| Constellation Research

AWS annual revenue run rate hits $100 billion as growth accelerates

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

Amazon Web Services revenue growth accelerated in the first quarter as the cloud giant reported sales of $25 billion.

Amazon reported overall first quarter net income of $10.4 billion, or 98 cents a share, on revenue of $143.3 billion, up 13%. Wall Street was expecting Amazon to report earnings of 83 cents a share on revenue of $142.56 billion.

With the Amazon and AWS results, it's clear that hyperscale cloud providers are landing AI workloads. Microsoft Azure revenue in Q3 up 31% | Alphabet shows Q1 strength in Google Cloud, initiates dividend

AWS delivered first quarter operating income of $9.4 billion on revenue of $25 billion, up 17% from a year ago. Fourth quarter revenue growth for AWS was 13%. Wedbush was expecting AWS first quarter revenue of $24.6 billion. AWS announced the general availability of Amazon Q earlier in the day.

By the numbers:

Amazon's North America commerce unit had first quarter revenue of $86.3 billion with operating income of $5 billion.
International commerce sales in the first quarter were $31.9 billion, up 10% from a year ago, with operating income of $900 million.
Amazon's first quarter net income includes a $2 billion non-operating loss from the company's investment in Rivian.
Amazon advertising revenue in the first quarter was $11.8 billion, up 24% from a year ago.

CEO Andy Jassy said AWS was benefiting from "the combination of companies renewing their infrastructure modernization efforts and the appeal of AWS’s AI capabilities is reaccelerating AWS’s growth rate (now at a $100 billion annual revenue run rate)."

Speaking on an analyst conference call, CEO Jassy talked up cloud demand, Amazon Bedrock and the company's approach to generative AI. He said:

"Companies have largely completed the lion's share of their cost optimization and have turned their attention to newer initiatives. We see considerable momentum on the AI front where we've accumulated a multi billion dollar revenue run rate already."

Jassy touted Bedrock and said enterprises are increasingly looking at generative AI strategies that revolve around model selection and customization ability. He said Bedrock's recent launch of custom model import was "a sneaky launch as it satisfies a customer request that has not been met yet." Amazon Bedrock gets custom model import, evaluation tools, new Titan models

"The prospect of these two linchpin services in SageMaker and Bedrock working well together is quite appealing the top of the stack for the Gen AI applications being built," said Jassy, who added that Q is off to a good start with enterprises.

He said capital spending will be up as AWS builds out data centers to meet demand.

"The more demand AWS has, the more we have to procure new data centers power and hardware. And as a reminder, we spend most of the capital upfront. As you've seen over the last several years, we make that up an operating margin and free cash flow down the road as demand scales out. We don't spend the capital without very clear signals that we can monetize it. We remain very bullish in AWS. We're at $100 billion dollar annualized revenue run right now and 85% or more of the global IT spend remains on premises. And this is before you even calculate genAI. There's a very large opportunity in front of us."

As for the outlook, Amazon projected second quarter sales of $144 billion to $149 billion, up 7% to 11% from the previous year.

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Amazon Q generally available with new pricing plans

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Amazon Q is generally available across the Amazon Web Services ecosystem and the generative AI capabilities come with new pricing models.

AWS' Q generative AI assistant was announced at re:Invent and has been tested across multiple use cases before availability. Amazon Q is a layer in the AWS stack that serves as glue across multiple services.

The headliners for Amazon Q are Amazon Q Developer, a coding assistant, Amazon Q Business, designed to make employees more productive, and Amazon Q Apps, which are part of Amazon Q Business and can automate business tasks. Amazon Q Apps are in preview.

As for pricing, Amazon Q Business has two tiers. Amazon Q Business Lite is $3 per user a month for basic functionality. Amazon Business Pro, which includes all features, Amazon QuickSight and Amazon Q Apps, goes for $20 per user a month.

Amazon Q Developer has a free tier and Pro, which is $19 per month per user.

AWS has a few pricing examples. Here's one for Amazon Business.

