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AI wave stimulates big tech spending and strong profits, but for how long?
Big tech companies have made it clear over the last week that they have no intention of slowing down their stunning levels of spending on artificial intelligence (AI), even though investors are getting worried that a big payoff is further down the line than most believe.
In the last quarter, Apple, Amazon, Meta, Microsoft and Google’s parent company Alphabet spent a combined $59 billion on capital expenses, 63% more than a year earlier and 161 percent more than four years ago. A large part of that was funneled into building data centers and packing them with new computer systems to build artificial intelligence. Only Apple has not dramatically increased spending, because it does not build the most advanced AI systems and is not a cloud service provider like the others.
At the beginning of this year, Meta said it would spend more than $30 billion in 2024 on new tech infrastructure. In April, he raised that to $35 billion. On Wednesday, he increased it to at least $37 billion. CEO Mark Zuckerberg said Meta would spend even more next year. He said he’d rather build too fast “rather than too late,” and allow his competitors to get a big lead in the A.I. race. Meta gives away the advanced A.I. systems it develops, but Mr. Zuckerberg still said it was worth it. “Part of what’s important about A.I. is that it can be used to improve all of our products in almost every way,” he said.
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This new wave of Generative A.I. is incredibly expensive. The systems work with vast amounts of data and require sophisticated computer chips and new data centers to develop the technology and serve it to customers. The companies are seeing some sales from their A.I. work, but it is barely moving the needle financially.
In recent months, several high-profile tech industry watchers, including Goldman Sachs’s head of equity research and a partner at the venture firm Sequoia Capital, have questioned when or if A.I. will ever produce enough benefit to bring in the sales needed to cover its staggering costs. It is not clear that AI will come close to having the same impact as the internet or mobile phones, Goldman’s Jim Covello wrote in a June report.
“What $1 trillion problem will AI solve?” he wrote. “Replacing low wage jobs with tremendously costly technology is basically the polar opposite of the prior technology transitions I’ve witnessed in my 30 years of closely following the tech industry.” “The reality right now is that while we’re investing a significant amount in the AI.space and in infrastructure, we would like to have more capacity than we already have today,” said Andy Jassy, Amazon’s chief executive. “I mean, we have a lot of demand right now.”
That means buying land, building data centers and all the computers, chips and gear that go into them. Amazon executives put a positive spin on all that spending. “We use that to drive revenue and free cash flow for the next decade and beyond,” said Brian Olsavsky, the company’s finance chief.
There are plenty of signs the boom will persist. In mid-July, Taiwan Semiconductor Manufacturing Company, which makes most of the in-demand chips designed by Nvidia (the ONLY tech company that is now making money from AI – much more below) that are used in AI systems, said those chips would be in scarce supply until the end of 2025.
Mr. Zuckerberg said AI’s potential is super exciting. “It’s why there are all the jokes about how all the tech C.E.O.s get on these earnings calls and just talk about A.I. the whole time.”
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Big tech profits and revenue continue to grow, but will massive spending produce a good ROI?
Last week’s Q2-2024 results:
- Google parent Alphabet reported $24 billion net profit on $85 billion revenue.
- Microsoft reported $22 billion net profit on $65 billion revenue.
- Meta reported $13.5 billion net profit on $39 billion revenue.
- Apple reported $21 billion net profit on $86 billion revenue.
- Amazon reported $13.5 billion net profit on $148 billion revenue.
This chart sums it all up:
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References:
https://www.nytimes.com/2024/08/02/technology/tech-companies-ai-spending.html
https://www.axios.com/2024/08/02/google-microsoft-meta-ai-earnings
https://www.nvidia.com/en-us/data-center/grace-hopper-superchip/
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Amdocs and NVIDIA to Accelerate Adoption of Generative AI for $1.7 Trillion Telecom Industry
Amdocs and NVIDIA today announced they are collaborating to optimize large language models (LLMs) to speed adoption of generative AI applications and services across the $1.7 trillion telecommunications and media industries.(1)
Amdocs and NVIDIA will customize enterprise-grade LLMs running on NVIDIA accelerated computing as part of the Amdocs amAIz framework. The collaboration will empower communications service providers to efficiently deploy generative AI use cases across their businesses, from customer experiences to network provisioning.
Amdocs will use NVIDIA DGX Cloud AI supercomputing and NVIDIA AI Enterprise software to support flexible adoption strategies and help ensure service providers can simply and safely use generative AI applications.
Aligned with the Amdocs strategy of advancing generative AI use cases across the industry, the collaboration with NVIDIA builds on the previously announced Amdocs-Microsoft partnership. Service providers and media companies can adopt these applications in secure and trusted environments, including on premises and in the cloud.
With these new capabilities — including the NVIDIA NeMo framework for custom LLM development and guardrail features — service providers can benefit from enhanced performance, optimized resource utilization and flexible scalability to support emerging and future needs.
“NVIDIA and Amdocs are partnering to bring a unique platform and unmatched value proposition to customers,” said Shuky Sheffer, Amdocs Management Limited president and CEO. “By combining NVIDIA’s cutting-edge AI infrastructure, software and ecosystem and Amdocs’ industry-first amAlz AI framework, we believe that we have an unmatched offering that is both future-ready and value-additive for our customers.”
“Across a broad range of industries, enterprises are looking for the fastest, safest path to apply generative AI to boost productivity,” said Jensen Huang, founder and CEO of NVIDIA. “Our collaboration with Amdocs will help telco service providers automate personalized assistants, service ticket routing and other use cases for their billions of customers, and help the telcos analyze and optimize their operations.”
Amdocs counts more than 350 of the world’s leading telecom and media companies as customers, including 27 of the world’s top 30 service providers.(2) With more than 1.7 billion daily digital journeys, Amdocs platforms impact more than 3 billion people around the world.
NVIDIA and Amdocs are exploring a number of generative AI use cases to simplify and improve operations by providing secure, cost-effective and high-performance generative AI capabilities.
Initial use cases span customer care, including accelerating customer inquiry resolution by drawing information from across company data. On the network operations side, the companies are exploring how to proactively generate solutions that aid configuration, coverage or performance issues as they arise.
(1) Source: IDC, OMDIA, Factset analyses of Telecom 2022-2023 revenue.
(2) Source: OMDIA 2022 revenue estimates, excludes China.
Editor’s Note:
- Language models: These models, like OpenAI’s GPT-3, generate human-like text. One of the most popular examples of language-based generative models are called large language models (LLMs).
- Large language models are being leveraged for a wide variety of tasks, including essay generation, code development, translation, and even understanding genetic sequences.
- Generative adversarial networks (GANs): These models use two neural networks, a generator, and a discriminator.
- Unimodal models: These models only accept one data input format.
- Multimodal models: These models accept multiple types of inputs and prompts. For example, GPT-4 can accept both text and images as inputs.
- Variational autoencoders (VAEs): These deep learning architectures are frequently used to build generative AI models.
- Foundation models: These models generate output from one or more inputs (prompts) in the form of human language instructions.
https://www.nvidia.com/en-us/glossary/data-science/generative-ai/
https://blogs.nvidia.com/blog/2023/01/26/what-are-large-language-models-used-for/
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