Have we already reached peak AI? As an investor in both venture capital and public equities, there are signs pointing to interest in generative AI starting to wane, just a little over 21 months in. (I consider the first viral moment of generative AI to be when tools like Dall-E 2 and Stable Diffusion were launched in summer 2022 and started to capture more public attention.) 

There are a few indications that are pointing to the hype now giving way to exhaustion. Nvidia, a darling of the AI boom, has almost become a new meme stock, with the redditors on r/Nvidia wondering daily when Nividia will hit $200 (to the moon? 🚀). If you have been paying attention, it seems like almost everyday there is a new AI product or feature being touted by an AI influencer on X, or their dedicated AI newsletters, launching yet another game-changing way you will now perform [once boring task]. Sora was the best video generation model, until it wasn’t. (For example, watch this YouTube video titled: OpenAI Shocks the AI Video World – Sora Changes Everything.)

Consider this: since June 2022, Nvidia’s stock price has grown at the ridiculous pace of 730%, minting thousands of paper and real millionaires who were fortunate enough to hold their stock. At one point this summer, Nvidia briefly held the crown as the most valuable company in the world, surpassing tech giants like Apple and Microsoft, with a market capitalization of $3.34 trillion, yes trillion, dollars. Since then, the share price has plateaued, trading in a range between $117 and $135 over the last 2 months. The changing landscape of AI-powered tech companies have led to commentators running out of time to coin new acronyms for the world’s best performing tech stocks, as FAANG has now given way to a group known rather boringly as the Magnificent 7, led of course by Nvidia.

What’s happening now? First, much of the run up in Nvidia’s share price has been fueled by the AI frenzy rather than business fundamentals. Investors flush with cash want to bet on the “next big thing”, and Nvidia’s dominant chip-making position has made it the favorite. Nvidia’s last quarter revenue grew to $26 billion, up 18% from Q4 and up 262% from the same quarter last year. These are crazy numbers – a large, mature electronics company growing at the rate of an early stage tech startup. But let’s consider the commonly used valuation benchmark, the Price-to-Earnings ratio: Apple’s PE ratio is 34x, and Microsoft’s is 37x. Another stock that has benefited from AI is Adobe, who has the huge potential to roll out a multitude of AI products to its professional video editors and photographers. It’s trading at 49x PE. Google and Meta, long the darlings of growth tech, are trading at a meager 27x PE. Guess what Nvidia’s PE is? An eye-popping 68x. OK it’s not quite meme stock territory, but there is clearly a lot more froth than fundamental analysis going on here. (Note: these are trailing PE ratios and not forward looking ones due to I not having access to this data. A forward PE ratio would give a better sense of valuation based on future earnings. But in lieu of that, the trailing ratios still give a sense of the gap and trajectory in valuations).

So has the Nvidia train seemingly run out of steam? I think public market investors are starting to realize that the AI hype is overdone, and wondering just how many real AI businesses being built will stand the test of time. This has an impact on the future demand for Nvidia chips, which will eventually cool down. Let’s take a closer look:

1) On an enterprise and business level, CEOs and decision makers are starting to ask the questions of what investing in genAI products will really do. The conversation around genAI tools has turned from one of FOMO to one of “what can it actually do for me”? Businesses are unlikely to adopt a new product unless it can 1) increase revenue or 2) reduce costs. Therefore, customer service AI agents and automated marketing content creation seem to be the two major use cases that have piqued interest so far. But there hasn’t yet been a needle moving use case at the enterprise level. In both cases, humans are still in the loop because AI makes many errors and need to be checked and monitored. And you’re likely not seeing full budgets being reallocated to pure AI products yet. So there are a lot of demos and experiments taking place, and they are not yet leading to actual implementation or budget commitment. 

2) On a consumer level, users are also starting to drift away. Consumers are notoriously fickle (can you remember the last time you opened Threads – Meta’s X competitor launched last year to much acclaim. And look what happened to once social darling BeReal)? ChatGPT, has had very impressive user growth, but at the end of the day it’s still competing with the likes of Instagram, TikTok, YouTube and all the other things we do in the day for our attention. It’s user growth has apparently stalled. For all the money OpenAI has raised and is pouring into development, all it has to show right now, is still just an app on your phone, and a website you type into your browser. 

