While the long-term opportunities related to AI are significant and far reaching, there are always risks that the hype associated with the underlying technologies subsides before those same technologies are widely adopted.
The news flow surrounding Generative AI and Large-Language Models in the first half of 2023 has been unrelenting and the speed at which the associated acronyms have become a mainstay in seemingly every corporate earnings call, no matter the industry or sector is astounding.
There is clearly a huge amount of upstream AI infrastructure investment taking place, as evidenced by Nvidia’s doubling of revenue in just 12 months, which is almost unheard of for a company of such a size[1].
But it will take time for upstream capital expenditure to be converted into durable company profits for a whole ecosystem. Downstream applications or adoptions of AI, even for the first movers, are not currently expected to contribute materially before the second half of 2024 or the first half of 2025 at the earliest, as CFO Amy Hood said of Microsoft’s AI services and co-pilots such as Azure AI.
Mike Scarpelli of Snowflake explained to analysts at their Q2 earnings call “It’s going to take some time for AI… people are still struggling to get GPUs and there is a time lag between when a chip manufacturer sells the chips to [when] it gets built into the hardware that actually gets deployed on the rack, in a data centre, and actually gets deployed to customers”[2].
Nevertheless, this hasn’t stopped the share prices (and valuations) of a number of downstream players from skyrocketing in the first half of 2023, as investors make bets on what ‘could be’ tomorrow rather than what ‘is’ today.
The challenge for these firms is three-fold: Will the fundamentals ever be able to match their elevated valuations? Once monetisation takes hold, will they be able to recoup their initial capital outlay and turn a profit? And how sustainable will those profits be once the mainstream catches up and the use of AI becomes ubiquitous?
So the expectation that a GPU purchased today will turn a profit tomorrow is a stretch to say the least. And the realities of what the margin of that profit will be are extremely difficult to predict at such an early juncture.
Nevertheless, over the longer run, the potential is undeniable. This is, in part, due to a democratization of future innovations and applications that is made possible thanks to generative AI’s ability to converse in human language.
Technical know-how is no longer a prerequisite for creative ideas or clever use-cases, anyone with a computer and a laptop can now write code, build apps, direct movies or compose a novel. And neither is technical hardware such a barrier to entry for new ideas as, thanks to cloud hosting, AI applications will be able to run on existing devices whether laptops, tablets or smartphones.
Karen Kharmandarian and Alexandre Zilliox are portfolio managers at Thematics AM, an affiliate of Natixis Investment Managers
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[1] Source: https://www.bloomberg.com/news/articles/2023-08-23/nvidia-gives-rosy-outlook-in-sign-ai-spending-remainsinsatiable?
sref=ialMV164
[2] Source: Microsoft & Snowflake corporate earnings calls, July 2023
