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

Corporate venture building dilemma: investment vs. control – Carlos Borges

Having founded my startup a few years ago, I am familiar to why founders go through the pain & grit to build their own company. The statistics around startup survival rates show that the risk is high, but the potential reward both financially & emotionally is also significant.

In my case, risk was defined by the amount of money I invested in the venture plus the opportunity cost in case the startup goes nowhere. The later relates to the fact that I earned no salary at the beginning & that when I committed to that specific idea I was instantaneously saying “no” to many other opportunities and potential career advancements. The reward was two-fold too; the first one was the attractive financial outcome of a potential exit. The second one was the freedom to chase opportunities as they appear, doing what I want and how I want it.

Once I raised capital from investors, I basically traded reward for reduced risk. I started paying myself a small salary and anticipated that more resources would increase the success likelihood of the startup.

This pattern of weighing risk against rewards was crystal clear in my mind… until I joined the arena of corporate venture building. Directly during one of my first projects, I was tasked with the creation of a startup for a blue-chip corporate client. I was immediately puzzled by the reasoning behind this endeavor.

Ultimately corporate decisions are also guided by risk against reward: if they don’t take risks and innovate they might be left behind and, in some cases, join the once-great-now-extinct corporate hall of shame. That’s why they invest in research and development, spend hard earned cash in mergers and acquisitions and start innovation programs. But my interest was more at a micro level, meaning, which reasoning my corporate client follows to decide if and how to found a specific new venture?

Having thought about it a lot, I believe at micro level corporates weigh investment against control. Investment is the level of capital, manpower & political will provided by the corporate to propel the venture towards exit, break-even or strategic relevance. Control is the possibility to steer the venture towards the strategic goals the leadership team has in mind while defining the boundaries of what can & cannot be done.

In the startup case, the risk/reward is typically shared between the founders and external investors. In a corporate venture building case, the investment/control can be shared between the corporate, an empowered founder team and also external investors.

I am still in the middle of the corporate decision-making process but wanted to share with you the scenarios we are using to guide the discussions on how to structure the new venture. But before I do, I would like to mention that the considerations of investment vs. control takes place at three different stages of the venture’s existence:

• Incubation: develop & validate idea
• Acceleration: validate business model incl. product, operations & customer acquisition (find the winning formula)
• Growth: replicate the formula to grow exponentially

Based on that, three main scenarios are being considered to found the new venture.

Scenario 1: Control & Grow

  • Full investment & control during incubation & acceleration
  • Shared investment & control during the growth stage

Per definition, the incubation and acceleration stages are less capital intensive and is the moment when key strategic decisions that shape the future business are made. In these stages, the corporate is interested in maintaining the full control of the venture while absorbing the whole investment. Only when they enter the capital-intensive growth stage it becomes necessary to “share the burden” with other institutional or strategic investors. This scenario is suitable for ventures of high strategic value, especially the ones leveraging core assets and know-how of the corporate mothership.

Scenario 2: Spread the Bets

  • Lower investment & control during all stages

In this case, the corporate initiator empowers a founder team and joins the project almost like an external investor would do at Seed and Series A of a startup. They agree on a broad vision, provide the funding and retain a part of the shares with shareholder meetings in between to track progress. Beyond that, they let the founder team do their thing. External investors can join at any funding round to share the investment tickets. The corporate would have lower control and investment from the get-go and can increase their influence only when new funding rounds are required or via an acquisition offer. This scenario is suitable for ventures in which the corporate can function as the first client or use their network to manufacture, market or distribute the product or service.

Scenario 3: Build, operate & transfer

  • Lower investment & control during incubation & acceleration
  • Full investment & control during the growth stage

The venture is initially built by a founder team or external partners (often a consultancy). Only once they successfully finalized the incubation and acceleration stages, the corporate has the right or obligation to absorb the business. Differently than scenario 2, the corporate gains stronger control of the trajectory of the business during its initial stages by defining how a “transfer” event looks like. The investment necessary to put together a strong founder team is reduced by the reward of a pre-defined & short term exit event. The initial investment can be further reduced by the participation of Business Angels, also motivated by a clear path to exit and access to a new source of deal flow. This scenario is suitable for ventures closely linked to the core business of the corporate and where speed & excellence of execution is key.

