding abundance:
  • Efficiency and business improvement is a path not a destination. Gerd estimates that total efficiency will be reached in 5 to 10 years, creating value through productivity gains along the way. However, after total efficiency is met, value comes from purpose. Purpose-driven companies have an aspirational purpose that aims to transform the planet; referred to as a massive transformative purpose in a recent book on exponential organizations. When you consider the value that the millennial generation places on purpose, it is clear that successful organizations must excel at both technology and humanity. If we allow technology to trump humanity, business would have no purpose
  • In the first phase, the value lies in the automation itself (productivity, cost savings). In the second phase, the value lies in those things that cannot be automated. Anything that is human about your company cannot be automated: purpose, design, and brand become more powerful. Companies must invent new things that are only possible because of automation
  • Technological unemployment is real this time — and exponential. Gerd talked to a recent study by the Economist that describes how robotics and artificial intelligence will increasingly be used in place of humans to perform repetitive tasks. On the other side of the spectrum is a demand for better customer service and greater skills in innovation driven by globalization and falling barriers to market entry. Therefore, creativity and social intelligence will become crucial differentiators for many businesses; jobs will increasingly demand skills in creative problem-solving and constructive interaction with others
  • Gerd described a basic income guarantee that may be necessary if some of these unemployment scenarios play out. Something like this is already on the ballot in Switzerland, and it is not the first time this has been talked about:
  • In the world of automation, experience becomes extremely valuable — and you can’t, nor should attempt to — automate experiences. We clearly see an intense focus on customer experience, and we had a great discussion on the topic on an August 26th Game Changers broadcast. Innovation is critical to both the service economy and experience economy. Gerd used a visual to describe the progression of economic value:
Source: B. Joseph Pine II and James Gilmore: The Experience Economy
  • Gerd used a second poll to sense how people would feel about humans becoming artificially intelligent. Here again, the audience leaned towards the first two possible answers:

Gerd then summarized the session as follows:

The future is exponential, combinatorial, and interdependent: the sooner we can adjust our thinking (lateral) the better we will be at designing our future.

My take: Gerd hits on a key point. Leaders must think differently. There is very little in a leader’s collective experience that can guide them through the type of change ahead — it requires us all to think differently

When looking at AI, consider trying IA first (intelligent assistance / augmentation).

My take: These considerations allow us to create the future in a way that avoids unintended consequences. Technology as a supplement, not a replacement

Efficiency and cost reduction based on automation, AI/IA and Robotization are good stories but not the final destination: we need to go beyond the 7-ations and inevitable abundance to create new value that cannot be easily automated.

My take: Future thinking is critical for us to be effective here. We have to have a sense as to where all of this is heading, if we are to effectively create new sources of value

We won’t just need better algorithms — we also need stronger humarithms i.e. values, ethics, standards, principles and social contracts.

My take: Gerd is an evangelist for creating our future in a way that avoids hellish outcomes — and kudos to him for being that voice

“The best way to predict the future is to create it” (Alan Kay).

My Take: our context when we think about the future puts it years away, and that is just not the case anymore. What we think will take ten years is likely to happen in two. We can’t create the future if we don’t focus on it through an exponential lens

Source : https://medium.com/@frankdiana/digital-transformation-of-business-and-society-5d9286e39dbf

How redesigning an enterprise product taught me to extend myself – Instacart

As designers, we want to work on problems that are intriguing and “game-changing”. All too often, we limit the “game-changing” category to a handful of consumer-facing mobile apps and social networks. The truth is: enterprise software gives designers a unique set of complex problems to solve. Enterprise platforms usually have a savvy set of users with very specific needs — needs that, when addressed, often affect a business’s bottom line.

One of my first projects as a product designer here at Instacart was to redesign elements of our inventory management tool for retailers (e.g. Kroger, Publix, Safeway, Costco, etc.). As I worked on the project more and more, I learned that Enterprise tools are full of gnarly complexity and often present opportunities to practice deep thought. As Jonathan, one of our current enterprise platform designers said —

The greater the complexity, the greater the opportunity to find elegance.

New login screen

As we scoped the project we found that the existing product wasn’t enabling retailers to manage their inventories as concisely and efficiently as they could. We found retailer users were relying on customer support to help carry out smaller tasks. Our goal with the redesign was to build and deliver a better experience that would enable retailers to manage their inventory more easily and grow their business with Instacart.

The first step in redesigning was to understand the flow of the current product. We mapped out the journey of a partner going through the tool and spoke with the PMs to figure out what we could incorporate into the roadmap.

