Tag: Microsoft Corporation’s (MSFT)

Big Tech’s In-House AI Chips: A Threat to Nvidia’s Data Center Revenue

Posted on by The INO.com Team

Nvidia Corporation (NVDA) has long been the dominant player in the AI-GPU market, particularly in data centers with paramount high-compute capabilities. According to Germany-based IoT Analytics, NVDA owns a 92% market share in data center GPUs.

Nvidia’s strength extends beyond semiconductor performance to its software capabilities. Launched in 2006, CUDA, its development platform, has been a cornerstone for AI development and is now utilized by more than 4 million developers.

The chipmaker’s flagship AI GPUs, including the H100 and A100, are known for their high performance and are widely used in data centers to power AI and machine learning workloads. These GPUs are integral to Nvidia’s dominance in the AI data center market, providing unmatched computational capabilities for complex tasks such as training large language models and running generative AI applications.

Additionally, NVDA announced its next-generation Blackwell GPU architecture for accelerated computing, unlocking breakthroughs in data processing, engineering simulation, quantum computing, and generative AI.

Led by Nvidia, U.S. tech companies dominate multiple facets of the burgeoning market for generative AI, with market shares of 70% to over 90% in chips and cloud services. Generative AI has surged in popularity since the launch of ChatGPT in 2022. Statista projects the AI market to grow at a CAGR of 28.5%, resulting in a market volume of $826.70 billion by 2030.

However, NVDA’s dominance is under threat as major tech companies like Microsoft Corporation, Meta Platforms, Inc. (META), Amazon.com, Inc. (AMZN), and Alphabet Inc. (GOOGL) develop their own in-house AI chips. This strategic shift could weaken Nvidia’s grip on the AI GPU market, significantly impacting the company’s revenue and market share.

Let’s analyze how these in-house AI chips from Big Tech could reduce reliance on Nvidia’s GPUs and examine the broader implications for NVDA, guiding how investors should respond.

The Rise of In-house AI Chips From Major Tech Companies

Microsoft Azure Maia 100

Microsoft Corporation’s (MSFT) Azure Maia 100 is designed to optimize AI workloads within its vast cloud infrastructure, like large language model training and inference. The new Azure Maia AI chip is built in-house at Microsoft, combined with a comprehensive overhaul of its entire cloud server stack to enhance performance, power efficiency, and cost-effectiveness.

Microsoft’s Maia 100 AI accelerator will handle some of the company’s largest AI workloads on Azure, including those associated with its multibillion-dollar partnership with OpenAI, where Microsoft powers all of OpenAI’s workloads. The software giant has been working closely with OpenAI during the design and testing phases of Maia.

“Since first partnering with Microsoft, we’ve collaborated to co-design Azure’s AI infrastructure at every layer for our models and unprecedented training needs,” stated Sam Altman, CEO of OpenAI. “Azure’s end-to-end AI architecture, now optimized down to the silicon with Maia, paves the way for training more capable models and making those models cheaper for our customers.”

By developing its own custom AI chip, MSFT aims to enhance performance while reducing costs associated with third-party GPU suppliers like Nvidia. This move will allow Microsoft to have greater control over its AI capabilities, potentially diminishing its reliance on Nvidia’s GPUs.

Alphabet Trillium

In May 2024, Google parent Alphabet Inc. (GOOGL) unveiled a Trillium chip in its AI data center chip family about five times as fast as its previous version. The Trillium chips are expected to provide powerful, efficient AI processing that is explicitly tailored to GOOGL’s needs.

Alphabet’s effort to build custom chips for AI data centers offers a notable alternative to Nvidia’s leading processors that dominate the market. Coupled with the software closely integrated with Google’s tensor processing units (TPUs), these custom chips will allow the company to capture a substantial market share.

The sixth-generation Trillium chip will deliver 4.7 times better computing performance than the TPU v5e and is designed to power the tech that generates text and other media from large models. Also, the Trillium processor is 67% more energy efficient than the v5e.

The company plans to make this new chip available to its cloud customers in “late 2024.”

Amazon Trainium2

Amazon.com, Inc.’s (AMZN) Trainium2 represents a significant step in its strategy to own more of its AI stack. AWS, Amazon’s cloud computing arm, is a major customer for Nvidia’s GPUs. However, with Trainium2, Amazon can internally enhance its machine learning capabilities, offering customers a competitive alternative to Nvidia-powered solutions.

AWS Trainium2 will power the highest-performance compute on AWS, enabling faster training of foundation models at reduced costs and with greater energy efficiency. Customers utilizing these new AWS-designed chips include Anthropic, Databricks, Datadog, Epic, Honeycomb, and SAP.

Moreover, Trainium2 is engineered to provide up to 4 times faster training compared to the first-generation Trainium chips. It can be deployed in EC2 UltraClusters with up to 100,000 chips, significantly accelerating the training of foundation models (FMs) and large language models (LLMs) while enhancing energy efficiency by up to 2 times.