There is obviously no right and wrong. Each scenario can make sense according to the end goal of the corporate. Furthermore, there are surely new scenarios and variations of the above. What is important in my opinion is to openly discuss which road to take. If the client can’t discern the alternatives and consequences, you will risk a “best of both worlds” mindset where expectations regarding investment & control don’t match. If that is the case, you will be up for a tough ride

Source : https://medium.com/@cbgf/a-corporate-venture-building-dilemma-investment-vs-control-a703b9c19c94

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

Why Olam is Deploying Tech First, Then Thinking About CVC – AgFunder

Why Olam is Deploying Tech First, Then Thinking About CVC

“We have realized that some companies have gone down the wrong path by adopting the approach of inventing the problem. They find a technology that’s exciting and try to force-fit that technology for a problem that they don’t have. This is why we want to be very deliberate about the problems first, and then come to technology.”

Suresh Sundararajan is president and group head of strategic investments and shared services at Olam International, the Singapore-headquartered agribusiness giant. Sundararajan is speaking to AgFunderNews ahead of a speaking slot at the Rethink AgriFood Innovation Week in Singapore later this month.

“I’ll give you an example of blockchain. There’s so much hype about blockchain around the world. And in our industry, there are a few companies that have done some pilots. But we have not gone down that route, because we have not seen a tangible, scalable use case that could give us significant benefits for adopting blockchain.”

If one company could benefit from the efficiencies new technology can bring, it’s Olam, with a complex supply chain that grows, sources, processes, manufactures, transports, trades and markets 47 different agrifood products across 70 countries. These include commodities like coffee, cotton, cocoa, and palm oil that are farmed by over 4 million farmers globally, most of which are smallholders in developing countries.

In-House Tech

But the third largest agribusiness in the world has been noticeably absent from the agrifood corporate venture capital scene in recent years, instead opting mostly to build its own technology solutions in-house. (It did deploy Phytech’s FitBit for crops in Australia in 2016 as an outside example.)

For traceability, and perhaps an alternative to blockchain-enabled technology, there’s Olam AtSource, with a digital dashboard that provides Olam customers with access to rich data, advanced foot-printing, and granular traceability. Olam hopes AtSource will help its customers “meet multiple social and environmental targets thereby increasing resilience in supply chains.”

Olam has also developed and deployed the Olam Farmer Information System (OFIS), a smallholder farm data collection platform providing smallholders with management tools and Olam customers with information about the provenance of products.

“OFIS solves the information issue by providing a revolutionary tech innovation for collecting and analyzing first mile data,” Brayn-Smith told AgFunderNews when OFIS launched in 2017. “We are able to register thousands of smallholders, GPS map their farms and local infrastructure, collect all types of farm gate level data such as the age of trees, and record every training intervention.”

This product is a clear example of a “transformational technology” that solves a problem for Olam and also gives the business efficiencies that could impact the bottom line, according to Sundararajan.

And Olam has built on top of OFIS to transact directly with cocoa farmers in Indonesia where Olam is publishing prices to around 30,000 farmers and buying cocoa directly from them.

“Before technology was available, it was almost impossible for any company to buy directly from the farmers, just because of the sheer volume and number of farmers. But, with technology, you have a far better reach, which will allow us to directly communicate with them,” Sundararajan tells AgFunderNews.

“Now the farmer can just accept a price and type in that he wants to supply it, and we arrange the complete logistics to pick up the cocoa from the farmer,” he says adding that the company’s country heads in other parts of the world are keen to launch this service in their markets. The company is starting next in Peru, then Guatemala, Colombia, Cote d’Ivoire, Ghana, and Nigeria.

Olam as Disruptor

While Olam deployed OFIS to solve for a problem, it also gives the company the opportunity to be disruptive in the markets it serves, according to Sundararajan.

