AI's $600B Revenue Gap Bubble

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THE HOTTEST THING IN TECH
The AI Sector’s $600B Revenue Gap Bubble

Two significant reports just stoked fears of an AI Bubble. One was a Goldman Sachs investor note questioning gen AI’s ROI and value. The other was a Sequoia Report highlighting the massive $600 billion hardware spend of the global AI sector. This data, and other factors including AI’s environmental footprint, is giving rise to very real concerns about the sustainability of AI startup valuations and expectations. So how realistic are the fears, and what are some possible solutions to the problem?

Why it Matters

A new Sequoia Report on the AI industry finds that the sector needs to earn ~$600 billion per year to pay for the massive hardware spend required to run its tech. Goldman Sachs has until now been a vocal cheerleader for AI’s potential as a world changing technology and investment opportunity. The report Gen AI: Too much spend, too little benefit? dialled back some of its prior enthusiasm with a dose of realism. It pointed out that outside of a few leading companies like Nvidia, OpenAI, Microsoft, and Google, Generative AI isn’t generating ROI and value.

It is unclear if it ever will. Sequoia Analyst David Cahn believes we could be witnessing the growth of an AI-fueled financial bubble. He points out even optimistic projections for major tech companies' AI revenues fall well short of breaking even. Cahn believes the industry must temper expectations of quick profits or we face a bust and a possible global economic crisis.

Costs of Doing (AI) Business

Nvidia alone earned nearly $47.5b in datacenter hardware revenue last year from AI and HPC. Companies like AWS, Google, Meta, Microsoft, and xAI are investing heavily in their AI infrastructure. Meta "scorched earth" strategy involves acquiring ~350k Nvidia H100 GPUs in 2024, worth ~$30b (larger than the Apollo Moon Mission). xAI’s planned GPU supercomputer clusters in Tesla's gigafactory will also use 350k Nvidia GPUs at similar cost.

On top of that there are significant power grid and environmental costs involved in generating the energy to run the data centers needed.

More Efficient Solutions

One solution is making AI more efficient. Big tech is investing heavily in this. Google DeepMind just published new research introducing JEST - a method that accelerates model training while reducing computing requirements. Microsoft just unveiled its MInference, tech to accelerate its LM processing by up to 90%, reducing costs and energy use. Intel just unveiled the 1st fully integrated optical compute interconnect AI chip. Startups like Groq & Etched are innovating in AI chip design and Baseten is using multiple AI models to offer clients high efficiency and AI scalability.

Another solution is State-Space Models which are effective for sequence modeling to summarize past information to predict future states or outputs. Stanford & CMU’s Cartesia is pioneering this innovative approach. On the energy use front, Intel just lodged a patent to improve AI energy resource management via a ML model that predicts likely energy emissions in processing tasks and prioritizing renewable energy sources.

Will this innovation be enough to prevent an AI crash? Only time will tell…

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Closed Deals:

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Deals to Watch:

Paramount Global - The film and TV giant looks set to merge with David Ellison’s Skydance concluding a months-long auction for the company’s controlling stake.

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