TikTok* has caused political controversies, made Meta change its Instagram platform to mimic it, and caused many a moral panic. All signs of success.
TikTok’s use of machine learning to present a never-ending stream of engaging content is an example of the successful application of machine learning at a gargantuan scale.
But, as the linked WSJ article shows, TikTok’s growth is driven by massive investments in technology and advertising.
- ByteDance, which owns TikTok, lost more than $7 billion from its operations in 2021 on $61.4b in revenues
- The company spent $27.4b on user acquisition and $14.6b on R&D
I believe that the value of applied machine learning technologies will accrue to those companies that can deploy vast resources to acquire data (in TikTok’s case – users who generate the data) and build massive data and ML infrastructure. I am sure we will see similar revenue and spending trends if we analyze Meta and Google’s results.
While Data Science and Machine Learning careers grab the limelight, making ML platforms more efficient and processing data much cheaper will be more lucrative in the long term.
If a company spends significant cash on ML and data infrastructure, it will always look for people to make things more efficient. Possible careers for the future:
- Data Engineering
- Data center operation and efficiency engineering
- The broad “ML Operations” category