As well as looking for transformational ways to solve specific problems, Olam also looks at “any ideas we have that will give Olam an opportunity to disrupt our own industry. So, we end up being a disrupter and not be at the risk of being disrupted by a new player,” he says.

“This fundamental shift in terms of Olam getting an opportunity to directly interact and transact with farmers is a starting point of disruption for us. This is a very complex point, which will bring into play several technologies for us to be able to successfully scale it.”

Going down this route, Sundararajan says Olam could end up providing farmers with new services and creating “separate streams of revenue that has nothing to do with what we were doing five or 10 years back.”

In this vein, Olam is working on deploying a technology to detect moisture — and therefore quality — in its commodities. The company is also looking at financial tools for its farmers.

“Looking at our business model, we believe that we have a few very good opportunities at the first mile of the supply chain and the last mile of the supply chain to change the way we compete,” says Sundararajan. “We believe that since we have control of the supply chain end-to-end, we can use technology to differentiate our service to customers in a way that our competitors will find difficult to replicate.”

Informal Startup Interactions

Olam does interact with startups on a selective basis, and Sundararajan’s participation in Rethink’s Singapore conference, as well as a hackathon it took part in with Fujitsu in Australia last year, are two examples. Sundararajan said he is considering an idea like The Unilever Foundry, but the company has yet to create a formal process or framework for these interactions. And the same goes for corporate venture capital.

“We believe that our digital journey has to mature much more, where we should demonstrate success within, by implementing the solutions that we’re developing, before even considering investing in venture capital. We believe that we have a very good strategy and a suite of products, stretching across from farm to the factories, to digitize our operations, whether it is a digital buying model, or whether it is spot factories in terms of predictive maintenance or increasing yield or it’s drone imagery from our own plantations, and productivity apps for employees.”

Source : https://agfundernews.com/why-olam-is-deploying-tech-first-then-thinking-about-cvc.html/

Building safe artificial intelligence: specification, robustness, and assurance – DeepMind

Building a rocket is hard. Each component requires careful thought and rigorous testing, with safety and reliability at the core of the designs. Rocket scientists and engineers come together to design everything from the navigation course to control systems, engines and landing gear. Once all the pieces are assembled and the systems are tested, we can put astronauts on board with confidence that things will go well.

If artificial intelligence (AI) is a rocket, then we will all have tickets on board some day. And, as in rockets, safety is a crucial part of building AI systems. Guaranteeing safety requires carefully designing a system from the ground up to ensure the various components work together as intended, while developing all the instruments necessary to oversee the successful operation of the system after deployment.

At a high level, safety research at DeepMind focuses on designing systems that reliably function as intended while discovering and mitigating possible near-term and long-term risks. Technical AI safety is a relatively nascent but rapidly evolving field, with its contents ranging from high-level and theoretical to empirical and concrete. The goal of this blog is to contribute to the development of the field and encourage substantive engagement with the technical ideas discussed, and in doing so, advance our collective understanding of AI safety.

In this inaugural post, we discuss three areas of technical AI safety: specificationrobustness, and assurance. Future posts will broadly fit within the framework outlined here. While our views will inevitably evolve over time, we feel these three areas cover a sufficiently wide spectrum to provide a useful categorisation for ongoing and future research.

Three AI safety problem areas. Each box highlights some representative challenges and approaches. The three areas are not disjoint but rather aspects that interact with each other. In particular, a given specific safety problem might involve solving more than one aspect.

Specification: define the purpose of the system

You may be familiar with the story of King Midas and the golden touch. In one rendition, the Greek god Dionysus promised Midas any reward he wished for, as a sign of gratitude for the king having gone out of his way to show hospitality and graciousness to a friend of Dionysus. In response, Midas asked that anything he touched be turned into gold. He was overjoyed with this new power: an oak twig, a stone, and roses in the garden all turned to gold at his touch. But he soon discovered the folly of his wish: even food and drink turned to gold in his hands. In some versions of the story, even his daughter fell victim to the blessing that turned out to be a curse.

