AI
AI Echo Chamber: “Upstream AI” companies huge spending fuels profit growth for “Downstream AI” firms
According to the Wall Street Journal, the AI industry has become an “Echo Chamber,” where huge capital spending by the AI infrastructure and application providers have fueled revenue and profit growth for everyone else. Market research firm Bespoke Investment Group has recently created baskets for “downstream” and “upstream” AI companies.
- The Downstream group involves “AI implementation,” which consist of firms that sell AI development tools, such as the large language models (LLMs) popularized by OpenAI’s ChatGPT since the end of 2022, or run products that can incorporate them. This includes Google/Alphabet, Microsoft, Amazon, Meta Platforms (FB), along with IBM, Adobe and Salesforce.
- Higher up the supply chain (Upstream group), are the “AI infrastructure” providers, which sell AI chips, applications, data centers and training software. The undisputed leader is Nvidia, which has seen its sales triple in a year, but it also includes other semiconductor companies, database developer Oracle and owners of data centers Equinix and Digital Realty.
The Upstream group of companies have posted profit margins that are far above what analysts expected a year ago. In the second quarter, and pending Nvidia’s results on Aug. 28th , Upstream AI members of the S&P 500 are set to have delivered a 50% annual increase in earnings. For the remainder of 2024, they will be increasingly responsible for the profit growth that Wall Street expects from the stock market—even accounting for Intel’s huge problems and restructuring.
It should be noted that the lines between the two groups can be blurry, particularly when it comes to giants such as Amazon, Microsoft and Alphabet, which provide both AI implementation (e.g. LLMs) and infrastructure: Their cloud-computing businesses are responsible for turning these companies into the early winners of the AI craze last year and reported breakneck growth during this latest earnings season. A crucial point is that it is their role as ultimate developers of AI applications that have led them to make super huge capital expenditures, which are responsible for the profit surge in the rest of the ecosystem. So there is a definite trickle down effect where the big tech players AI directed CAPEX is boosting revenue and profits for the companies down the supply chain.
As the path for monetizing this technology gets longer and harder, the benefits seem to be increasingly accruing to companies higher up in the supply chain. Meta Platforms Chief Executive Mark Zuckerberg recently said the company’s coming Llama 4 language model will require 10 times as much computing power to train as its predecessor. Were it not for AI, revenues for semiconductor firms would probably have fallen during the second quarter, rather than rise 18%, according to S&P Global.
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A paper written by researchers from the likes of Cambridge and Oxford uncovered that the large language models (LLMs) behind some of today’s most exciting AI apps may have been trained on “synthetic data” or data generated by other AI. This revelation raises ethical and quality concerns. If an AI model is trained primarily or even partially on synthetic data, it might produce outputs lacking human-generated content’s richness and reliability. It could be a case of the blind leading the blind, with AI models reinforcing the limitations or biases inherent in the synthetic data they were trained on.
In this paper, the team coined the phrase “model collapse,” claiming that training models this way will answer user prompts with low-quality outputs. The idea of “model collapse” suggests a sort of unraveling of the machine’s learning capabilities, where it fails to produce outputs with the informative or nuanced characteristics we expect. This poses a serious question for the future of AI development. If AI is increasingly trained on synthetic data, we risk creating echo chambers of misinformation or low-quality responses, leading to less helpful and potentially even misleading systems.
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In a recent working paper, Massachusetts Institute of Technology (MIT) economist Daron Acemoglu argued that AI’s knack for easy tasks has led to exaggerated predictions of its power to enhance productivity in hard jobs. Also, some of the new tasks created by AI may have negative social value (such as design of algorithms for online manipulation). Indeed, data from the Census Bureau show that only a small percentage of U.S. companies outside of the information and knowledge sectors are looking to make use of AI.
References:
https://deepgram.com/learn/the-ai-echo-chamber-model-collapse-synthetic-data-risks
https://economics.mit.edu/sites/default/files/2024-04/The%20Simple%20Macroeconomics%20of%20AI.pdf
AI wave stimulates big tech spending and strong profits, but for how long?
AI winner Nvidia faces competition with new super chip delayed
SK Telecom and Singtel partner to develop next-generation telco technologies using AI
Telecom and AI Status in the EU
Vodafone: GenAI overhyped, will spend $151M to enhance its chatbot with AI
Data infrastructure software: picks and shovels for AI; Hyperscaler CAPEX
SK Telecom (SKT) and Nokia to work on AI assisted “fiber sensing”
SK Telecom (SKT) and Nokia have agreed to work on artificial intelligence (AI) assisted “fiber sensing,” a wired network technology that employs AI to monitor the environment around optical cables. The two companies signed a memorandum of understanding (see photo below) last Wednesday, with a plan to “accumulate empirical data based on machine learning” from SKT’s commercial network. SKT, South Korea’s largest mobile network carrier, said on Monday that it will utilize Nokia’s product to detect earthquakes, climate changes and other unexpected situations that might arise from nearby construction areas in order to stabilize network conditions. The objective is nationwide deployment in South Korea by the end of this year.
In a joint statement, the companies explained when data runs through an optical cable, the phase of the light can change due to various factors like temperature fluctuations or physical strain on the cable. The changes can be detected and analyzed to provide precise measurements of the environmental conditions affecting the fiber. Using AI-based technology, SKT and Nokia aim to stabilize fiber optic networks in advance by tracking the impact of weather conditions and construction on optical cables. The statement added “fiber sensing” has no distance limitations, unlike some existing wired network monitoring technologies, making it possible to quickly apply the new technology to major backbone networks.
SKT-Nokia monitors wired network status with AI:
– Tracking the impact of weather, earthquakes, construction, etc. on optical cables with ‘fiber sensing’ technology
– Immediately applicable to existing networks and no distance restrictions, making it easy to apply to backbone networks
– Both companies’ capabilities will be combined to quickly internalize new AI-based wired network technology
A signing ceremony for the memorandum of understanding took place at SK Telecom’s headquarters Wednesday in central Seoul. SK Telecom’s Ryu Jung-hwan, head of infrastructure strategy and technology, and John Harrington, Nokia’s senior vice president and head of network infrastructure sales for the Asia-Pacific region, attended the event.
