Over the past 3 months Nvidia has moved through ranges that some stocks don’t do in years, in some cases decades. Having lost over 35 per cent in the June to August sell off, it quickly bounced over 40 per cent in the preceding 20 days once it hit its August low as we build positions ahead of its results. 

These results delivered Nvidia style numbers with three figure growth on the sales, net profit and earnings lines but this did not appease the market, seeing it fall 22 per cent in a little over 8 days. Which brings us to now – a new 16 per cent drive as Nivida reports it’s struggling to meet demands and that the AI revolution is translating faster than even it expected. 

This got us thinking – Where are we right “Now” in the AU players? Thus, it’s time to dive into the drivers for the Nvidia and Co. AI players. 

Supersonic

As mentioned, Nvidia’s results have been astonishing – and it still has time to do a US$50 billion buyback. It collected the award for becoming the world’s largest company in the shortest timeframe in the post-WWII era, think about that for one second – that’s faster than Amazon, Microsoft, Apple, Google, Shell, BP, ExxonMobil, TV players of the 60s and 70s. 

So the question is how does it keep its speed and trajectory? Well that comes from what some are calling the ‘supersonic’ scalers. These are the players like Google, Amazon, Meta and Microsoft that are the users and providers of the AI revolution. These are the players that have spent hundreds billions thus far on the third digital revolution. 

Let us once again put that into perspective, the amount of spending is (inflation adjusted) the same as what was spent during the 1960’s on mainframe computing and the 1990’s distribution of fibre-optics.  

So we have now seen that level of spending in AI the next step is ‘usage’ and that is the inflection point we find ourselves at. Currently AI is mainly used to train foundational models and chatbots – which is fine but not long-term financially stable. It needs to move into things like productions – that is producing models for corporate clients that forecast, streamline and increase productivity. This is the ‘Grail’

This immediately raises the bigger question for now – can this Grail be achieved?

The Voices

To answer that – let us present some arguments from some of AI’s largest “Voices”

On the AI potential and the possibility of a profound and rapid technological revolution, Sam Altman, CEO of OpenAI, has claimed that AI represents the “biggest, best, and most important of all technology revolutions,” and predicts that AI will become increasingly integrated into all aspects of life. This reflects a belief in AI’s far-reaching influence over time.

The never subtle McKinsey and Co. has projected that generative AI could eventually contribute up to $8 trillion to the global economy annually. This figure underscores the massive economic potential of AI. The huge caveat: McKinsey’s predictions are never real-world tested and inevitably fall flat in the market.

This kind of money is what makes AI so attractive to players in Venture Capital. For the VC watchers out there the one that is catching everyone’s attention is VC accelerator Y Combinator which is fully embracing the technology. Just to put Y Combinator into context, according to Jared Heyman’s Rebel Fund, if anyone had invested in every Y Combinator deal since 2005 (which would have been impossible just to let you know), the average annual return would have been 176%, even after accounting for dilution. 

Furthermore to the VC story – AI has accounted for over 40 per cent of new unicorns (startups valued at $1 billion or more) in the first half of 2024, and 60 per cent of the increase in VC-backed valuations. So far in 2024, U.S. unicorn valuations have grown by $162 billion, largely driven by AI’s rapid expansion, according to Pitchbook data.

So the Voices certainly believe it can be achieved. But is this a good thing?

The Good, the Bad and the Ugly

AI is advancing at such a rapid pace that existing performance benchmarks, such as reading comprehension, image classification and advanced maths, are becoming outdated, necessitating the creation of new standards. This reflects the fast-moving nature of AI progress.

For example, look at the success of AlphaFold, an AI-driven algorithm that accurately predicts protein structures. Some see this as one of the most important achievements in AI’s short history and underscores AI’s transformative impact on science, particularly in fields like biology and healthcare. This is the Good.

Then there is the 165-page paper titled “Situational Awareness” by Aschenbrenner which has predicted that by 2030, AI will achieve superintelligence and create a $1 trillion industry. Also, a positive, but will consume 20 per cent of the U.S. power supply. 

These incredible predictions emphasise the enormous scale of AI and the impact it will have on industry, infrastructure and people.

The latest Google study found that generative AI could significantly improve workforce productivity. The study suggests that roughly 80 per cent of jobs could see at least 10 per cent of tasks completed twice as fast due to AI, which has implications for industries such as call centres, coding, and professional writing. This highlights AI’s capacity to streamline tasks and enhance efficiency across various fields. However it also raises the massive concern around job security, job satisfaction and the socio-economic divide as the majority of those affected by AI ‘productivity’ are in mid to low scales. 

Then we come to Elon Musk’s new AI startup, xAI, which raised $6 billion at a valuation of $24 billion this year. The company is planning to build the world’s largest supercomputer in Tennessee to support AI training and inference. This all sounds economically and financially exciting but it has a darker side. These are the kinds of AI ventures that have seen ‘deep-fake’ creations. For example Musk himself shared a deep-fake video of Vice President Kamala Harris. This is the ugly side of AI and reflects the broader cultural and ethical issues surrounding AI-generated content.

Furthermore – we should always be forecasting both the good and the bad for investment opportunities. These issues are already attracting regulations and compliance responses. How impactful will these be? And will it halt the AI driven share price appreciation? It is a very real and present issue.

Where does this leave us?

The share price future of Nvidia and Co is clearly dependent on the longer-term achievement of the AI revolution. As shown, the supersonic players in technology and venture capital are betting big on AI, with predictions that it will reshape the global economy, industries, and even basic societal structures. 

However, there is still uncertainty about the exact timeline for these changes and how accurately the market is pricing in AI’s potential. The AI ecosystem is moving at breakneck speed, with new developments outpacing benchmarks and productivity gains reshaping jobs, but whether all these projections that range from trillion-dollar economies to superintelligence materialises remains to be seen.

Thus – for now – Nvidia and Co’s recent roller-coaster trading looks set to continue.

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