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March 28 2013

How crowdfunding and the JOBS Act will shape open source companies

Currently, anyone can crowdfund products, projectscauses, and sometimes debt. Current U.S. Securities and Exchange Commission (SEC) regulations make crowdfunding companies (i.e. selling stocks rather than products on crowdfund platforms) illegal. The only way to sell stocks to the public at large under the current law is through the heavily regulated Initial Public Offering (IPO) process.

The JOBS Act will soon change these rules. This will mean that platforms like Kickstarter will be able to sell shares in companies, assuming those companies follow certain strict rules. This change in finance law will enable open source companies to access capital and dominate the technology industry. This is the dawn of crowdfunded finance, and with it comes the dawn of open source technology everywhere.

The JOBS Act is already law, and it required the SEC to create specific rules by specific deadlines. The SEC is working on the rulemaking, but it has made it clear that given the complexity of this new finance structure, meeting the deadlines is not achievable. No one is happy with the delay but the rules should be done in late 2013 or early 2014.

When those rules are addressed, thousands of open source companies will use this financial instrument to create new types of enterprise open source software, hardware, and bioware. These companies will be comfortably funded by their open source communities. Unlike traditional venture-capital-backed companies, these new companies will narrowly focus on getting the technology right and putting their communities first. Eventually, I think these companies will make most proprietary software companies obsolete.

How are companies like Oracle, Apple, Microsoft, SAS and Cisco able to make so much money in markets that have capable commercial open source competitors? In a word: capital. These companies have access to guaranteed cash flows from locked-in users of their products.

Therefore, venture capital investors are willing to provide startup capital to new business only when they demonstrate the capacity for new lock-in. Investors that start technology companies avoid investments that do not trap their user bases. That means entrenched proprietary players frequently face no serious threats from open source alternatives. The result? Lots of lock-in and lots of customers trapped in long-term relationships with proprietary companies that have little motivation to treat them fairly.

The only real argument against business models that respect software freedom have always been about access to capital. Startups are afraid to release using a FOSS license because that decision limits their access to investment. Early-stage investors love to hear the words “proprietary,” “patent-pending” and “trade secret,” which they mentally translate into “exit strategy.” For these investors, trapping users is a hedge against their inability to evaluate early-stage technology startups. (I am sympathetic; predicting the future is hard.) As a result, most successfully funded technology startups are either proprietary, patented platforms or software-as-a-service (SaaS) platforms.

This is all going to change.

Crowdfunded finance is going to shift the funding of software forever, and it is going to create a new class of tech organization: freedom-first technology companies.

Now, open source projects will be able to seek and find crowds of investors from within their own communities. These companies will have both the traditional advantages of proprietary companies (well-capitalized companies recruit armies of competent programmers and sales forces that can survive long sales cycles) and the advantages of the open source development model (open code review and the ability to integrate the insights of outsiders).

Yesterday, it was a big deal if you could get Intel to invest in your company. Tomorrow, you will seek funding from 500 Intel employees, who are all better qualified to vet your technology startup than 90% of the people in Intel’s investment arm. These crowdfunders are also willing to make a decision to invest in six hours rather than six months.

For this reason, I believe there will be a treasure trove of companies that will soon be born out of open source/libre software/hardware/bioware projects by asking their communities to crowdfund their initial rounds of financing. Large community projects will give birth to one or several different companies that are designed from the ground up to support those projects. GitHub and Thingiverse will become the new hubs for investors. Developers who have demonstrated competence in projects will be rewarded with access to financing that is both cheaper and faster than seed or angel funding.

With this fundamental change in incentive structures, open source projects will have the capital they need to try truly radical approaches to the design of their projects. Currently, open source projects have to choose between begging for capital or living without that capital. Many open source projects choose slow and gradual development not because they prefer it, but because this is what the developers involved can afford to do in their spare time. The Debian and Ubuntu projects are illustrative of the differences in style and result when the same community is “shoestringing it” versus having access to capital. The people running many open source projects know that no angel investor would touch them, so they make slow and steady progress to “good” software releases rather than rapid progress to “amazing” software releases.

These new freedom-first companies will be able to prioritize what is best for their projects and their communities without bearing the wrath of their investors. That’s because their communities are their investors.