SK Telecom’s Ryu Jung-hwan, head of infrastructure strategy and technology, right, and John Harrington, Nokia’s senior vice president and head of network infrastructure sales for the Asia-Pacific region, pose for a photo after a signing ceremony at SK Telecom’s headquarters in central Seoul on Wednesday, August 7th. Photo Credit: SK TELECOM
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In July, SKT and Singtel announced that they have signed a Memorandum of Understanding (MoU) to collaborate on building next-generation telecommunications networks that will drive innovation, improve network performance and security and deliver enhanced customer experiences over the next two years. The partners will explore the use of artificial intelligence (AI), orchestration tools, and deepen the domain knowledge of network virtualization and other technologies – central to laying the necessary building blocks for progressing to 6G.
References:
https://www.linkedin.com/feed/update/urn:li:activity:7228552138988134402/
AI winner Nvidia faces competition with new super chip delayed
The Clear AI Winner Is: Nvidia!
Strong AI spending should help Nvidia make its own ambitious numbers when it reports earnings at the end of the month (it’s 2Q-2024 ended July 31st). Analysts are expecting nearly $25 billion in data center revenue for the July quarter—about what that business was generating annually a year ago. But the latest results won’t quell the growing concern investors have with the pace of AI spending among the world’s largest tech giants—and how it will eventually pay off.
In March, Nvidia unveiled its Blackwell chip series, succeeding its earlier flagship AI chip, the GH200 Grace Hopper Superchip, which was designed to speed generative AI applications. The NVIDIA GH200 NVL2 fully connects two GH200 Superchips with NVLink, delivering up to 288GB of high-bandwidth memory, 10 terabytes per second (TB/s) of memory bandwidth, and 1.2TB of fast memory. The GH200 NVL2 offers up to 3.5X more GPU memory capacity and 3X more bandwidth than the NVIDIA H100 Tensor Core GPU in a single server for compute- and memory-intensive workloads. The GH200 meanwhile combines an H100 chip [1.] with an Arm CPU and more memory.
Photo Credit: Nvidia
Note 1. The Nvidia H100, sits in a 10.5 inch graphics card which is then bundled together into a server rack alongside dozens of other H100 cards to create one massive data center computer.
This week, Nvidia informed Microsoft and another major cloud service provider of a delay in the production of its most advanced AI chip in the Blackwell series, the Information website said, citing a Microsoft employee and another person with knowledge of the matter.
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Nvidia Competitors Emerge – but are their chips ONLY for internal use?
In addition to AMD, Nvidia has several big tech competitors that are currently not in the merchant market semiconductor business. These include:
- Huawei has developed the Ascend series of chips to rival Nvidia’s AI chips, with the Ascend 910B chip as its main competitor to Nvidia’s A100 GPU chip. Huawei is the second largest cloud services provider in China, just behind Alibaba and ahead of Tencent.
- Microsoft has unveiled an AI chip called the Azure Maia AI Accelerator, optimized for artificial intelligence (AI) tasks and generative AI as well as the Azure Cobalt CPU, an Arm-based processor tailored to run general purpose compute workloads on the Microsoft Cloud.
- Last year, Meta announced it was developing its own AI hardware. This past April, Meta announced its next generation of custom-made processor chips designed for their AI workloads. The latest version significantly improves performance compared to the last generation and helps power their ranking and recommendation ads models on Facebook and Instagram.
- Also in April, Google revealed the details of a new version of its data center AI chips and announced an Arm-based based central processor. Google’s 10 year old Tensor Processing Units (TPUs) are one of the few viable alternatives to the advanced AI chips made by Nvidia, though developers can only access them through Google’s Cloud Platform and not buy them directly.
As demand for generative AI services continues to grow, it’s evident that GPU chips will be the next big battleground for AI supremacy.
References:
AI Frenzy Backgrounder; Review of AI Products and Services from Nvidia, Microsoft, Amazon, Google and Meta; Conclusions
https://www.nvidia.com/en-us/data-center/grace-hopper-superchip/
https://www.theverge.com/2024/2/1/24058186/ai-chips-meta-microsoft-google-nvidia/archives/2
https://news.microsoft.com/source/features/ai/in-house-chips-silicon-to-service-to-meet-ai-demand/
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/
AI Frenzy Backgrounder; Review of AI Products and Services from Nvidia, Microsoft, Amazon, Google and Meta; Conclusions
Microsoft choses Lumen’s fiber based Private Connectivity Fabric℠ to expand Microsoft Cloud network capacity in the AI era
Lumen Technologies and Microsoft Corp. announced a new strategic partnership today. Microsoft has chosen Lumen to expand its network capacity and capability to meet the growing demand on its datacenters due to AI (i.e. huge processing required for Large Language Models, including data collection, preprocessing, training, and evaluation). Datacenters have become critical infrastructure that power the compute capabilities for the millions of people and organizations who rely on and trust the Microsoft Cloud.
Microsoft claims they are playing a leading role in ushering in the era of AI, offering tools and platforms like Azure OpenAI Service, Microsoft Copilot and others to help people be more creative, more productive and to help solve some of humanity’s biggest challenges. As Microsoft continues to evolve and scale its ecosystem, it is turning to Lumen as a strategic supplier for its network infrastructure needs and is investing with Lumen to support its next generation of applications for Microsoft platform customers worldwide.
Lumen’s Private Connectivity Fabric℠ is a custom network that includes dedicated access to existing fiber in the Lumen network, the installation of new fiber on existing and new routes, and the use of Lumen’s new digital services. This AI-ready infrastructure will strengthen the connectivity capabilities between Microsoft’s datacenters by providing the network capacity, performance, stability and speed that customers need as data demands increase.