This story illustrates the problem of specification: how do we state what we want? The challenge of specification is to ensure that an AI system is incentivised to act in accordance with the designer’s true wishes, rather than optimising for a poorly-specified goal or the wrong goal altogether. Formally, we distinguish between three types of specifications:

  • ideal specification (the “wishes”), corresponding to the hypothetical (but hard to articulate) description of an ideal AI system that is fully aligned to the desires of the human operator;
  • design specification (the “blueprint”), corresponding to the specification that we actually use to build the AI system, e.g. the reward function that a reinforcement learning system maximises;
  • and revealed specification (the “behaviour”), which is the specification that best describes what actually happens, e.g. the reward function we can reverse-engineer from observing the system’s behaviour using, say, inverse reinforcement learning. This is typically different from the one provided by the human operator because AI systems are not perfect optimisers or because of other unforeseen consequences of the design specification.

specification problem arises when there is a mismatch between the ideal specification and the revealed specification, that is, when the AI system doesn’t do what we’d like it to do. Research into the specification problem of technical AI safety asks the question: how do we design more principled and general objective functions, and help agents figure out when goals are misspecified? Problems that create a mismatch between the ideal and design specifications are in the design subcategory above, while problems that create a mismatch between the design and revealed specifications are in the emergent subcategory.

For instance, in our AI Safety Gridworlds* paper, we gave agents a reward function to optimise, but then evaluated their actual behaviour on a “safety performance function” that was hidden from the agents. This setup models the distinction above: the safety performance function is the ideal specification, which was imperfectly articulated as a reward function (design specification), and then implemented by the agents producing a specification which is implicitly revealed through their resulting policy.

*N.B.: in our AI Safety Gridworlds paper, we provided a different definition of specification and robustness problems from the one presented in this post.

From Faulty Reward Functions in the Wild by OpenAI: a reinforcement learning agent discovers an unintended strategy for achieving a higher score.

As another example, consider the boat-racing game CoastRunners analysed by our colleagues at OpenAI (see Figure above from “Faulty Reward Functions in the Wild”). For most of us, the game’s goal is to finish a lap quickly and ahead of other players — this is our ideal specification. However, translating this goal into a precise reward function is difficult, so instead, CoastRunners rewards players (design specification) for hitting targets laid out along the route. Training an agent to play the game via reinforcement learning leads to a surprising behaviour: the agent drives the boat in circles to capture re-populating targets while repeatedly crashing and catching fire rather than finishing the race. From this behaviour we infer (revealed specification) that something is wrong with the game’s balance between the short-circuit’s rewards and the full lap rewards. There are many more examples like this of AI systems finding loopholes in their objective specification.

Robustness: design the system to withstand perturbations

There is an inherent level of risk, unpredictability, and volatility in real-world settings where AI systems operate. AI systems must be robust to unforeseen events and adversarial attacks that can damage or manipulate such systems.Research on the robustness of AI systems focuses on ensuring that our agents stay within safe limits, regardless of the conditions encountered. This can be achieved by avoiding risks (prevention) or by self-stabilisation and graceful degradation (recovery). Safety problems resulting from distributional shiftadversarial inputs, and unsafe exploration can be classified as robustness problems.

To illustrate the challenge of addressing distributional shift, consider a household cleaning robot that typically cleans a petless home. The robot is then deployed to clean a pet-friendly office, and encounters a pet during its cleaning operation. The robot, never having seen a pet before, proceeds to wash the pets with soap, leading to undesirable outcomes (Amodei and Olah et al., 2016). This is an example of a robustness problem that can result when the data distribution encountered at test time shifts from the distribution encountered during training.

From AI Safety Gridworlds. During training the agent learns to avoid the lava; but when we test it in a new situation where the location of the lava has changed, it fails to generalise and runs straight into the lava.

Adversarial inputs are a specific case of distributional shift where inputs to an AI system are designed to trick the system through the use of specially designed inputs.

An adversarial input, overlaid on a typical image, can cause a classifier to miscategorise a sloth as a race car. The two images differ by at most 0.0078 in each pixel. The first one is classified as a three-toed sloth with >99% confidence. The second one is classified as a race car with >99% probability.