This is not going to merely create a class of software that can rival current proprietary software vendors. In a sense, current commercial open source companies are already doing a fine job of that. But those open source companies typically have investors who are similarly desperate for hockey-stick returns. Even they must choose software development strategies that will pay off for investors. This new class of company will prefer technical strategies that will pay off for end users.

That might seem like a small distinction, but this incentive tweak will change everything about how software is made.

The new companies that leverage this funding option will look a lot like Canonical. Canonical is the kind of company you get when the geeks are fully in charge, and you have investors who are very tolerant of long-term risk because they grok the underlying technical problems that sometimes take decades to entirely resolve. Also, the investors probably know what the word “grok” means. But, unfortunately, there are only so many Mark Shuttleworth-types around (one as far as I know, but a guy who can get himself into space can probably be first in line for human cloning, too).

Shuttleworth is famous for reading printouts of the Debian mailing list on vacation as he figured out which Debian developers to hire for Canonical, the new Linux startup he was funding. That kind of behavior is not what most financial analysts do before making an investment, but this, and other similar efforts, allowed Shuttleworth to predict and control the future of a very technical financial opportunity. It is this kind of focus that allowed Shuttleworth to make one great investment and know that it would work, rather than making hundreds of investments hoping that one of them would work. Using the JOBS Act, community members that already sustain that level of research about an open source project can make the same kinds of bets, but with much less money. (It is ironic that so many of the critics of the JOBS Act presume that the crowd is ignorant rather than recognizing the potential for hyper-informed investor communities.)

In addition, companies like Canonical, Rackspace, Google, Amazon, and Red Hat might acquire these new companies. All of these established organizations can afford billion-dollar acquisitions, and they are either entirely open source or they are very open-source friendly. This kind of acquisition potential will ensure that once open source technology companies prove themselves, they will have access to series A and B financing. I also expect there will be several new open source mega-companies that emerge that are even more devoted to community and end-user experience than these current open source leaders.

This new class of company will have lots and lots of hockey sticks and plenty of billion-dollar exits. Companies will achieve these exits precisely because they do not focus on them (it’s very Zen). They will choose and execute visionary technical strategies that no outside investor could understand. These strategies will seem obvious to their communities of users/investors. These companies will be able to move into capitalization as soon as their communities are convinced the technical strategies and execution capabilities are sound. All of this will lead to better, faster, bigger open source stuff.


If you’d like to talk with other people who are interested in getting and giving funding for open source companies, I set up a related mailing list and Twitter account (@WeInvestInUs).

Related:

April 13 2012

Four short links: 13 April 2012

  1. Change the Game (Video) -- Amy Hoy's talk from Webstock '12, on being contrary and being successful. Was one of the standout talks for me.
  2. Rise4Fun -- software engineering tools from Microsoft Research. (via Hacker News)
  3. Why Obama's JOBS Act Couldn't Suck Worse (Rolling Stone) -- get ready for an avalanche of shareholder suits ten years from now, since post-factum civil litigation will be the only real regulation of the startup market.
  4. Socio-economic Return Of FTTH Investment in Sweden (PDF) -- This preliminary study analyses the socio-economic impacts of the investment in FTTH. The goal of the study was: Is it possible to calculate how much a krona (SEK) invested in fibre will give back to society? The conclusion is that a more comprehensive statistical data and more calculations are needed to give an exact estimate. The study, however, provides an indication that 1 SEK invested over four years brings back a minimum of 1.5 SEK in five years time. The study estimates the need for investment to achieve 100% fibre penetration, identifies and quantifies a number of significant effects of fibre deployment, and then calculates the return on investment. (via Donald Clark)

August 31 2011

Why the finance world should care about big data and data science

ABOVE by Lyfetime, on FlickrFinance experts already understand that data has value. It's the lifeblood of their industry, after all. But as O'Reilly director of market research Roger Magoulas notes in the following interview, some in the financial domain may not grasp all that data has to offer. Data science and big data have led to an expansion of data types, Magoulas says, and the associated influx of information could very well shape investment strategies and create new businesses.

How does big data apply to the financial world?

Roger Magoulas: There are two flavors of it. One is analyzing things like your investments, econometrics, trading activity, and longer-term data analysis. That's clearly part and parcel of the finance business, and people in the space already have great familiarity with this side of data.

The second flavor is the integrated approach to data in all facets of how organizations do business. This involves understanding customers, understanding competitors, understanding behavior, taking advantage of the world of sensors, and using a computational and quantitative mindset to make sense of a very confusing world.

