When AI Goes Too Far

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This page provides real life examples of an analysis we refer to as AI Gone Too Far (AITF).

AITF and similar protocols establish a boundary to protect the content and business models of solo practitioners and small businesses as well as inventors, writers, artists, and other creative professionals from overly zealous vendors of AI-enabled technology.

Professionals who create original content have expended significant time, energy, and resources developing their content, frequently going to extended lengths to add personalized aspects and custom elements to ensure the content reflects their unique experiences and perspectives. In addition to time, content creation is not without cost, with typical expenses involving, at a minimum, the purchase of software, applications, storage, hardware, advertising, hosting, and website development and management services. 

It is an unreasonable practice for a brand new AI-enabled business to traverse the Internet, and to thereafter seize someone else's content for the purpose of growing their own business. The misappropriation of content in this manner is especially egregious when an AI vendor seizes the content and converts it into a ChatBox, stripping away the personalized aspects of the content, and deploying the tool for use by the AI vendor's own customers.

Despite the obvious need for regulatory intervention in this area, as of today, there are no formal protocols, procedures, or processes in place to prevent this type of conduct.  As a consequence, independent content creators are being targeted and their unique content is being gobbled up, in a flash.

A high-level overview is that AITF addresses an intersection that involves, on one hand, professional content creators who seek to protect their original content, and on the other, ambitious individuals, businesses, and entities who have banked their futures on the large scale consumption of human data and original content.

Despite the complexity of the issues arising in this space (generally referred to as Open Source), we anticipated this conundrum years ago. Indeed, upon the application of our expertise, which includes long-range risk assessment, particularly  in the area of telecommunications, network engineering, and software development, as soon as we spotted the issue, we designed infrastructure to protect ourselves. Years ago, we realized that protocols would be necessary to protect our technology risk assessment practice, which, similar to the programmers behind the creation of AI, also operates on the extreme far edge of custom R&D.

Face-to-face encounters within this space are rare. On that basis, the collection of data regarding these types of encounters has been challenging. However, we are fortunate to have been recently exposed to a couple of cases that allow us to analyze this issue in greater detail. While we would have preferred an open, transparent, and collaborative approach to resolving these matters, it has become clear to us that collaborative discussion is not possible. On that basis, we will continue to post our experiences publicly with the hope that public discussion will result in Sustainable Solutions.