Art by Midjourney for Fierce Network
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“AI is reshaping our daily lives and fundamentally changing how businesses operate,” said Erin Chapple, corporate vice president of Azure Core Product and Design, Microsoft. “We are focused both on the impact and opportunity for customers relative to AI today, and a generation ahead when it comes to our network infrastructure. Lumen has the network infrastructure and the digital capabilities needed to help support Azure’s mission in creating a reliable and scalable platform that supports the breadth of customer workloads—from general purpose and mission-critical, to cloud-native, high-performance computing, and AI, plus what’s on the horizon. Our work with Lumen is emblematic of our investments in our own cloud infrastructure, which delivers for today and for the long term to empower every person and every organization on the planet to achieve more.”
“We are preparing for a future where AI is the driving force of innovation and growth, and where a powerful network infrastructure is essential for companies to thrive,” said Kate Johnson, president and CEO, Lumen Technologies (a former Microsoft executive). “Microsoft has an ambitious vision for AI and this level of innovation requires a network that can make it reality. Lumen’s expansive network meets this challenge, with unique routes, unmatched coverage, and a digital platform built to give companies the flexibility, access and security they need to create an AI-enabled world.”
Lumen has launched an enterprise-wide transformation to simplify and optimize its operations. By embracing Microsoft’s cloud and AI technology, Lumen can reduce its overall technology costs, remove legacy systems and silos, improve its offerings, and create new solutions for its global customer base. Lumen will migrate and modernize its workloads to Microsoft Azure, use Microsoft Entra solutions to safeguard access and prevent identity attacks and partner with Microsoft to create and deliver new telecom industry-specific solutions. This element alone is expected to improve Lumen’s cash flow by more than $20 million over the next 12 months while also improving the company’s customer experience.
“Azure’s advanced global infrastructure helps customers and partners quickly adapt to changing economic conditions, accelerate technology innovation, and transform their business with AI,” said Chapple. “We are committed to partnering with Lumen to help deliver on their transformation goals, reimagine cloud connectivity and AI synergies, drive business growth, and help customers achieve more.”
This collaboration expands upon the longstanding relationship between Lumen Technologies and Microsoft. The companies have worked together for several years, with Lumen leveraging Copilot to automate routine tasks and reduce employee workloads and enhance Microsoft Teams.
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Lumen’s CMO Ryan Asdourian hinted the deal could be the first in a series of such partnerships, as network infrastructure becomes the next scarce resource in the era of AI. “When the world has talked about what’s needed for AI, you usually hear about power, space and cooling…[these] have been the scarce resources,” Asdourian told Fierce Telecom. Asdourian said Lumen will offer Microsoft access to a combination of new and existing routes in the U.S., and will overpull fiber where necessary. However, he declined to specify the speeds which will be made available or exactly how many of Microsoft’s data centers it will be connecting.
Microsoft will retain full control over network speeds, routes and redundancy options through Lumen’s freshly launched Private Connectivity Fabric digital interface. “That is not something traditional telecom has allowed,” Asdourian said.
Asdourian added that Lumen isn’t just looking to enable AI, but also incorporate it into its own operations. Indeed, part of its partnership deal with Microsoft involves Lumen’s adoption of Azure cloud and other Microsoft services to streamline its internal and network systems. Asdourian said AI could be used to make routing and switching on its network more intelligent and efficient.
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About Lumen Technologies:
Lumen connects the world. We are igniting business growth by connecting people, data, and applications – quickly, securely, and effortlessly. Everything we do at Lumen takes advantage of our network strength. From metro connectivity to long-haul data transport to our edge cloud, security, and managed service capabilities, we meet our customers’ needs today and as they build for tomorrow. For news and insights visit news.lumen.com, LinkedIn: /lumentechnologies, Twitter: @lumentechco, Facebook: /lumentechnologies, Instagram: @lumentechnologies and YouTube: /lumentechnologies.
About Microsoft:
Microsoft (Nasdaq “MSFT” @microsoft) creates platforms and tools powered by AI to deliver innovative solutions that meet the evolving needs of our customers. The technology company is committed to making AI available broadly and doing so responsibly, with a mission to empower every person and every organization on the planet to achieve more.
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References:
https://news.lumen.com/2024-07-24-Microsoft-and-Lumen-Technologies-partner-to-power-the-future-of-AI-and-enable-digital-transformation-to-benefit-hundreds-of-millions-of-customers
https://fierce-network.com/cloud/microsoft-taps-lumens-fiber-network-help-it-meet-ai-demand
AI Frenzy Backgrounder; Review of AI Products and Services from Nvidia, Microsoft, Amazon, Google and Meta; Conclusions
Lumen, Google and Microsoft create ExaSwitch™ – a new on-demand, optical networking ecosystem
ACSI report: AT&T, Lumen and Google Fiber top ranked in fiber network customer satisfaction
Lumen to provide mission-critical communications services to the U.S. Department of Defense
Dell’Oro: Optical Transport market to hit $17B by 2027; Lumen Technologies 400G wavelength market
Vodafone: GenAI overhyped, will spend $151M to enhance its chatbot with AI
GenAI is probably the most “overhyped” technology for many years in the telecom industry, said Vodafone Group’s chief technology officer (CTO) Scott Petty at a press briefing this week. “Hopefully, we are reaching the peak of those inflated expectations, because we are about to drop into a trough of disillusionment,” he said.
“This industry is moving too quickly,” Petty explained. “The evolution of particularly GPUs and the infrastructure means that by the time you’d actually bought them and got them installed you’d be N minus one or N minus two in terms of the technology, and you’d be spending a lot of effort and resource just trying to run the infrastructure and the LLMs that sit around that.”
Partnerships with hyper-scalers remain Vodafone’s preference, he said. Earlier this year, Vodafone and Microsoft signed a 10-year strategic agreement to use Microsoft GenAI in Vodafone’s network.
Vodafone is planning to invest some €140 million ($151 million) in artificial intelligence (AI) systems this year to improve the handling of customer inquiries, the company said on July 4th. Vodafone said it is investing in advanced AI from Microsoft and OpenAI to improve its chatbot, dubbed TOBi, so that it can respond faster and resolve customer issues more effectively.