Meta Training and Inference Accelerator

Meta Platforms, Inc. (META) is investing heavily in developing its own AI chips. The Meta Training and Inference Accelerator (MTIA) is a family of custom-made chips designed for Meta’s AI workloads. This latest version demonstrates significant performance enhancements compared to MTIA v1 and is instrumental in powering the company’s ranking and recommendation ads models.

MTIA is part of Meta’s expanding investment in AI infrastructure, designed to complement its existing and future AI infrastructure to deliver improved and innovative experiences across its products and services. It is expected to complement Nvidia’s GPUs and reduce META’s reliance on external suppliers.

Bottom Line

The development of in-house AI chips by major tech companies, including Microsoft, Meta, Amazon, and Alphabet, represents a significant transformative shift in the AI-GPU landscape. This move is poised to reduce these companies’ reliance on Nvidia’s GPUs, potentially impacting the chipmaker’s revenue, market share, and pricing power.

So, investors should consider diversifying their portfolios by increasing their exposure to tech giants such as MSFT, META, AMZN, and GOOGL, as they are developing their own AI chips and have diversified revenue streams and strong market positions in other areas.

Given the potential for reduced revenue and market share, investors should re-evaluate their holdings in NVDA. While Nvidia is still a leader in the AI-GPU market, the increasing competition from in-house AI chips by major tech companies poses a significant risk. Reducing exposure to Nvidia could be a strategic move in light of these developments.

Datadog (DDOG): A Software Industry Savior?

Posted on by The INO.com Team

  Shares of Snowflake Inc. (SNOW), MongoDB Inc. (MDB), and Elastic N.V. (ESTC) rallied sharply in last Tuesday’s premarket trading after Datadog, Inc.’s (DDOG) stronger-than-expected third-quarter earnings report eased fears about consumption-based software companies for the most recent quarter.

SNOW’s shares surged more than 8%, shares of MDB were up nearly 6%, while ESTC shares climbed about 4%. DDOG’s stock surged more than 24% last Tuesday, marking its best day ever. Moreover, the stock has gained more than 22% over the past month and nearly 51% year-to-date.

Mizuho desk-based analyst Jordan Klein called DDOG “clearly one of the most shorted and over-sold names” in all software and among consumption-based players. After Datadog topped expectations with its latest results and outlook, “Other peers like SNOW, MDB and ESTC yet to report should bounce, but also a good sign for AWS and Azure demand trends in my view and broader software,” wrote Klien.

Let’s determine if DDOG is a solid buy now and what the stock’s upbeat earnings report means for other computer-based software companies.

Here are some of the factors that could impact DDOG’s performance in the near term:

Robust Financial Performance in The Last Reported Quarter

For the third quarter that ended September 30, 2023, DDOG, the monitoring and security platform for cloud applications, reported revenue of $547.54 million, beating analysts’ estimate of $524.20 million. This compared to the revenue of $436.53 million in the same quarter of 2022. Its non-GAAP gross profit grew 29.5% year-over-year to $450.87 million.

Datadog continued to grow its customer base and ended the quarter with 3,130 customers worth $100,000 in annual recurring revenue (ARR). This is an increase of 140 customers from the previous quarter and 530 more than the company had a year ago, at the end of September 2022.

The cloud company’s non-GAAP operating income came in at $130.76 million, an increase of 74.7% from the prior year’s quarter. Its non-GAAP net income rose 95.5% year-over-year to $158.46 million. It posted non-GAAP net income per share of $0.45, compared to the consensus estimate of $0.34, and up 95.7% year-over-year.

Furthermore, net cash provided by operating activities increased 82.7% year-over-year to $152.78 million. DDOG’s free cash flow stood at $138.19 million, up 105.9% from the same period last year.

Upbeat Full-Year Guidance

“We were pleased with our execution in the third quarter, with 25% year-over-year revenue growth, robust new logo bookings, and a continued focus on solving our customers' DevSecOps pain points,” said Olivier Pomel, co-founder and CEO of DDOG.

“Companies across all industries and sizes are building cloud applications and services to deliver positive business outcomes, including more users, higher revenue growth, improved productivity, and cost savings. With our unified, cloud-native, end-to-end observability and security platform, Datadog is uniquely positioned to help our customers reach their goals,” added Pomel.

After upbeat third-quarter earnings and confidence in continued business momentum, DDOG raised its revenue and profit view for the full fiscal year 2023. For the full year, the company expects its revenue to be between $2.103 billion and $2.107 billion. Its non-GAAP operating income and non-GAAP net income per share are expected to be in the range of $453-$457 million and $1.52-$1.54, respectively.

DDOG now expects fourth-quarter revenue between $564 million and $568 million. The company’s non-GAAP operating income is anticipated to be between $129 million and $133 million, while its non-GAAP net income per share to be between $0.42 and $0.44.