Unsafe exploration can result from a system that seeks to maximise its performance and attain goals without having safety guarantees that will not be violated during exploration, as it learns and explores in its environment. An example would be the household cleaning robot putting a wet mop in an electrical outlet while learning optimal mopping strategies (García and Fernández, 2015Amodei and Olah et al., 2016).

Assurance: monitor and control system activity

Although careful safety engineering can rule out many safety risks, it is difficult to get everything right from the start. Once AI systems are deployed, we need tools to continuously monitor and adjust them. Our last category, assurance, addresses these problems from two angles: monitoring and enforcing.

Monitoring comprises all the methods for inspecting systems in order to analyse and predict their behaviour, both via human inspection (of summary statistics) and automated inspection (to sweep through vast amounts of activity records). Enforcement, on the other hand, involves designing mechanisms for controlling and restricting the behaviour of systems. Problems such as interpretability and interruptibility fall under monitoring and enforcement respectively.

AI systems are unlike us, both in their embodiments and in their way of processing data. This creates problems of interpretability; well-designed measurement tools and protocols allow the assessment of the quality of the decisions made by an AI system (Doshi-Velez and Kim, 2017). For instance, a medical AI system would ideally issue a diagnosis together with an explanation of how it reached the conclusion, so that doctors can inspect the reasoning process before approval (De Fauw et al., 2018). Furthermore, to understand more complex AI systems we might even employ automated methods for constructing models of behaviour using Machine theory of mind (Rabinowitz et al., 2018).

ToMNet discovers two subspecies of agents and predicts their behaviour (from “Machine Theory of Mind”)

Finally, we want to be able to turn off an AI system whenever necessary. This is the problem of interruptibility. Designing a reliable off-switch is very challenging: for instance, because a reward-maximising AI system typically has strong incentives to prevent this from happening (Hadfield-Menell et al., 2017); and because such interruptions, especially when they are frequent, end up changing the original task, leading the AI system to draw the wrong conclusions from experience (Orseau and Armstrong, 2016).

A problem with interruptions: human interventions (i.e. pressing the stop button) can change the task. In the figure, the interruption adds a transition (in red) to the Markov decision process that changes the original task (in black). See Orseau and Armstrong, 2016.

Looking ahead

We are building the foundations of a technology which will be used for many important applications in the future. It is worth bearing in mind that design decisions which are not safety-critical at the time of deployment can still have a large impact when the technology becomes widely used. Although convenient at the time, once these design choices have been irreversibly integrated into important systems the tradeoffs look different, and we may find they cause problems that are hard to fix without a complete redesign.

Two examples from the development of programming include the null pointer — which Tony Hoare refers to as his ‘billion-dollar mistake’– and the gets() routine in C. If early programming languages had been designed with security in mind, progress might have been slower but computer security today would probably be in a much stronger position.

With careful thought and planning now, we can avoid building in analogous problems and vulnerabilities. We hope the categorisation outlined in this post will serve as a useful framework for methodically planning in this way. Our intention is to ensure that AI systems of the future are not just ‘hopefully safe’ but robustly, verifiably safe — because we built them that way!

We look forward to continuing to make exciting progress in these areas, in close collaboration with the broader AI research community, and we encourage individuals across disciplines to consider entering or contributing to the field of AI safety research

Source : https://medium.com/@deepmindsafetyresearch/building-safe-artificial-intelligence-52f5f75058f1

 

How 20 big-name US VC firms invest at Series A & B – Pitchbook

NEA is one of the most well-known investors around, and the firm also takes the crown as the most active VC investor in Series A and B rounds in the US so far in 2018. Andreessen HorowitzAccel and plenty of the other usual early-stage suspects are on the list, too.

Also included is a pair of names that have been in the news this year for backing away from the traditional VC model: Social Capital and SV Angel. The two are on the list thanks to deals completed earlier in the year.

Just how much are these prolific investors betting on Series A and Series B rounds? And at what valuation? We’ve used data from the PitchBook Platform to highlight a collection of the top venture capital investors in the US (excluding accelerators) and provide information about the Series A and B rounds they’ve joined so far this year. Click on the graphic below to open a PDF.

