Warp speed
AI – or machine learning – is not new. Academics have been conducting research in the field since the 1950s. But what has changed is the recent explosion in the amount of data and computing power available, allowing researchers to build far more powerful models than previously thought possible.
It seems likely that the next generation of models that will emerge in the next few years will be hundreds of times better than those today. AI will soon be able to do many things we thought only humans could do and do many things that humans simply cannot do.
Handle with care
AI has a lot more potential than simply taking care of our more mundane tasks. While a sci-fi style scenario of computers turning rogue is implausible, AI tools do need to be managed and used carefully. Viewed, perhaps, like an arrogant intern.
This is not a plug-and-play solution. The difference between ‘tourist’ users and those that have been trained to use AI is profound. Proficiency at prompting is key: much like accessing a database, speaking the appropriate query language is vital to ensure accurate results.
In addition to helping solve many problems, AI is also creating new ones and risks frustrating efforts to forge a more sustainable future. For instance, India is a services-led economy. Its workforce is benefiting significantly from business process outsourcing (BPO) to the country. Yet BPO is a classic use case for AI and automation has the potential to displace jobs. There is a big role for governments here in managing the transition.
No longer just a tech story
From an investment perspective, AI use cases have been relatively narrow and focused on the tech sector (e.g., targeted ads), with the large incumbent tech firms the main winners to date. But open source has allowed companies across many more sectors to make advances, and governments are also investing as they view AI as an engine of growth.
Still in exponential growth phase
Some of the hype around AI is clearly unsustainable and the promises are not credible: exponential growth can’t last forever. However, growth rates are more likely to moderate than reverse, with progress in the field still moving at a rapid pace. The simple reason for this: there are many use cases lining up – identified needs that AI has the potential to solve – when the technology becomes good enough, cheap enough and fast enough.
Investment implications
With AI, financial markets are shooting first and asking questions later. Companies perceived as being at risk of disruption, e.g., those running outsourced call centres, have been derated significantly regardless of their earnings dynamics. In contrast, companies that are helping governments and businesses to implement AI today are seeing a re-rating. There are many companies caught in the middle, at times viewed as victims of disruption and at times viewed as opportunities to tap into the AI theme, with market valuations oscillating accordingly.
Initially, investment gains are likely to be concentrated in the infrastructure unpinning AI e.g., companies selling Graphic processing units (GPUs). Looking further ahead, applications may be the biggest opportunity: there is no Uber equivalent in the AI era…yet. Uber was born from the technology available at the time: a smartphone with location and payment capabilities. That made ride hailing on demand possible. We don’t know who the next big winner will be yet, but companies are already racing to build the killer AI apps of tomorrow.
Note: The views expressed within the summary are from the panelists who participated in the discussion and are not specifically views expressed from Ninety One.
