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SENTIMENT is down on the so-called magnificent seven US tech stocks, but it would be a mistake to believe the AI theme has run its course.
That’s the view of Elise McKay, an investment analyst with Pendal’s Aussie equities team who has just returned from a US tour where she met with dozens of companies.
AI was a topic in almost every meeting, McKay says.
“AI is not a fad,” says McKay.
“Economic wobbles and geo-political uncertainty have contributed to a recent sell-off in the Nasdaq.
“But there’s strong evidence that over the longer term generative AI will have a big impact across the business landscape.”
ChatGPT is the best-known example, but the technology is driving efficiencies in everything from video creation to medical diagnosis and cyber-security.
Equities investors should keep an eye on how companies are investing in AI, as well as which infrastructure suppliers are best placed to take advantage of a fast-evolving market.
“Over the medium term I expect to see company budgets moving from things like administration, sales and marketing to IT,” McKay says.
Researcher IDC estimates IT budgets will grow by 3.5 per cent this year, and 7 per cent in 2024. A proportion of that spend is being reallocated into generative AI solutions.
An October Gartner poll found 55 per cent of organisations were in pilot or production mode with generative AI — up from 19 per cent in April.
Which companies are likely to benefit quickly from reallocation of IT spend into AI?
Look for companies with access to high-quality data as potential winners of early competitive advantage in the AI race.
“Companies with access to data which they can use to train AI models should generate further barriers to entry,” says McKay. “The strong get stronger.”
There is will also be evolving opportunities among infrastructure providers, says McKay
Computer chip maker NVIDIA is regarded as a leading infrastructure winner, because its graphics processing (GP) units are in big demand by data centres needed for AI training.
But it is not the only winner.
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“Not only do we need ‘training models’, but we also need large numbers of ‘inference models’ and the infrastructure required to support them,” says McKay.
Generally, a generative AI model is trained by exposing it to a large amount of data. This model training is resource-intensive and often happens in big, centralised data centres powered by thousands of computer chips. Over the long term, McKay expects this market to become more commoditised.
On the other hand, inferencing makes use of previous training or live data to solve a task.
This process requires less computing power and can take place on the “edge” of a network – closer to applications.
The inference market will be more highly distributed with greater opportunity to value-add, predicts McKay.
“Inference will need to take place away from big data centres at ‘the edge’ in metro data centres, smart phones, cars and the Internet of Things to ensure mass adoption and minimise latency.
“Infrastructure requirements will be wide and varied with no one player controlling the market.
Next week: Elise will share more insights on the AI infrastructure market
Elise is an investment analyst and portfolio manager with Pendal’s Australian equities team. Elise previously worked as an investment analyst for US fund manager Cartica where she covered a variety of emerging market companies.
She has also worked in investment banking and corporate finance at JP Morgan and Ernst & Young.
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