Overview of the older version of the retailer tool

Once we had a good understanding of the lay of the land, engineering resources, and retailers’ needs, we got into the weeds. Here are a few improvements we made to the tool —

Aisle and department management for Retailers

We used the department tiles feature from our customer-facing product as the catalog’s landing page (1.0 above). With this, we worked to:

  • Refine our visual style
  • Present retailers with an actionable page on the get-go
  • Make it quick and easy to add, delete, and modify items
New Departments page for the Partner Tool. Responsive tiles allow partners to view and edit their Aisles and Departments quickly.

Establishing Overall Hierarchy

Older item search page
Beverages > Coffee returns a list of coffees from the retailer’s catalog

Our solution simplified a few things:

  • A search bar rests atop the product to help find and add items without having to be on this specific page. It pops up a modal that offers a search and add experience. This was visually prioritized since it’s the most common action taken by retailers
  • Decoupled search flow and “Add new product” flow to streamline the workflows
  • Pagination, which was originally on the top and bottom, is now pinned to the bottom of the page for easy navigation
  • We also rethought the information hierarchy on this page. In the example below, the retailer is in the “Beverages” aisle under the “Coffee” item category, which is on the top left. They are editing or adding the item “Eight O’Clock Coffee,” which is the page title. This title is bigger to anchor the user on the page and improve navigation throughout the platform
Focused view of top bar. The “New Product” button is disabled since this is a view to add products

Achieving Clarity

While it’s great that the older Item Details page was partitioned into sections, from an IA perspective, it offered challenges for two reasons:

  1. The category grouping didn’t make sense to retailers
  2. Retailers had to read the information vertically but digest it horizontally and vertically
Older version of Item Details page

To address this, we broke down the sections into what’s truly necessary. From there, we identified four main categories of information that the data fell under:

  1. Images — This is first to encourage retailers to add product photos
  2. Basic Info — Name, brand, size, and unit
  3. Item description — Below the item description field, we offered the description seen on the original package (where the data was available) to help guide them as they wrote
  4. Product attributes — help better categorize the product (e.g. Kosher)

Sources now pop up on the top right of the input fields so the editor knows who last made changes.


Takeaways

Seeking validation through numbers is always fantastic. We did a small beta launch of this product and saw an increase in weekly engagement and decrease in support requests.

I learned that designing enterprise products helps you extend yourself as a visual designer and deep product thinker. I approached this project as an opportunity to break down complex interactions and bring visual elegance to a product through thoughtful design. To this day, it remains one of my favorite projects at Instacart as it stretched my thinking and enhanced my visual design chops. Most importantly, it taught me to look at Enterprise tools in a new light; now when I look at them, I am able to appreciate the complexity within

Source: https://tech.instacart.com/how-redesigning-an-enterprise-product-taught-me-to-extend-myself-8f83d72ebcdf

6 Biases Holding You Back From Rational Thinking – Robert Greene

Emotions are continually affecting our thought processes and decisions, below the level of our awareness. And the most common emotion of them all is the desire for pleasure and the avoidance of pain. Our thoughts almost inevitably revolve around this desire; we simply recoil from entertaining ideas that are unpleasant or painful to us. We imagine we are looking for the truth, or being realistic, when in fact we are holding on to ideas that bring a release from tension and soothe our egos, make us feel superior. This pleasure principle in thinking is the source of all of our mental biases. If you believe that you are somehow immune to any of the following biases, it is simply an example of the pleasure principle in action. Instead, it is best to search and see how they continually operate inside of you, as well as learn how to identify such irrationality in others.

These biases, by distorting reality, lead to the mistakes and ineffective decisions that plague our lives. Being aware of them, we can begin to counterbalance their effects.

1) Confirmation Bias

I look at the evidence and arrive at my decisions through more or less rational processes.

To hold an idea and convince ourselves we arrived at it rationally, we go in search of evidence to support our view. What could be more objective or scientific? But because of the pleasure principle and its unconscious influence, we manage to find that evidence that confirms what we want to believe. This is known as confirmation bias.

We can see this at work in people’s plans, particularly those with high stakes. A plan is designed to lead to a positive, desired objective. If people considered the possible negative and positive consequences equally, they might find it hard to take any action. Inevitably they veer towards information that confirms the desired positive result, the rosy scenario, without realizing it. We also see this at work when people are supposedly asking for advice. This is the bane of most consultants. In the end, people want to hear their own ideas and preferences confirmed by an expert opinion. They will interpret what you say in light of what they want to hear; and if your advice runs counter to their desires, they will find some way to dismiss your opinion, your so-called expertise. The more powerful the person, the more they are subject to this form of the confirmation bias.