The chatbot was introduced into Vodafone’s customer service five years ago and is equipped with the real voice of a Vodafone employee.
The new system, which is called SuperTOBi in many countries, has already been introduced in Italy and Portugal and will be rolled out in Germany and Turkey later this month with other markets to follow later in the year, Vodafone said in a press release.
According to the company, SuperTOBi “can understand and respond faster to complex customer enquiries better than traditional chatbots.” The new bot will assist customers with various tasks, such as troubleshooting hardware issues and setting up fixed-line routers, the company said.
Vodafone is not about to expose Vodafone’s data to publicly available models like ChatGPT. Nor will the UK based telco create large language models (LLMs) on its own. Instead, a team of 50 data scientists are working on fine-tuning LLMs like Anthropic and Vertex. Vodafone can expose information to those LLMs by dipping into its 24-petabyte data “ocean,” created with Google. Secure containers within public clouds ensure private information is securely cordoned off and unavailable to others.
According to Petty’s estimates, the performance speed of LLMs has improved by a factor of 12 in the last nine months alone, while operational costs have decreased by a factor of six. A telco that invested nine months ago would already have outdated and expensive technology. Petty, moreover, is not the only telco CTO wary of plunging into Nvidia’s GPU chips.
“This is a very weird moment in time where power is very expensive, natural resources are scarce and GPUs are extremely expensive,” said Bruno Zerbib, the CTO of France’s Orange, at the 2024 Mobile World Congress in Barcelona, Spain. “You have to be very careful with your investment because you might buy a GPU product from a famous company right now that has a monopolistic position.”
Petty thinks LLM processing may eventually need to be processed outside hyper-scalers’ facilities. “To really create the performance that we want, we are going to need to push those capabilities further toward the edge of the network,” he said. “It is not going to be the hype cycle of the back end of 2024. But in 2025 and 2026, you’ll start to see those applications and capabilities being deployed at speed.”
“The time it takes for that data to get up and back will dictate whether you’re happy as a consumer to use that interface as your primary interface, and the investment in latency is going to be critically important,” said Petty. “We’re fortunate that 5G standalone drives low latency capability, but it’s not deployed at scale. We don’t have ubiquitous coverage. We need to make sure that those things are available to enable those applications.”
Data from Ericsson supports that view, showing that 5G population coverage is just 70% across Europe, compared with 90% in North America and 95% in China. The figure for midband spectrum – considered a 5G sweet spot that combines decent coverage with high-speed service – is as low as 30% in Europe, against 85% in North America and 95% in China.
Non-standalone (NSA) 5G, which connects a 5G radio access network (RAN) to a 4G core (EPC), is “dominating the market,” said Ericsson.
Vodafone has pledged to spend £11 billion (US$14 billion) on the rollout of a nationwide standalone 5G network in the UK if authorities bless its proposed merger with Three. With more customers, additional spectrum and a bigger footprint, the combined company would be able to generate healthier returns and invest in network improvements, the company said. But a UK merger would not aid the operator in Europe’s four-player markets.
Petty believes a “pay for search” economic model may emerge using GenAI virtual assistants. “This will see an evolution of a two-sided economic model that probably didn’t get in the growth of the Internet in the last 20 years,” but it would not be unlike today’s market for content delivery networks (CDNs).
“Most CDNs are actually paid for by the content distribution companies – the Netflixes, the TV sports – because they want a great experience for their users for the paid content they’ve bought. When it’s free content, maybe the owner of that content is less willing to invest to build out the capabilities in the network.”
Like other industry executives, Petty must hope the debates about net neutrality and fair contribution do not plunge telcos into a long disillusionment trough.
References:
Vodafone CTO: AI will overhaul 5G networks and Internet economics (lightreading.com)
Vodafone UK report touts benefits of 5G SA for Small Biz; cover for proposed merger with Three UK?
Big Tech post strong earnings and revenue growth, but cuts jobs along with Telecom Vendors
Tech companies have been consistently laying off employees since late 2022. As of April 25th, some 266 tech companies have laid off nearly 75,000 workers in 2024, according to the independent layoff-tracking site layoffs.fyi. A total of 262,682 workers in tech lost their jobs in 2023 compared with 164,969 in 2022. The volume of layoffs in 2023 — a total of 1,186 companies — also surpassed 2022, when 1,061 companies in tech laid off workers — and that total was more than in 2020 and 2021 combined.
Big Tech companies Alphabet (Google’s parent), Amazon, Apple, Meta, Microsoft and Netflix, collectively cut nearly 45,500 jobs in their most recent full fiscal year. Since 2020, however, they have added more than 358,500, bringing total headcount to nearly 2,170,000. Excluding Amazon, which accounts for 70% of that figure, job numbers fell by around 29,700 last year but have grown by 131,500 since 2020 (data from earnings reports and SEC filings – see chart below).
- Today, Amazon reported better-than-expected earnings and revenue for the first quarter, driven by growth in advertising and cloud computing. Operating income soared more than 200% in the period to $15.3 billion, far outpacing revenue growth, the latest sign that the company’s cost-cutting measures and focus on efficiency is bolstering its bottom line. AWS accounted for 62% of total operating profit. Net income also more than tripled to $10.4 billion, or 98 cents a share, from $3.17 billion, or 31 cents a share, a year ago. Sales increased 13% from $127.4 billion a year earlier.
- Google parent Alphabet also posted robust profits, with net income in the latest quarter soaring 57% to $23.7 billion while revenue grew 15% in the quarter. That’s despite job cuts of 12,115 and net headcount reduction of ~8,000 in 2023.
- Microsoft last week managed 20% year-over-year growth in third-quarter net income, to around $21.9 billion, on 17% growth in sales, to $61.9 billion. The number of Microsoft employees was unchanged in 2023 from the previous year, despite the company laying off 11,158 employees. Future headcount reductions may be necessary to help pay for Microsoft’s multi-billion-dollar splurge on AI and the data centers needed to train the Large Language Models and associated generative AI technology. But few expect job cuts to slow Microsoft down.
