Ben Bajarin, a technology expert, and Jay Goldberg, a cloud investment commentator, dive deep into the soaring capex of cloud hyperscalers like Google and Amazon. They analyze the link between capex spending and revenue growth, spotlighting the hurdles in monetizing AI investments. The duo discusses the exciting potential of innovations in AI wearables, AR, and autonomous vehicles, while also contemplating the economic impact of these advancements. They emphasize the importance of clear consumer use cases and the deflationary effects on tech costs.
The podcast emphasizes the projected 44% year-over-year growth in capital expenditure by cloud hyperscalers, indicating intense competition for infrastructure investments.
Despite rising CapEx, the hosts express skepticism about the unclear monetization strategies for AI investments and the need for defined consumer use cases.
Deep dives
Skyrocketing CapEx Trends
The podcast details the significant rise in capital expenditure (CapEx) among cloud hyperscalers, projecting a 44% year-over-year growth for 2024. This increase could mean an added $50 billion in spending, with total estimated CapEx reaching nearly $200 billion across major companies like Google, Microsoft, and Amazon. The discussion highlights the competitive advantage that firms are pursuing through these investments in data centers and infrastructure. Notably, Microsoft’s CapEx reportedly surpasses that of TSMC, illustrating the intense competition in the tech space driven by the demand for cloud services.
Connecting CapEx to Revenue Growth
A key concern raised is the lack of clarity on how increased CapEx will translate into revenue growth for these companies. The high costs associated with compute infrastructures such as GPUs present a challenge, leading to questions about the monetization strategies for AI advances. Without clear consumer use cases and actionable plans for revenue generation, there is skepticism about the sustained investments in CapEx. Companies like Meta and Microsoft are recognizing this need for clarity, yet they are still in the early stages of turning significant spending into quantifiable returns.
Consumer Applications and Market Readiness
The podcast mentions that despite the enthusiasm for generative AI technologies, consumer retention on these platforms is not meeting expectations, leading to uncertainty about their long-term viability. Applications currently being developed, such as marketing tools and educational aids, represent only a fraction of the potential uses for AI. The hosts highlight the anticipation for future developments, including generative video and more advanced image processing, but recognize that many of these applications remain in the exploratory phase. As a result, while optimism exists for upcoming innovations, the timeline for widespread adoption remains uncertain.
Cost Deflation and Industry Evolution
The conversation wraps up with a focus on how eventual cost deflation in computing resources may reshape the industry landscape. The hosts discuss the potential for alternative, cheaper solutions to emerge as competition increases, which can lead to significant innovation opportunities. Historical parallels are drawn to past technology cycles, emphasizing the inevitability of oversupply followed by a recalibration of the market. While the effects of increased CapEx spending are immediately favorable, the hosts caution that a subsequent period of excess inventory and price compression could just be around the corner, shaping the future of tech investments.
In this episode, Ben Bajarin and Jay Goldberg discuss the skyrocketing capex by cloud hyper scalers and its implications. They explore the significant growth in capex spending by companies like Google, Microsoft, and Amazon, and the connection between capex and revenue growth. They also discuss the challenges of monetizing AI investments and the need for clear consumer use cases. The conversation touches on the potential for new innovations in AI wearables, AR glasses, autonomous cars, and robotics. They also consider the deflationary effects on costs and the timeline for the development of AI applications.
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