You are an enterprise company with 5,000 employees looking to deploy Amazon Q Business. You decide to purchase Amazon Q Business Lite for 4,500 users and Amazon Q Business Pro for 500 users. You have 1 million enterprise documents across sources like SharePoint, Confluence, and ServiceNow that need indexing with an Enterprise Index. Your monthly charges will be as follows:

Enterprise Index for 1M documents will need 50 index units of 20K capacity each (assuming that the extracted text size of 1M documents is less than 200 MB * 50 units = 10 GB) :

$0.264 per hour x 50 units x 24 hours x 30 days = $9,504

User subscriptions:

4,500 users * $3 per user/month = $13,500

500 users * $20 per user/month = $10,000

Total user subscriptions: $23,500

In summary, your monthly charges are as follows:

Enterprise Index: $9,504

User subscriptions: $23,500

Total per month: $33,004

The return-on-investment case for Amazon Q is straightforward--Amazon Q will provide a productivity boost. Amazon Q Developer also has tools to optimize their AWS environments by analyzing billing trends, consumption and costs by region.

Amazon Q Business is able to connect to more than 40 common business tools to surface insights and serve up insights and dashboards on the fly.

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Coursera's outlook highlights how genAI reskilling will be lumpy

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

The state of reskilling in the generative AI era looks like it's going to be a bit lumpy if Coursera's first quarter results and outlook are any indication. Coursera CEO Jeff Maggioncalda said, "we remain in the early stages of understanding how generative AI will reshape the way we live, learn and work."

Coursera's first quarter results were mixed as earnings beat expectations, but revenue fell short. The second quarter outlook from Coursera calls for revenue of $162 million and $166 million, well below Wall Street estimates of $177.8 million. Coursera said 2024 revenue will be $695 million to $705 million, which was short of $736.5 million.

The company has three operating units--consumer, enterprise and degrees. Consumer revenue was up 18% in the first quarter compared to a year ago and enterprise and degrees revenue was up 10%. AI courses were driving demand in consumer and building in enterprise and degrees. Coursera's generative AI transformation is about a year old.

However, for all the talk of reskilling in the genAI era there are false starts. Citing an Accenture report, Maggioncalda noted that 95% of employees see value in working with generative AI, but only 5% of organizations are actively reskilling their workforce at scale.

For a company like Coursera, the challenges are leveraging genAI for content creation in an accurate way, being able to swap models as needed and hitting learnings beyond builders. Coursera has built out its AI courses and landed deals with enterprises as well as universities.

Maggioncalda said Coursera partners have built out more than 75 new courses and project in generative AI since the start of the year. Coursera is also using generative AI to power Coursera Course Builder that will enable "any business, government or campus customer, to easily and quickly produce high-quality custom private courses at scale."

In addition, Coursera Coach is designed to be an AI-powered tutor. So, what's the problem? Here are a few.

Consumer revenue. Ken Hahn, CFO of Coursera, said consumer revenue "was softer than anticipated" in North America. "We underperformed in our North American region, where we are experiencing a lower volume and conversion of paid learners compounded by the delay of the key content launch from one of our educator partners as compared to the timing in our financial plan," said Kahn.
Businesses are mixed when it comes to reskilling. Government and campus verticals are showing strong demand for Coursera for Business, but corporate learning budgets are tight. "We continue to see a divergence in performance across our verticals, specifically pressuring Coursera for Business, offset by momentum in our other two verticals, government and campus," said Kahn. "Corporate learning budgets remain under pressure."
The degree revolution hasn't arrived just yet. Coursera's degree revenue was $14.8 million in the first quarter, up 10% from a year ago. Total number of students was 22,200, up 23% from a year ago. There are no content costs for degree revenue, so the segment sees gross margins of 100% of revenue. Kahn said:

"We remain focused on the long-term opportunity in degrees. We believe that our platform is uniquely positioned to fundamentally transform the college degree. We need to start validating that potential with renewed and increasing growth. We believe that the path to better degrees growth lies in working with our university partners to create stronger pathways between our consumer segment where we benefit from scale and the growing selection of pathway degree programs."

Maggioncalda said AI content is driving engagement, but there needs to be more courses that cater to a larger base. He said:

"People want new AI content, both for the builders who are building these models. But also, the users, people who need to learn how to use this stuff. We see broad appetite 4x what we saw last year in terms of people taking AI-related content."

Ultimately, the population of AI users is going to be much larger than the builders. Coursera needs to accelerate content launches that add AI throughout existing courses and launch modules that educate people on how their roles will change.

Maggioncalda said:

"Generative AI will have a huge impact on the way people do their jobs.