Is there a disconnect between the finance world and terms like "data science" and "big data"?

Roger Magoulas: Everyone is struggling with the semantics, so finance isn't worse off than others. They're actually making an effort to understand it. Adding to the semantic confusion, the terms "data science" and "big data" are sometimes co-opted by organizations trying to show how they embody these attributes. That's fine, but the finance ecosystem has a responsibility to learn as much as it can about these areas. The best way to do that is directly from the data science practitioners: see the tools data scientists use and how they approach their work. That firsthand experience will help finance experts inform their investment strategies and see where the data space is heading.

What's the relationship between data science and business intelligence?

Roger Magoulas: My background is in data warehousing, and the front-end access to the data warehouse was known as "business intelligence" in the '90s. These early data warehouses were mostly constructed out of quantitative data from operational systems — things like order entry and customer service systems. "Business intelligence" tools were used to access the mostly well-understood operational data in the data warehouses.

What's changed is that we've had an explosion of data types. For example, no one was doing analysis on search terms back in the '90s because the tools to do that weren't available. Now, we need new terms to help accommodate what analysts do: natural language processing, machine learning, etc. Moreover, the old business intelligence tools were based on operational things, like how many orders a customer placed. They weren't built to tackle these new tasks.

Will data science and big data incrementally improve existing techniques with new tools? Or are we also talking about the creation of whole new industries?

Roger Magoulas: It's going to do both. The analogy might be to when open source software became widely used. While there were open source business models and companies, the real growth of open source came from companies like Google, Yahoo and Amazon that based their core technologies on the open source stacks. There was this two-headed approach that came out of the adoption of open source.

LinkedIn is an example of this two-headed approach. The company is a social network, but it uses data science tools, techniques and processes to build products that make sense of the social network for LinkedIn's clients. Would LinkedIn exist without data science? I think you can imagine a social network that just helps business people connect with each other, but the real monetization part — the thing that helped them go public — came from LinkedIn using the data they capture to identify and build products.

This interview was edited and condensed.

Strata Conference New York 2011, being held Sept. 22-23, covers the latest and best tools and technologies for data science — from gathering, cleaning, analyzing, and storing data to communicating data intelligence effectively.

Save 30% on registration with the code ORM30

Finance sessions at Strata New York

A number of sessions at the three Strata NY events (Sept. 19-23 in New York City) will examine the intersection of finance and data science. Here's a selection:

Thin and Thick Value in a Transparent Environment

Presenter: Umair Haque, Havas Media Lab, HBR

Big data is a necessary part of a transition to an economy that's not just more efficient and productive, but more efficient and productive in 21st century terms. Yet today, we're hyper-connected, but in a relative data vacuum, which leaves us prone to large-scale crises and "too big to fail" thinking. In this session, Harvard's Umair Haque looks at the future of thin and thick value in a data-driven world.

Next Best Action for MBAs

Presenter: James Kobielus, Forrester Research, Inc.

Leading-edge organizations have implemented "next best action" (NBA) technologies, such as big data analytics, within their multichannel customer relationship management programs. In this session, Forrester senior analyst James Kobielus will provide a vision, case studies, ROI metrics, and guidance for business professionals evaluating applications of NBA in their organizations.

Big Data: The Next Frontier

Presenter: Michael Chui, McKinsey Global Institute

McKinsey's influential big data report has helped define and explain the opportunity created by the torrent of data flowing daily through business. Michael Chui outlines the big picture of data innovation, challenges and competitive advantage.

The New Corporate Intelligence

Presenter: Sean Gourley, Quid

What if corporate strategists could literally draw a map to find growth opportunities? A technique called semantic clustering analysis makes this possible. When applied to technology entities worldwide, this analysis can reveal not only which innovation areas are thick with competition, but also where in the market there are opportunities, or "white spaces," ripe for innovation.

Creating a National Data Utility: Dodd-Frank Financial Reforms

Presenters: Donald F. Donahue, The Depository Trust & Clearing Corporation; Paul Sforza, U.S. Department of the Treasury

Donahue and Sforza will discuss America's first public financial services data utility. This project is being incorporated into the United States' existing information infrastructure to provide consistent, quality data to investors, institutions, and regulators.

Photo: ABOVE by Lyfetime, on Flickr

March 04 2009

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