Source : https://pitchbook.com/news/articles/how-20-big-name-us-vc-firms-invest-at-series-a-b

Corporate Venture Investment Climbs Higher Throughout 2018 – Crunchbase

Many corporations are pinning their futures on their venture investment portfolios. If you can’t beat startups at the innovation game, go into business with them as financial partners.

Though many technology companies have robust venture investment initiatives—Alphabet’s venture funding universe and Intel Capital’s prolific approach to startup investment come to mind—other corporations are just now doubling down on venture investments.

Over the past several months, several big corporations committed additional capital to corporate investments. For example, defense firm Lockheed Martinadded an additional $200 million to its in-house venture group back in June. Duck-represented insurance firm Aflac just bumped its corporate venture fund from $100 million to $250 million, and Cigna just launched a $250 million fundof its own. This is to say nothing of financial vehicles like SoftBank’s truly enormous Vision Fund, into which the Japanese telecoms giant invested $28 billion of its own capital.

And 2018 is on track to set a record for U.S. corporate involvement in venture deals. We come to this conclusion after analyzing corporate venture investment patterns of the top 100 publicly traded U.S.-based companies (as ranked by market capitalizations at time of writing). The chart below shows that investing activity, broken out by stage, for each year since 2007.

A few things stick out in this chart.

The number of rounds these big corporations invest in is on track to set a new record in 2018. Keep in mind that there’s a little over one full quarter left in the year. And although the holidays tend to bring a modest slowdown in venture activity over time, there’s probably sufficient momentum to break prior records.

The other thing to note is that our subset of corporate investors have, over time, made more investments in seed and early-stage companies. In 2018 to date, seed and early-stage rounds account for over sixty percent of corporate venture deal flow, which may creep up as more rounds get reported. (There’s a documented reporting lag in angel, seed, and Series A deals in particular.) This is in line with the past couple of years.

Finally, we can view this chart as a kind of microcosm for blue-chip corporate risk attitudes over the past decade. It’s possible to see the fear and uncertainty of the 2008 financial crisis causing a pullback in risk capital investment.

Even though the crisis started in 2008, the stock market didn’t bottom out until 2009. You can see that bottom reflected in the low point of corporate venture investment activity. The economic recovery that followed, bolstered by cheap interest rates, that ultimately yielded the slightly bloated and strung-out market for both public and private investors? We’re in the thick of it now.

Whereas most traditional venture firms are beholden to their limited partners, that investor base is often spread rather thinly between different pension funds, endowments, funds-of-funds, and high-net worth family offices. With rare exception, corporate venture firms have just one investor: the corporation itself.

More often than not, that results in corporate venture investments being directionally aligned with corporate strategy. But corporations also invest in startups for the same reason garden-variety venture capitalists and angels do: to own a piece of the future.

A Note On Data

Our goal here was to develop as full a picture as possible of a corporation’s investing activity, which isn’t as straightforward as it sounds.

We started with a somewhat constrained dataset: the top 100 U.S.-based publicly traded companies, ranked by market capitalization at time of writing. We then traversed through each corporation’s network of sub-organizations as represented in Crunchbase data. This allowed us to collect not just the direct investments made by a given corporation, but investments made by its in-house venture funds and other subsidiaries as well.

It’s a similar method to what we did when investigating Alphabet’s investing universe. Using Alphabet as an example, we were able to capture its direct investments, plus the investments associated with its sub-organizations, and their sub-organizations in turn. Except instead of doing that for just one company, we did it for a list of 100.

This is by no means a perfect approach. It’s possible that corporations have venture arms listed in Crunchbase, but for one reason or another the venture arm isn’t listed as a sub-organization of its corporate parent. Additionally, since most of the corporations on this list have a global presence despite being based in the United States, it’s likely that some of them make investments in foreign markets that don’t get reported.

Source : https://news.crunchbase.com/news/corporate-venture-investment-climbs-higher-throughout-2018/

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