They're going to need to learn new skill to be, you name it, a PR comms person or a financial analyst or a supply chain manager or a UX designer. We think there's a very broad opportunity to really refresh the content to appeal to strong demand that we're seeing from learners around generative AI."

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Snapchat gets boost from lower cloud infrastructure costs

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Snapchat made a bet on using machine learning and AI to improve its advertising platform, increase content engagement and ultimately revenue growth. If it could optimize its infrastructure spending, Snapchat would be able to grow the bottom line.

The first quarter gave an indicator that Snapchat's bets are starting to pay off. What's unclear is whether the company can continue to optimize its cloud spending since the first quarter bottom line was helped along by credits from hyperscale cloud providers.

Google Cloud is Snapchat's primary cloud provider, but Amazon Web Services is in the mix, according to Snapchat's regulatory filings.

CFO Derek Anderson said:

"We benefited from higher-than-average service provider credits in Q1 that helped to further reduce infrastructure costs in Q1. As a result, infrastructure cost per DAU declined from $0.84 in Q4 of 2023 to $0.80 in Q1 of 2024."

Saving 4 cents per daily active user doesn't sound like much until you scale those savings across 422 million daily active users. In Snapchat's results, cloud costs--operating expenses--turn up in cost of revenue. Snapchat executives said on the company's first quarter earnings call that the company is spending about $100 million a quarter on machine learning and AI.

"CxOs need to consider that cloud infrastructure costs are driven by usage, no matter how efficient an application's usage of the infrastructure is," said Constellation Research analyst Holger Mueller. "So for Snapchat shaving infrastructure cost is a step in the right direction – but not an insight on the efficiency of its coding."

The company reported a net loss of $305.1 million, or 19 cents a share, on revenue of $1.19 billion, up 21% from a year ago. Non-GAAP earnings were 3 cents a share, well above expectations.

Snapchat CEO Evan Spiegel said in a shareholder letter and the earnings call that the company is improving content ranking and personalization with the help of its AI investments.

"We've built larger and more advanced ranking models that are driving improvements in content engagement. In addition, we made significant progress toward unifying the ranking models between Spotlight and Stories to a single backend stack that ranks all content types," said Spiegel. "A single, unified stack will benefit Snapchatters by showing them the most relevant and entertaining content across Snapchat and helping creators find and deepen engagement with their audience."

An improving ad market also helped Snapchat, but its cloud savings flowed faster to the bottom line. Snapchat noted that it has improved its cloud infrastructure unit costs with "engineering efficiency and pricing improvements."

Indeed, Snapchat was an active participant in Google Cloud Next with sessions on moving to a microservices architecture and how it is using BigQuery and other data platforms. Snapchat has been built on Google Cloud since its inception and participated in 10 sessions at Google Cloud Next.

However, Snapchat said quarterly costs per daily active user (DAU) will be in the 83 cents to 85 cents range for the remainder of 2024. It's worth noting that Snapchat had an infrastructure cost per daily active user of 59 cents in the first quarter of 2023 and 70 cents due to its AI investment in the second quarter of 2023 before escalating to current levels of 80 cents to 85 cents per DAU.

Snapchat's infrastructure cost per DAU guidance seems to indicate that the company still has to work on its cloud optimizations and account for its AI investments. It's possible that the first quarter cost of revenue was helped in a large part because of one-off cloud credits.

Fully optimized--assuming infrastructure costs from 2022 are a benchmark--Snapchat's optimized state is infrastructure costs of 59 cents per DAU.

Spiegel said that infrastructure costs are slowing quarter over quarter as the company optimizes its spend even as it boosts revenue growth.

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A neutral vendor in your stack is great, but also a pipe dream

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Every enterprise technology stack needs neutral vendors that play well with others, integrates seamlessly and keeps customer value front and center while refraining from the dreaded cross-sell.

The problem is that these neutral vendors are acquired if they become too successful. Once these neutral vendors are acquired it's all about the cross-sell game under new ownership.

We had a near miss recently with Informatica. Informatica has a data management platform that connects with all the primary clouds and enterprise systems. As businesses look to adopt generative AI, they're increasingly realizing that a vendor like Informatica is a go-to player.

Informatica was doing well being Switzerland that Salesforce reportedly wanted to buy it. Talks broke down and Informatica remains a neutral party--for now.

There are a bevy of neutral vendors considering regulators around the world are a hard sell on mergers and acquisitions. At some point that'll change. There are already signs that the M&A market will accelerate.