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As expected, telecom vendors, which have many fewer employees, than Big Tech had a higher percentage of job reductions. CommScope, Corning, Dell, Ericsson, and Nokia, suppliers to some of the world’s biggest telcos, shed nearly 36,500 jobs last year as large IT customers spent less on new equipment.
The following table shows the total number of jobs per year for many vendors/cloud service providers.
Source: Light Reading & company reports/SEC filings
Huawei was the exception to the telecom vendor layoff craze (even ZTE reduced its workforce in 2023). Despite U.S. sanctions and a European backlash against the company, Huawei gained 12,000 employees in 2022, giving it a workforce of 207,000 that year. The number was unchanged in 2023, according to its recently published annual report. Restrictions have not been as effective at hindering Huawei’s progress as the U.S. had hoped.
On the semiconductor side, Intel experienced a net workforce reduction of 7,100 jobs. Profits have tanked because of market share losses, a downturn in customer spending on equipment (explained partly by the earlier build-up of inventory that happened after the pandemic) and investments in new foundries designed to challenge the Asian giants of TSMC and Samsung. Big Tech moves to build in-house AI augmented processor chips that can substitute for Intel’s microprocessors are among the problems the company faces. Intel’s profits have collapsed, just as they have at the mobile networks business group of silicon customer Nokia, and it is at risk of displacement by chip rivals in important markets.
These big tech layoffs are a peculiar outlier in an otherwise strong employment environment: The unemployment rate has hovered between 3.4% and 3.8% since Feb. 2022, bureau data shows. And quit rates, which reflect a lack of worker confidence, this year are consistently at some of the highest levels in more than 20 years, according to the Federal Reserve Bank of St. Louis.
In summary, Big Tech companies continue to thrive financially, but they are also making strategic adjustments, including job cuts, as they navigate the evolving landscape of technology and generative AI. The emphasis on AI development, large language models, and cloud services remains a key driver for their growth and profitability. Telecom vendors are facing tremendous pain due to continued reduction in telco CAPEX which may persists for many years.
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References:
https://www.nerdwallet.com/article/finance/tech-layoffs
US cable and telecom network operators feel the pain
Bloomberg: Higher borrowing costs hurting indebted wireless companies; industry is 2nd largest source of distressed debt
Telecom layoffs continue unabated as AT&T leads the pack – a growth engine with only 1% YoY growth?
High Tech Layoffs Explained: The End of the Free Money Party
NTT & Yomiuri: ‘Social Order Could Collapse’ in AI Era
From the Wall Street Journal:
Japan’s largest telecommunications company and the country’s biggest newspaper called for speedy legislation to restrain generative artificial intelligence, saying democracy and social order could collapse if AI is left unchecked.
Nippon Telegraph and Telephone, or NTT, and Yomiuri Shimbun Group Holdings made the proposal in an AI manifesto to be released Monday. Combined with a law passed in March by the European Parliament restricting some uses of AI, the manifesto points to rising concern among American allies about the AI programs U.S.-based companies have been at the forefront of developing.
The Japanese companies’ manifesto, while pointing to the potential benefits of generative AI in improving productivity, took a generally skeptical view of the technology. Without giving specifics, it said AI tools have already begun to damage human dignity because the tools are sometimes designed to seize users’ attention without regard to morals or accuracy.
Unless AI is restrained, “in the worst-case scenario, democracy and social order could collapse, resulting in wars,” the manifesto said.
It said Japan should take measures immediately in response, including laws to protect elections and national security from abuse of generative AI.
A global push is under way to regulate AI, with the European Union at the forefront. The EU’s new law calls on makers of the most powerful AI models to put them through safety evaluations and notify regulators of serious incidents. It also is set to ban the use of emotion-recognition AI in schools and workplaces.
The Biden administration is also stepping up oversight, invoking emergency federal powers last October to compel major AI companies to notify the government when developing systems that pose a serious risk to national security. The U.S., U.K. and Japan have each set up government-led AI safety institutes to help develop AI guidelines.
Still, governments of democratic nations are struggling to figure out how to regulate AI-powered speech, such as social-media activity, given constitutional and other protections for free speech.
NTT and Yomiuri said their manifesto was motivated by concern over public discourse. The two companies are among Japan’s most influential in policy. The government still owns about one-third of NTT, formerly the state-controlled phone monopoly.
Yomiuri Shimbun, which has a morning circulation of about six million copies according to industry figures, is Japan’s most widely-read newspaper. Under the late Prime Minister Shinzo Abe and his successors, the newspaper’s conservative editorial line has been influential in pushing the ruling Liberal Democratic Party to expand military spending and deepen the nation’s alliance with the U.S.
The two companies said their executives have been examining the impact of generative AI since last year in a study group guided by Keio University researchers.
The Yomiuri’s news pages and editorials frequently highlight concerns about artificial intelligence. An editorial in December, noting the rush of new AI products coming from U.S. tech companies, said “AI models could teach people how to make weapons or spread discriminatory ideas.” It cited risks from sophisticated fake videos purporting to show politicians speaking.
NTT is active in AI research, and its units offer generative AI products to business customers. In March, it started offering these customers a large-language model it calls “tsuzumi” which is akin to OpenAI’s ChatGPT but is designed to use less computing power and work better in Japanese-language contexts.
An NTT spokesman said the company works with U.S. tech giants and believes generative AI has valuable uses, but he said the company believes the technology has particular risks if it is used maliciously to manipulate public opinion.
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From the Japan News (Yomiuri Shimbun):
Challenges: Humans cannot fully control Generative AI technology
･ While the accuracy of results cannot be fully guaranteed, it is easy for people to use the technology and understand its output. This often leads to situations in which generative AI “lies with confidence” and people are “easily fooled.”
･ Challenges include hallucinations, bias and toxicity, retraining through input data, infringement of rights through data scraping and the difficulty of judging created products.
･ Journalism, research in academia and other sources have provided accurate and valuable information by thoroughly examining what information is correct, allowing them to receive some form of compensation or reward. Such incentives for providing and distributing information have ensured authenticity and trustworthiness may collapse.
