In combination with semiconductor chips, captive data sets are now the global economy’s “new oil.”  And software companies that sell subscriptions or targeted advertising are suddenly recognizing the potential to create additional revenue streams from allowing third-party models to utilize their vast data caches.  What’s not clear is whether or not the use of these data sets, and the revenue generated from them, are temporary, based solely on the immediate need for training models, or whether they are recurring, as they are utilized for inference and retraining over time.  The efficacy of this new profit driver and the ability of this novel model to withstand regulatory and privacy concerns will determine the ultimate impact on public and private valuations.

An eye-opener for some.

Reddit’s disclosures, two weeks ago in the midst of going public, were an eye opener for some, including the FTC, who is now investigating (Reddit Says the FTC Has Asked Its AI Data-Licensing Program – Barron’s).  In its public filings and comments, Reddit indicated that it expects to generate $200 million in data-licensing revenue over the next three years, including $60 million / year from Google, as its unique users’ attributes are utilized to train AI models.  More deals across the industry seem imminent.  Many speculate that if TikTok wasn’t under so much scrutiny, it would have already cut an AI deal.  According to biographer Walter Isaacson, part of Elon Musk’s eventual rationalization for his purchase of Twitter (now X) was a data monetization play.  Like Reddit, the X platform also cut off access to its free application programming interface (API) last year.

Big data consumers tend to be drawn to user-generated content platforms. 

Why? First, to train AI to act and respond more like humans. Secondly, because you don’t have to worry about paying content creators, or even acknowledging them. Until recently, OpenAI contended that it was impossible to use data that didn’t violate copyright laws. However, according to a recent article in Wired, in January a non-profit called Fairly Trained proved that you can – by building a LLM called KL3M that utilizes curated legal, financial, and regulatory documents.

Large language models (LLMs) take it to the next level.

Certainly, data privacy and ownership has been a hot topic over the last few years (about which we have previously written: AI Progress is a Poor Excuse for Seizing the Intellectual Property of Others).  For years, Tesla has been utilizing the video and telemetry data, from roughly 10-20% of its vehicles that subscribe to its Full Self-Driving offering, to build and train its autonomous-driving AI models.  Even Apple, the self-proclaimed poster child for responsible care of user data, benefits from the data gleaned from across the Apple ecosystem to enhance their own products and services (and lately, according to the US Justice Department, to quelch competition).  But large language models (LLMs) potentially take this debate to the next level.  After all, it’s one thing to provide a platform service – free or otherwise – and then allow advertisers to access insights from user data in a controlled environment – ala Google, Facebook, Instagram, Snap, Twitter, TikTok, etc.  It seems like another to outright sell that data outside the ecosystem to third parties for their own purposes.  Heretofore, data largely has been confined within closed systems with some exceptions — Facebook’s Cambridge Analytica scandal, for instance.  But, at the time, everyone seemed to agree that even indirectly selling data to outsiders was wrong.  Now, not so much.

Is it fair that LLM’s insatiable hunger for data is fed for free (data scraping) with no renumeration to the platform from which it was generated?  Probably not.  Ultimately, the question is who should get paid – the user, the platform, or both?  In the end, users must also share in the bounty, right?  Not so far.  It’s actually quite amazing (and perhaps informative) that new revenue models which include rewarding users for their data, either in whole or in part, have not emerged meaningfully across the software landscape. 

Is the dominance of the largest tech players stifling innovation in order to protect their well-accepted business models?  Could Lina Khan’s FTC be onto something?  In part, yes.  It is hard to endorse a collectivist-influenced agenda where wealth or high market share is somehow suspect and every business practice leads to profits. Like finding oil, it’s better to be specific and pick your spots wisely.

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The Validus Concentrated Alpha Strategy and the Destra Multi-Alternative Fund that is sub-advised by Validus invest in TSLA.  TSLA  is also utilized as an underlier in Validus-researched market-linked notes. Validus’ Global Growth strategy invests in Facebook/Instagram  (owned by META). TSLA and Facebook/Instagram are also part of Validus’ Inflection Universe.

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