When investigating confirmation bias in the world take a look at theories that seem a little too good to be true. Statistics and studies are trotted out to prove them, which are not very difficult to find, once you are convinced of the rightness of your argument. On the Internet, it is easy to find studies that support both sides of an argument. In general, you should never accept the validity of people’s ideas because they have supplied “evidence.” Instead, examine the evidence yourself in the cold light of day, with as much skepticism as you can muster. Your first impulse should always be to find the evidence that disconfirms your most cherished beliefs and those of others. That is true science.

2) Conviction Bias

I believe in this idea so strongly. It must be true.

We hold on to an idea that is secretly pleasing to us, but deep inside we might have some doubts as to its truth and so we go an extra mile to convince ourselves — to believe in it with great vehemence, and to loudly contradict anyone who challenges us. How can our idea not be true if it brings out of us such energy to defend it, we tell ourselves? This bias is revealed even more clearly in our relationship to leaders — if they express an opinion with heated words and gestures, colorful metaphors and entertaining anecdotes, and a deep well of conviction, it must mean they have examined the idea carefully and therefore express it with such certainty. Those on the other hand who express nuances, whose tone is more hesitant, reveal weakness and self-doubt. They are probably lying, or so we think. This bias makes us prone to salesmen and demagogues who display conviction as a way to convince and deceive. They know that people are hungry for entertainment, so they cloak their half-truths with dramatic effects.

3) Appearance Bias

I understand the people I deal with; I see them just as they are.

We do not see people as they are, but as they appear to us. And these appearances are usually misleading. First, people have trained themselves in social situations to present the front that is appropriate and that will be judged positively. They seem to be in favor of the noblest causes, always presenting themselves as hardworking and conscientious. We take these masks for reality. Second, we are prone to fall for the halo effect — when we see certain negative or positive qualities in a person (social awkwardness, intelligence), other positive or negative qualities are implied that fit with this. People who are good looking generally seem more trustworthy, particularly politicians. If a person is successful, we imagine they are probably also ethical, conscientious and deserving of their good fortune. This obscures the fact that many people who get ahead have done so by doing less than moral actions, which they cleverly disguise from view.

4) The Group Bias

My ideas are my own. I do not listen to the group. I am not a conformist.

We are social animals by nature. The feeling of isolation, of difference from the group, is depressing and terrifying. We experience tremendous relief to find others who think the same way as we do. In fact, we are motivated to take up ideas and opinions because they bring us this relief. We are unaware of this pull and so imagine we have come to certain ideas completely on our own. Look at people that support one party or the other, one ideology — a noticeable orthodoxy or correctness prevails, without anyone saying anything or applying overt pressure. If someone is on the right or the left, their opinions will almost always follow the same direction on dozens of issues, as if by magic, and yet few would ever admit this influence on their thought patterns.

5) The Blame Bias

I learn from my experience and mistakes.

Mistakes and failures elicit the need to explain. We want to learn the lesson and not repeat the experience. But in truth, we do not like to look too closely at what we did; our introspection is limited. Our natural response is to blame others, circumstances, or a momentary lapse of judgment. The reason for this bias is that it is often too painful to look at our mistakes. It calls into question our feelings of superiority. It pokes at our ego. We go through the motions, pretending to reflect on what we did. But with the passage of time, the pleasure principle rises and we forget what small part in the mistake we ascribed to ourselves. Desire and emotion will blind us yet again, and we will repeat exactly the same mistake and go through the same mild recriminating process, followed by forgetfulness, until we die. If people truly learned from their experience, we would find few mistakes in the world, and career paths that ascend ever upward.

6) Superiority Bias

I’m different. I’m more rational than others, more ethical as well.

Few would say this to people in conversation. It sounds arrogant. But in numerous opinion polls and studies, when asked to compare themselves to others, people generally express a variation of this. It’s the equivalent of an optical illusion — we cannot seem to see our faults and irrationalities, only those of others. So, for instance, we’ll easily believe that those in the other political party do not come to their opinions based on rational principles, but those on our side have done so. On the ethical front, few will ever admit that they have resorted to deception or manipulation in their work, or have been clever and strategic in their career advancement. Everything they’ve got, or so they think, comes from natural talent and hard work. But with other people, we are quick to ascribe to them all kinds of Machiavellian tactics. This allows us to justify whatever we do, no matter the results.

We feel a tremendous pull to imagine ourselves as rational, decent, and ethical. These are qualities highly promoted in the culture. To show signs otherwise is to risk great disapproval. If all of this were true — if people were rational and morally superior — the world would be suffused with goodness and peace. We know, however, the reality, and so some people, perhaps all of us, are merely deceiving ourselves. Rationality and ethical qualities must be achieved through awareness and effort. They do not come naturally. They come through a maturation process.

Source : https://medium.com/the-mission/6-biases-holding-you-back-from-rational-thinking-f2eddd35fd0f

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