A need to respond: Generative AI must be controlled both technologically and legally
･ If generative AI is allowed to go unchecked, trust in society as a whole may be damaged as people grow distrustful of one another and incentives are lost for guaranteeing authenticity and trustworthiness. There is a concern that, in the worst-case scenario, democracy and social order could collapse, resulting in wars.
･ Meanwhile, AI technology itself is already indispensable to society. If AI technology is dismissed as a whole as untrustworthy due to out-of-control generative AI, humanity’s productivity may decline.
･ Based on the points laid out in the following sections, measures must be realized to balance the control and use of generative AI from both technological and institutional perspectives, and to make the technology a suitable tool for society.
Point 1: Confronting the out-of-control relationship between AI and the attention economy
･ Any computer’s basic structure, or architecture, including that of generative AI, positions the individual as the basic unit of user. However, due to computers’ tendency to be overly conscious of individuals, there are such problems as unsound information spaces and damage to individual dignity due to the rise of the attention economy.
･ There are concerns that the unstable nature of generative AI is likely to amplify the above-mentioned problems further. In other words, it cannot be denied that there is a risk of worsening social unrest due to a combination of AI and the attention economy, with the attention economy accelerated by generative AI. To understand such issues properly, it is important to review our views on humanity and society and critically consider what form desirable technology should take.
･ Meanwhile, the out-of-control relationship between AI and the attention economy has already damaged autonomy and dignity, which are essential values that allow individuals in our society to be free. These values must be restored quickly. In doing so, autonomous liberty should not be abandoned, but rather an optimal solution should be sought based on human liberty and dignity, verifying their rationality. In the process, concepts such as information health are expected to be established.
Point 2: Legal restraints to ensure discussion spaces to protect liberty and dignity, the introduction of technology to cope with related issues
･ Ensuring spaces for discussion in which human liberty and dignity are maintained has not only superficial economic value, but also a special value in terms of supporting social stability. The out-of-control relationship between AI and the attention economy is a threat to these values. If generative AI develops further and is left unchecked like it is currently, there is no denying that the distribution of malicious information could drive out good things and cause social unrest.
･ If we continue to be unable to sufficiently regulate generative AI — or if we at least allow the unconditional application of such technology to elections and security — it could cause enormous and irreversible damage as the effects of the technology will not be controllable in society. This implies a need for rigid restrictions by law (hard laws that are enforceable) on the usage of generative AI in these areas.
･ In the area of education, especially compulsory education for those age groups in which students’ ability to make appropriate decisions has not fully matured, careful measures should be taken after considering both the advantages and disadvantages of AI usage.
･ The protection of intellectual property rights — especially copyrights — should be adapted to the times in both institutional and technological aspects to maintain incentives for providing and distributing sound information. In doing so, the protections should be made enforceable in practice, without excessive restrictions to developing and using generative AI.
･ These solutions cannot be maintained by laws alone, but rather, they also require measures such as Originator Profile (OP), which is secured by technology.
Point 2: Legal restraints to ensure discussion spaces to protect liberty and dignity, and the introduction of technology to cope with related issues
･ Ensuring spaces for discussion in which human liberty and dignity are maintained has not only superficial economic value, but also a special value in terms of supporting social stability. The out-of-control relationship between AI and the attention economy is a threat to these values. If generative AI develops further and is left unchecked like it is currently, there is no denying that the distribution of malicious information could drive out good things and cause social unrest.
･ If we continue to be unable to sufficiently regulate generative AI — or if we at least allow the unconditional application of such technology to elections and security — it could cause enormous and irreversible damage as the effects of the technology will not be controllable in society. This implies a need for rigid restrictions by law (hard laws that are enforceable) on the usage of generative AI in these areas.
･ In the area of education, especially compulsory education for those age groups in which students’ ability to make appropriate decisions has not fully matured, careful measures should be taken after considering both the advantages and disadvantages of AI usage.
･ The protection of intellectual property rights — especially copyrights — should be adapted to the times in both institutional and technological aspects to maintain incentives for providing and distributing sound information. In doing so, the protections should be made enforceable in practice, without excessive restrictions to developing and using generative AI.
･ These solutions cannot be maintained by laws alone, but rather, they also require measures such as Originator Profile (OP), which is secured by technology.
Point 3: Establishment of effective governance, including legislation
･ The European Union has been developing data-related laws such as the General Data Protection Regulation, the Digital Services Act and the Digital Markets Act. It has been developing regulations through strategic laws with awareness of the need to both control and promote AI, positioning the Artificial Intelligence Act as part of such efforts.
･ Japan does not have such a strategic and systematic data policy. It is expected to require a long time and involve many obstacles to develop such a policy. Therefore, in the long term, it is necessary to develop a robust, strategic and systematic data policy and, in the short term, individual regulations and effective measures aimed at dealing with AI and attention economy-related problems in the era of generative AI.
･However, it would be difficult to immediately introduce legislation, including individual regulations, for such issues. Without excluding consideration of future legislation, the handling of AI must be strengthened by soft laws — both for data (basic) and generative AI (applied) — that offer a co-regulatory approach that identifies stakeholders. Given the speed of technological innovation and the complexity of value chains, it is expected that an agile framework such as agile governance, rather than governance based on static structures, will be introduced.
･ In risk areas that require special caution (see Point 2), hard laws should be introduced without hesitation.
･ In designing a system, attention should be paid to how effectively it protects the people’s liberty and dignity, as well as to national interests such as industry, based on the impact on Japan of extraterritorial enforcement to the required extent and other countries’ systems.
･ As a possible measure to balance AI use and regulation, a framework should be considered in which the businesses that interact directly with users in the value chain, the middle B in “B2B2X,” where X is the user, reduce and absorb risks when generative AI is used.
･ To create an environment that ensures discussion spaces in which human liberty and dignity are maintained, it is necessary to ensure that there are multiple AIs of various kinds and of equal rank, that they keep each other in check, and that users can refer to them autonomously, so that users do not have to depend on a specific AI. Such moves should be promoted from both institutional and technological perspectives.