Impressive Historical Growth

Over the past three years, DDOG’s revenue grew at a CAGR of 55%. Its tangible book value and total assets increased at CAGRs of 16.5% and 25% over the same period, respectively. Also, the company’s levered free cash flow improved at 88% CAGR over the same timeframe.

Positive Recent Developments

On November 8, Datadog expanded a strategic partnership with Google Cloud, which allows Google Cloud customers to proactively observe and secure their cloud-native and hybrid applications within Datadog’s unified platform.

As a part of the extended partnership and integrations, DDOG is one of the first AI/ML observability solution partners for Vertex AI, enabling AI ops teams and developers to monitor, analyze, and optimize the performance of their ML models in production.

Also, on August 3, DDOG announced new AI observability capabilities that assist customers in deploying LLM-based applications to production with confidence and help them troubleshoot health, cost, and accuracy in real-time.

These capabilities include integrations for the end-to-end AI stack: AI Infrastructure and compute, embeddings and data management, model serving and deployment, model layer, and orchestration framework. Datadog’s LLM observability includes model catalog, model performance, and model drift.

Datadog’s CEO Olivier Pomel told analysts on a conference call that “AI-native customers” contributed 2.5% of the company’s annualized revenue during the last reported quarter.

Favorable Analyst Estimates

Analysts expect DDOG’s revenue for the fourth quarter (ending December 2023) to grow 21.1% year-over-year to $568.56 million. The consensus EPS estimate of $0.44 for the ongoing year indicates a 68.6% year-over-year increase. Moreover, the company has surpassed the consensus revenue and EPS estimates in each of the trailing four quarters, which is impressive.

For the fiscal year 2023, Street expects DDOG’s revenue and EPS to grow 25.4% and 56.4% year-over-year to $2.10 billion and $1.53, respectively. In addition, the company’s revenue and EPS for the fiscal year 2024 are expected to increase 22.7% and 14.8% from the previous year to $2.58 billion and $1.76, respectively.

Rating Upgrade

Mark Murphy, an analyst at JPMorgan Chase & Co., upgraded his rating of DDOG to “Overweight” from “Neutral”, stating that the “worst period” of declining revenue growth at the solution solutions group has most likely ended. Also, the analyst bumped up the share price target of the stock to $115 from $90.

What Do DDOG’s Upbeat Earnings Mean for Other Computer-Based Software Firms?

Datadog and other consumption-based software companies, including Amazon.com, Inc.’s (AMZN) AWS, Microsoft Corporation’s (MSFT) Azure, and SNOW, among others, have been grappling with a slowdown in cloud spending by inflation-hit customers.

After COVID-19 prompted companies, governments, and schools to switch to cloud services driven by the surge in work-from-home, several cloud-computing firms enjoyed robust demand. However, when inflation hit last year, the central bank hiked interest rates, and cloud stocks began tumbling as companies responded by scrutinizing their IT spending as they engaged in cost-reduction measures.

Inflation has declined sharply from its 2022 peak, with the Consumer Price Index (CPI) showing further signs of easing in October. The core CPI, excluding volatile food and energy prices, increased 0.2% for the month and 4% year-over-year, against the forecast of 0.3% and 4.1%, respectively. The annual level was the lowest in nearly two years and down from 4.1% in September.

With these positive developments, cloud infrastructure providers indicated last month that some organizations’ cost-cutting efforts have begun to wane. Datadog’s Pomel also validated this observation, saying optimization activity among the company’s clients could be easing.

“Overall, we continue to see impact from optimization in our business, but we believe that the intensity and breadth of optimization we’ve experienced in recent quarters is moderating,” he said.

DDOG’s significant surge last week, following its upbeat earnings and optimistic guidance, also buoyed other cloud-computing names, including SNOW, MDB, and ESTC.

As per the latest forecast from Gartner, global end-user spending on public cloud is expected to rise by 20.4% to a total of $678.80 billion in 2024, an increase from $563.60 billion in 2023. Growing business needs and emerging technologies like GenAI drive cloud model innovation.

Bottom Line

DDOG, the data analytics platform provider, beat third-quarter Wall Street’s expectations for earnings and revenue. Further, the cloud company raised its full fiscal year 2023 guidance on third-quarter upside and expected continued business momentum.

Due to reduced IT spending by inflation-hit clients, Datadog’s revenue growth slowed from 83% in early 2022 to 25% now. However, this slowdown will likely “moderate and level out,” driven by the recovery in companies’ cloud spending, benefitting DDOG significantly. The company is well-positioned to serve its customers effectively with its unified, cloud-native, end-to-end observability and security platform.

According to Alex Zukin of Wolfe Research, DDOG has the potential to become the “fastest-growing software company” amid the AI boom. Datadog’s platform can offer advanced predictive analytics and intelligent alerting by leveraging new AI and ML capabilities.

Given DDOG’s solid financials, accelerating profitability, and bright growth outlook, it could be wise to consider investing in this software stock.