The Complicated Economy of Open Source Software

Open source software is now at the heart of the tech platforms and services most of us use every day, including Microsoft, whose former CEO Steve Ballmer once famously described Linux and other open source projects as a “Cancer.” These days, Microsoft positions itself as a champion of open source development, as does Google, Facebook, Amazon, IBM, and even the US government.

Free software, if it is mentioned at all, is usually brought up under the umbrella term Free and Open Source Software or FOSS. THE ECONOMICS OF OPEN SOURCE. Although open source software was rapidly embraced by many of the biggest tech companies in Silicon Valley, economists struggled to explain how these projects, which bucked all the conventions of the marketplace, could be so successful.

Titled “The Simple Economics of Open Source,” Lerner and Tirole identified multiple immediate and long term benefits gained by open source developers such that the role of altruism in open source development was reduced to an accidental byproduct.

As for long term benefits, open source development was used to advance a programmer’s career by demonstrating their talents to a prospective employer or venture capitalist, as well as providing a signalling mechanism whereby developers can gain recognition for their technical chops from their peers in open source.

As the developer William Gross described the issue, the rising tide of companies that depend on open source software means that open source developers are deluged with feature requests and issues with the code and many of these companies expect that their improvements and issues should take priority.

So anyone is free to create their own Android operating systems from the open source code, but Google has a policy that forbids using its applications on any non-official Android OS. This policy was justified on the grounds that it would help app developers from having to modify their apps to fit dozens of slightly different versions of Android, but the most noticeable effect is that the open source Android OS has become inextricably bound to proprietary Google products.

In January, GitHub’s open source project manager Devon Zuegel wrote a blog post on the site titled “Let’s talk about open source sustainability.” The post highlighted a number of problems in the open source community, which included inadequate resources and governance, lack of communication, and work overload. Zuegel implored the community to give input to the company on how it could improve these areas for open source maintainers and contributors.

This article was summarized automatically with AI / Article-Σ ™/ BuildR BOT™.

Original link

Decentralised AI has the potential to upend the online economy

Most AI giants on the internet rely on the continuous collection of personal data from their users, primarily to build and maintain machine-learning models.

The repeated delivery of ads by third-party services creates excessive bandwidth and energy usage, something consumers are noticing as ongoing data collection and analysis by background apps slows their internet connection.

As many recent cases have shown, there are now serious privacy concerns from excessive data collection and the resulting exposure from linkages of personal data across different services.

Advertisement In 2019, we will see an alternative to these practices emerging in the form of AI at the edge – machine learning that will take place “Near” the user, on their device or home hub, or at a local data-aggregation point.

While the technology is clearly ahead of the economics, the edge’s ability to correlate across a greater variety of user data that is not readily available to different cloud-based services will give it market advantage.

Training and fitting models to individuals at the edge, such as individual voice or physical-activity recognition, creates superior results, and the economic efficiency of edge solutions will slowly peel off centralised systems, much like the way outsourcing of services peeled off centralised systems in the 90s. Advertisement In 2019, we will see battles for AI solutions oscillating between centralised and decentralised models.

We will find ways of exchanging data created at the edge for new services.

This article was summarized automatically with AI / Article-Σ ™/ BuildR BOT™.

Original link