Outlook for the Future:
･ Generative AI is a technology that cannot be fully controlled by humanity. However, it is set to enter an innovation phase (changes accompanying social diffusion).
･ In particular, measures to ensure a healthy space for discussion, which constitutes the basis of human and social security (democratic order), must be taken immediately. Legislation (hard laws) are needed, mainly for creating zones of generative AI use (strong restrictions for elections and security).
･ In addition, from the viewpoint of ecosystem maintenance (including the dissemination of personal information), it is necessary to consider optimizing copyright law in line with the times, in a manner compatible with using generative AI itself, from both institutional and technological perspectives.
･ However, as it takes time to revise the law, the following steps must be taken: the introduction of rules and joint regulations mainly by the media and various industries, the establishment and dissemination of effective technologies, and making efforts to revise the law.
･ In this process, the most important thing is to protect the dignity and liberty of individuals in order to achieve individual autonomy. Those involved will study the situation, taking into account critical assessments based on the value of community.
References:
‘Joint Proposal on Shaping Generative AI’ by The Yomiuri Shimbun Holdings and NTT Corp.
Major technology companies form AI-Enabled Information and Communication Technology (ICT) Workforce Consortium
MTN Consulting: Generative AI hype grips telecom industry; telco CAPEX decreases while vendor revenue plummets
Cloud Service Providers struggle with Generative AI; Users face vendor lock-in; “The hype is here, the revenue is not”
Amdocs and NVIDIA to Accelerate Adoption of Generative AI for $1.7 Trillion Telecom Industry
Light Source Communications Secures Deal with Major Global Hyperscaler for Fiber Network in Phoenix Metro Area
Light Source Communications is building a 140-mile fiber middle-mile network in the Phoenix, AZ metro area, covering nine cities: Phoenix, Mesa, Tempe, Chandler, Gilbert, Queen Creek, Avondale, Coronado and Cashion. The company already has a major hyperscaler as the first anchor tenant.
There are currently 70 existing and planned data centers in the area that Light Source will serve. As one might expect, the increase in data centers stems from the boom in artificial intelligence (AI).
The network will include a big ring, which will be divided into three separate rings. In total, Light Source will be deploying 140 miles of fiber. The company has partnered with engineering and construction provider Future Infrastructure LLC, a division of Primoris Services Corp., to make it happen.
“I would say that AI happens to be blowing up our industry, as you know. It’s really in response to the amount of data that AI is demanding,” said Debra Freitas [1.], CEO of Light Source Communications (LSC).
Note 1. Debra Freitas has led LSC since co-founding in 2014. Owned and operated network with global OTT as a customer. She developed key customer relationships, secured funding for growth. Currently sits on the Executive Board of Incompas.
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Light Source plans for the entire 140-mile route to be underground. It’s currently working with the city councils and permitting departments of the nine cities as it goes through its engineering and permit approval processes. Freitas said the company expects to receive approvals from all the city councils and to begin construction in the third quarter of this year, concluding by the end of 2025.
Primoris delivers a range of specialty construction services to the utility, energy, and renewables markets throughout the United States and Canada. Its communications business is a leading provider of critical infrastructure solutions, including program management, engineering, fabrication, replacement, and maintenance. With over 12,700 employees, Primoris had revenue of $5.7 billion in 2023.
“We’re proud to partner with Light Source Communications on this impactful project, which will exceed the growing demands for high-capacity, reliable connectivity in the Phoenix area,” said Scott Comley, president of Primoris’ communications business. “Our commitment to innovation and excellence is well-aligned with Light Source’s cutting-edge solutions and we look forward to delivering with quality and safety at the forefront.”
Light Source is a carrier neutral, owner-operator of networks serving enterprises throughout the U.S. In addition to Phoenix, several new dark fiber routes are in development in major markets throughout the Central and Western United States. For more information about Light Source Communications, go to lightsourcecom.net.
The city councils in the Phoenix metro area have been pretty busy with fiber-build applications the past couple of years because the area is also a hotbed for companies building fiber-to-the-premises (FTTP) networks. In 2022 the Mesa City Council approved four different providers to build fiber networks. AT&T and BlackRock have said their joint venture would also start deploying fiber in Mesa.
Light Source is focusing on middle-mile, rather than FTTP because that’s where the demand is, according to Freitas. “Our route is a unique route, meaning there are no other providers where we’re going. We have a demand for the route we’re putting in,” she noted.
The company says it already has “a major, global hyperscaler” anchor tenant, but it won’t divulge who that tenant is. Its network will also touch Arizona State University at Tempe and the University of Arizona.
Light Source doesn’t light any of the fiber it deploys. Rather, it is carrier neutral and sells the dark fiber to customers who light it themselves and who may resell it to their own customers.
Light Source began operations in 2014 and is backed by private equity. It did not receive any federal grants for the new middle-mile network in Arizona.
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Bill Long, Zayo’s chief product officer, told Fierce Telecom recently that data centers are preparing for an onslaught of demand for more compute power, which will be needed to handle AI workloads and train new AI models.
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About Light Source Communications:
Light Source Communications (LSC) is a carrier neutral, customer agnostic provider of secure, scalable, reliable connectivity on a state-of-the-art dark fiber network. The immense amounts of data businesses require to compete in today’s global market requires access to an enhanced fiber infrastructure that allows them to control their data. With over 120 years of telecom experience, LSC offers an owner-operated network for U.S. businesses to succeed here and abroad. LSC is uniquely positioned and is highly qualified to build the next generation of dark fiber routes across North America, providing the key connections for business today and tomorrow.