Many of the generative AI startups I have met with often show the same spiky high growth and high churn metrics: 

• They start with a launch, and users acquisition is high
• Then after a few months, retention drops off
• There is another campaign and user acquisition grows again
• Then after a few months, retention again drops off.
• Rinse and repeat and you’ll find your Customer Acquisition Cost (CAC) never really goes down.

It’s clear what’s happening here: People are trying out the product, but don’t find the need to keep coming back. Not the way to build a company.

On an anecdotal level, I have used ChatGPT and Writer to help kickstart my blogs. I have experimented with generating videos and images using RunwayML and Dall-E. I have also used AI notetakers like Fireflies and MeetGeek for my meetings. I find my use of generative AI tools such as these to be helpful but in a sometimes frustratingly limited way – I need to refine the prompts, or keep generating versions… usually until I run out of credits! And most of the time I forget to use them until I’m midway through my project. For the meeting tools, I find that the AI note-taker who joins the meetings is somewhat invasive and off-putting, as it doesn’t make the meeting feel very private. What if I want the meeting to be completely private? Should the person using the AI notetaker first ask me for permission?

There is also relatively high friction to learning how to use these new products. This often leads to me getting more value from telling someone that I have tried an AI product, compared to the actual value of using it and being happy with the outcome. None of these fantastic products have yet made it into my daily routine (I check my emails, open X and Instagram on my phone, and still search for things in google – ok I’m seeing these new google AI summaries, but AI search is a whole other discussion point). And that really is the challenge for genAI products – they are unable yet to go from a fad to a fixture in our lives.

Thankfully there are some charts that explain all of this. Gartner’s hype cycle explains the perception by society of new technologies, going from one of excitement and hyped up expectations, to then fatigue and disillusionment, before settling into a stable growth and adoption. In the meantime, tons of companies and startups will fail.

Sometimes new technologies don’t survive beyond that. Take the early stages of web3 (and the crypto bros), where everyone decided to open a crypto account somewhere, and this drove BTC and ETH to crazy highs before we entered this long crypto winter. Or just look at the bumpy start, stop, start “the metaverse” has had. Facebook did change its name to Meta, and let’s not forget the number of Chief Metaverse Officers, that were created within companies, or the many folks who rushed to buy the Apple Vision Pro. Where are they now? High adoption, high churn. We see it in every early part of a new technology cycle. 

There is another smart chart. The technology S-curve gives another perspective to why we are seeing this in this part of AI technology innovation. With all new technologies, we’re likely to see slopes in the curve where there is more friction, or more growth. In the early stages of a technology S-curve, we are likely to see much higher adoption rates of new products due to interest and excitement in the technology. This eventually gives way to more sustainable growth as the technology matures.

So where does that leave us with AI? Are we in the early stages of an S-curve, or will we end up in the same boom-bust cycle we see with earlier technology cycles? Is this really the next wave of the Internet, or another false dawn technology pivot, like Facebook’s infamous “shift to video” moment?

Benedict Evans writes about exactly this in his fantastic piece here. Taking in survey data from Bain and Accenture on the state of AI adoption, he describes it as “what happens when the utopian dreams of AI maximalism meet the messy reality of consumer behaviour and enterprise IT budgets – it takes longer than you think, and it’s complicated”. 

The fundamental challenge facing builders in the next phase of generative AI is to go from hype to sustainable growth. As more money pours in, the technology will get better, much, much better. But for all the technological innovation and performance, it still needs useful and relevant applications for people to adopt them. Every technology shift has that problem – be it smart phones, web3 or virtual reality. 

To survive this, the questions I hope builders in AI will ask are the same questions we would ask any founder building a new product before we make an investment:

1. Is this solving a fundamental pain point for consumers or businesses? 
2. Is this something a need to have vs a good to have?

To this, I will add a third question: Does this solution need to be built using AI?

If the answer is yes for all the above, then there is a high chance the product will have utility and stickiness and survive the AI fatigue that will eventually set in. If not, then it might serve the founder well to re-evaluate. AI models will get faster, smarter, and cheaper, but the products they power, will always need to solve the very fundamental question of “why do we really need this?”. Until they do, that amazing technology and idea will continue to be just that.