Interview Synopsis

Machine Learning Industry Dynamics & Data Science Products

  • Public Equity
  • TMT
  • North America

Although the macroeconomic environment is taking its toll on some companies’ spending, interest across the machine learning (ML) and broader AI landscape is showing no sign of waning, a regional VP at New York-headquartered software company told Third Bridge Forum. After outlining the shape of the market, the expert highlighted two areas in which they are seeing accelerating growth and development: data labelling and ML ops.

AI product TAM could reach 500bn by 2030

As companies use more complex technologies, such as image/video recognition or natural language processing (NLP), many of those with large data sets are struggling to label all that data, as it can be very cost and time-intensive. Snorkel is among the companies, we heard, working on automatic labelling. Meanwhile, ML ops focuses on getting models into production and making them usable on a day-to-day basis. This is an area many start-ups are focusing on, the specialist said, and also primarily concerning large models.

On competitive dynamics, the Interview suggested that hyperscalers are challenged because although they offer a wide array of services, these are “never really stitched together in an easy-to-use format” and sometimes important tools are lacking. However, we also heard that hyperscalers, Palantir, and to a lesser extent, Databricks, are best positioned to capitalise on the market for verticalised ML.

There was significant momentum in NLP models last year. The Interview discussed how chatbots are ripe for disruption as well as how the introduction of ChatGPT has “raised the profile” of this area and could “provoke much more interest in regulation than anything had before”. 

In terms of industry TAM, the specialist placed the current market cap for AI products at USD 50bn-100bn – but this could grow by a factor of 10 over the next decade, if not more, we heard.

They also commented on why they believe the first wave of synthetic data will “probably fail” and create challenges that will ultimately shape the future of AI and ML.

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