References:
https://www.lightsourcecom.net/services/
https://www.fiercetelecom.com/ai/ai-demand-spurs-light-source-build-middle-mile-network-phoenix
Proposed solutions to high energy consumption of Generative AI LLMs: optimized hardware, new algorithms, green data centers
AI sparks huge increase in U.S. energy consumption and is straining the power grid; transmission/distribution as a major problem
AI Frenzy Backgrounder; Review of AI Products and Services from Nvidia, Microsoft, Amazon, Google and Meta; Conclusions
AI Frenzy Backgrounder; Review of AI Products and Services from Nvidia, Microsoft, Amazon, Google and Meta; Conclusions
Backgrounder:
Artificial intelligence (AI) continues both to astound and confound. AI finds patterns in data and then uses a technique called “reinforcement learning from human feedback.” Humans help train and fine-tune large language models (LLMs). Some humans, like “ethics & compliance” folks, have a heavier hand than others in tuning models to their liking.
Generative Artificial Intelligence (generative AI) is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. AI technologies attempt to mimic human intelligence in nontraditional computing tasks like image recognition, natural language processing (NLP), and translation. Generative AI is the next step in artificial intelligence. You can train it to learn human language, programming languages, art, chemistry, biology, or any complex subject matter. It reuses training data to solve new problems. For example, it can learn English vocabulary and create a poem from the words it processes. Your organization can use generative AI for various purposes, like chatbots, media creation, and product development and design.
Review of Leading AI Company Products and Services:
1. AI poster child Nvidia’s (NVDA) market cap is about $2.3 trillion, due mainly to momentum-obsessed investors who have driven up the stock price. Nvidia currently enjoys 75% gross profit margins and has an estimated 80% share of the Graphic Processing Unit (GPU) chip market. Microsoft and Facebook are reportedly Nvidia‘s biggest customers, buying its GPUs last year in a frenzy.
Nvidia CEO Jensen Huang talks of computing going from retrieval to generative, which investors believe will require a long-run overhaul of data centers to handle AI. All true, but a similar premise about an overhaul also was true for Cisco in 1999.
During the dot-com explosion in the late 1990s, investors believed a long-run rebuild of telecom infrastructure was imminent. Worldcom executives claimed that internet traffic doubled every 100 days, or about 3.5 months. The thinking at that time was that the whole internet would run on Cisco routers at 50% gross margins.
Cisco’s valuation at its peak of the “Dot.com” mania was at 33x sales. CSCO investors lost 85% of their money when the stock price troughed in October 2002. Over the next 16 years, as investors waited to break even, the company grew revenues by 172% and earnings per share by a staggering 681%. Over the last 24 years, CSCO buy and hold investors earned only 0.67% per year!
2. Microsoft is now a cloud computing/data-center company, more utility than innovator. Microsoft invested $13 billion in OpenAI for just under 50% of the company to help develop and roll out ChatGPT. But much of that was funny money — investment not in cash but in credits for Microsoft‘s Azure data centers. Microsoft leveraged those investments into super powering its own search engine, Bing, with generative AI which is now called “Copilot.” Microsoft spends a tremendous amount of money on Nvidia H100 processors to speed up its AI calculations. It also has designed its own AI chips.
3. Amazon masquerades as an online retailer, but is actually the world’s largest cloud computing/data-center company. The company offers several generative AI products and services which include:
- Amazon CodeWhisperer, an AI-powered coding companion.
- Amazon Bedrock, a fully managed service that makes foundational models (FMs) from AI21 Labs, Anthropic, and Stability AI, along with Amazon’s own family of FMs, Amazon Titan, accessible via an API.
- A generative AI tool for sellers to help them generate copy for product titles and listings.
- Generative AI capabilities that simplify how Amazon sellers create more thorough and captivating product descriptions, titles, and listing details.
Amazon CEO Jassy recently said the the company’s generative AI services have the potential to generate tens of billions of dollars over the next few years. CFO Brian Olsavsky told analysts that interest in Amazon Web Services’ (AWS) generative AI products, such as Amazon Q and AI chatbot for businesses, had accelerated during the quarter. In September 2023, Amazon said it plans to invest up to $4 billion in startup chatbot-maker Anthropic to take on its AI based cloud rivals (i.e. Microsoft and Google). Its security teams are currently using generative AI to increase productivity
4. Google, with 190,000 employees, controls 90% of search. Google‘s recent launch of its new Gemini AI tools was a disaster, producing images of the U.S. Founding Fathers and Nazi soldiers as people of color. When asked if Elon Musk or Adolf Hitler had a more negative effect on society, Gemini responded that it was “difficult to say.” Google pulled the product over “inaccuracies.” Yet Google is still promoting its AI product: “Gemini, a multimodal model from Google DeepMind, is capable of understanding virtually any input, combining different types of information, and generating almost any output.”
5. Facebook/Meta controls social media but has lost $42 billion investing in the still-nascent metaverse. Meta is rolling out three AI features for advertisers: background generation, image cropping and copy variation. Meta also unveiled a generative AI system called Make-A-Scene that allows artists to create scenes from text prompts . Meta’s CTO Andrew Bosworth said the company aims to use generative AI to help companies reach different audiences with tailored ads.
Conclusions:
Voracious demand has outpaced production and spurred competitors to develop rival chips. The ability to secure GPUs governs how quickly companies can develop new artificial-intelligence systems. Tech CEOs are under pressure to invest in AI, or risk investors thinking their company is falling behind the competition.
As we noted in a recent IEEE Techblog post, researchers in South Korea have developed the world’s first AI semiconductor chip that operates at ultra-high speeds with minimal power consumption for processing large language models (LLMs), based on principles that mimic the structure and function of the human brain. The research team was from the Korea Advanced Institute of Science and Technology.
While it’s impossible to predict how fast additional fabricating capacity comes on line, there certainly will be many more AI chips from cloud giants and merchant semiconductor companies like AMD and Intel. Fat profit margins Nvidia is now enjoying will surely attract many competitors.
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References:
https://www.zdnet.com/article/how-to-use-the-new-bing-and-how-its-different-from-chatgpt/
https://cloud.google.com/ai/generative-ai
https://aws.amazon.com/what-is/generative-ai/
https://www.wsj.com/articles/amazon-is-going-super-aggressive-on-generative-ai-7681587f
Curmudgeon: 2024 AI Fueled Stock Market Bubble vs 1999 Internet Mania? (03/11)
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