AI: Syncing AI's $600 Billion+ Investments vs Returns. RTZ #418

...as hard this AI Tech Wave as prior tech waves; but bigger rewards likely despite bigger risks, with time

With AI capex ramping up to hundreds of billions already in this AI Tech Wave, the debate on the ‘If and When’ on ‘Syncing’ the Returns on Investment (ROIs), is getting louder. As I outlined in this Saturday’s AI: RTZ Weekly, the “AI Spend vs Returns Debate Returns”:

“VC and Wall Street debates continue on AI capex spend vs timing of eventual returns. Both Sequoia, Goldman Sachs and others have detailed reports worth going through. Timing of investments vs returns seldom sync in big tech waves. This debate is likely to continue the rest of the year and beyond. Background on this debate from last year here.”

This debate is an evergreen and perennial one across every tech wave of the past. But it’s ramped up faster this time since OpenAI’s ‘ChatGPT’ moment almost two years ago. The GS 30+ page piece on AI this time is also worth perusing for the charts and data, as is Sequoia’s arguments on ‘AI’s $600 Billion Question’ as summarized in the chart above.

I wrote about this debate twice last year, and earlier this year in this AI Tech Wave. The debate goes up and down the AI Tech Stack I’ve summarized briefly in a video before.

It’s a topic that we discussed this week on the ‘Trends with Friends’ podcast with Public/Private Investors Howard Lindzon, JC Parets, and Barry Ritholz (who runs $5 billion in assets). At the 58 minute mark here, we discuss the above reports and the market context. We had a broader discussion a couple of weeks ago on ‘AI’s Exponential Value Growth’.

As Axios summarizes the current debate as well this week in the dramatically titled “Wall Street Fears AI Cash Bonfire”:

“Silicon Valley still has twinkly stars in its eyes over AI — but on Wall Street, analysts are beginning to doubt that revenue from the new technology will support its massive costs any time soon.”

“Why it matters: The U.S. stock market's current highs have been driven in large part by optimism about AI.”

“The big picture: Newly published reports from Goldman Sachs, Barclays, and Sequoia Capital have crunched the numbers on how much has been and will be spent on AI-related infrastructure, and how much extra revenue companies will need to make all that spending worth it.”

• “Nvidia's costly AI chips may be flying off the shelves, but they don't seem likely to pay for themselves in higher corporate revenues any time soon.”

“The big picture: In the best-case scenario from skeptics and cautious optimists, the promise of AI will take much longer to materialize than the current investment frenzy suggests.”

“Between the lines: "Overbuilding things the world doesn't have use for, or is not ready for, typically ends badly," Goldman Sachs head of global equity research Jim Covello warns.”

This is a point I’m familiar with from my days at the head of Internet Equities Research at Goldman Sachs, when the telecom industry raised over a trillion dollars to build out the internet telecom infrastructure. I had similar debates and discussions back then with my heads of GS Equities Research.

In hindsight that Internet/Telecom capex wave was out of sync as the full utilization of that internet backbone, that took a decade or more longer than originally anticipated.

I also discussed this evergreen debate on investment vs returns last fall in “Booms that Rhyme”, on why the AI buildout this time is likely to get used faster than the Internet/Broadband/Cloud/Mobile waves and capex buildouts:

“The telecom infrastructure SUPPLY that got built in the 1990s into the early 2000s was built so far ahead of the eventual DEMAND, that the networks were less than 5% used at the end of that time frame.”

“That is likely NOT going to be repeated this time around because of the need for ‘reinforced learning loops’ for inference cycles driven off every user query in every AI application and service, be it to generate photos, videos, or smart agents saying smart-ass things to their users. That variable use component is a distinct difference to be aware of this time around.”

“The other key difference of course is the ‘extractive’ Data depicted in Box 4 above. The AI Tech Wave is going to need boundless amounts of it going forward, both human and AI generated (synthetic). And contrary to some fears, the supply here as well is likely to be endless. That supply curve and investment to build that out as yet to get started in this wave. But it’s ahead of us as well.”

“And it too will take three to seven years or more to build out.”

I summarized my take on the issue on AI investment vs returns on X/Twitter this week as well following a podcast discussion on the issue (worth watching), between Tech Investors Brad Gerstner and Bill Gurley. Here are five points I emphasized:

“My take: Timing of investments vs returns seldom sync in big tech waves. This AI Tech Wave is no exception especially with unique 5 headwinds:

(1) Unprecedented early Market Fears of AI

(2) Regulatory concerns globally

(3) Multi-year AI GPU/data center/Power/Talent input shortages,

(4) Extended lead times needed for Companies and Enterprises to find ‘Product-Market-Fit’, and

(5) Unique bottleneck to source, secure, and scale new Data sources.”

Despite these headwinds, and the unprecedented sums invested this early in vs any other tech wave, I continue to lean into AI investment returns syncing up with the investments sooner than the current skeptics. This view is particularly driven by my continued conviction in the deep technical secular drivers of AI as it Scales. And the innovations that can be built upon it, further up the AI tech stack chart above. All the way to Box 6. And across most industries.

BUT we WILL have to ‘Wait for it’, as I’ve argued before. The timing of the Sync-up may not be fast as at times anticipated.

I of course understand and acknowledge that the sheer size of the AI capex is notable and disconcerting for many, even with the backdrop of prior tech waves. As Tech Analyst Ben Evans puts it in “The AI Summer” this week (worth reading in full):

“In other words - “These things are the future and will change everything, right now, and they need all this money, and we have all this money.”

“As a lot of people have now pointed out, all of that adds up to a stupefyingly large amount of capex (and a lot of other investment too) being pulled forward for a technology that’s mostly still only in the experimental budgets.”

“That rush means we’ve skipped the slow painful period at the bottom of the S-Curve, where you try to work out what product-market fit looks like, while you build the actual product. The web, and e-commerce, and the iPhone had to go through a painful process of growing and learning to become useful. The App Store wasn’t part of the plan for the iPhone, and Tim Berners-Lee’s original web browser included an editor, because this looked like a better network drive (ask your parents), not a publishing platform. LLMs skipped that part, where you work out what this is and what it’s for, and went straight to ‘it’s for everything!’ before meeting an actual user.”

“Of course, the crazy dreams of the Dotcom bubble really did happen, and the AI maximalists might be right - it may be that LLMs can do the whole thing. LLMs may be able to swallow most or all of existing software, and they may be able to automate vast new classes of task that were never in software before, just by themselves and with whole new layers of product, company and enterprise sales built around them. This might be the first S-Curve in tech history that turns out to be a J-Curve. But not this year.”

I agree with Ben ‘it’s not this year’, and likely not next year as well. But the application for AI technologies in this AI Tech Wave, be they as a ‘product’ or a ‘built-in service’ as he puts it, is likely to be more robust globally across billions of mainstream users.

The investment waves in other waves may not be as synced as many would like today, but for now are the ‘table stakes’ to do AI at Scale. Again, we’ll just have to ‘Wait for it’ to get answers to the ‘If’ and ‘When’ on the payoff returns. Stay tuned.

(NOTE: The discussions here are for information purposes only, and not meant as investment advice at any time. Thanks for joining us here)