This post first appeared in the Constellation Insight newsletter, which features bespoke content weekly and is brought to you by Hitachi Vantara.

It's possible that HashiCorp looked like a neutral vendor across clouds, but it has now been acquired by IBM in a $6.5 billion deal. IBM is an example of a company that has played the neutrality game and scaled. IBM has morphed repeatedly over the years, but the combination of AI and consulting are strong. Keep in mind that IBM also acquired Red Hat, which has kept most of its neutrality cred.

When conditions change vendor neutrality will become a bit of a pipe dream. Consider VMware. VMware was neutral across data center infrastructure and connected clouds. Now that VMware is part of Broadcom, customers are antsy about lock-in and the transition to subscriptions over licenses. Nutanix gets the neutrality sales pitch--until it's acquired.

I'd argue ServiceNow is a version of a neutral play as it can build workflows and processes on top of multiple systems of records. However, ServiceNow dependence also means higher prices at some point, but hopefully there's value too. "We're not interested in shutting anybody out. We're actually technology capable enough to open up to everybody and that's really turning on the whole ecosystem in our favor," said ServiceNow CEO Bill McDermott, speaking on the company's first quarter earnings call.

When it comes to large language models (LLMs), AWS obviously wants to be your Switzerland of generative AI. Google Cloud will also play that game. Microsoft will speak to it, but for now is closely affiliated with OpenAI. Hugging Face is your neutrality play but could be acquired down the line.

Celonis is another company that's neutral and can tap into multiple systems for process intelligence. It might grow into an IPO--or become part of a larger vendor. UiPath is in a similar automation and process bucket. There are plenty of other neutrality options including Atlassian, Nvidia and data platforms such as Snowflake, MongoDB and DataBricks.

Bottom line: Chances are that your neutral vendor today will not remain that way 5 years from now. CIOs can manage the vendor neutrality dream if they play along early and then keep their options open. The biggest lesson from the Informatica cadence is that neutrality can be successful, but eventually it's a pipe dream.

Also:

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SoundCommerce CEO Eric Best: 'Data capability enables customer lifetime value'

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

SoundCommerce Co-Founder and CEO Eric Best said retail winners will increasingly be determined by how they leverage data and artificial intelligence to drive customer lifetime value.

Best, along with CTO Jared Stiff started the company to help brands deliver better shopper experiences with data. SoundCommerce, founded in 2018, has raised more than $33 million in funding. The company platform is designed to take retail data infrastructure and make it composable and no code so retailers can better model experiences on the fly.

Retailers will need to leverage data to thrive amid margin compression, pricing arbitrage, higher cost of capital and fierce competition. On DisrupTV, Constellation Research's Ray Wang and Holger Mueller caught up with Best. Here are some of the key takeaways.

Two megatrends impacting retail. Best said there are two obvious megatrends that are reshaping retail. The first is the return to shopping in person and retail as a community activity. The second is that the cost of capital has increased dramatically. Both are impacting direct-to-consumer retailers.

Best said of direct-to-consumer retail:

"There are high variable costs on the front end to acquire a customer through some combination of Facebook, Tik Tok, Instagram and Google and on the other end, there are very high costs associated with doorstep delivery. We often joke internally that there are a thousand things that must go right to be successful in digital retail. And you fail if any one of them goes wrong."

Best of times, worst of times. Best said it is easier than ever to start a retail business because of platforms like Shopify and marketplace providers like Amazon and Walmart. "Getting started is very easy. Anyone can throw up a shingle," said Best. "Scaling the business is hard and profitable growth is exceedingly difficult."

Because of this difficulty you see direct-to-consumer brands like Casper, Warby Parker and Dollar Shave Club to back to omnichannel, wholesale and physical stores. "The market is proving that these are difficult businesses to operate," said Best.

Who owns the customer? Best added that there are trade-offs between following a direct-to-consumer model vs standing up for business in a marketplace. For instance, if you're an Amazon seller much of the complexity of the business model is removed once you figure out how to promote your products.

The trade-off happens when the marketplace owns the customer and experience as well as the data, which is "a really important asset to the enterprise value of consumer brands," said Best. "It is rare to see companies that are able to rely on a marketplace," he added.

Feedback loops. Data is critical because digital commerce requires a lot of data, analytics and insights to power the next best actions and feedback loops. Best said he expects data platforms to become more important to retail. "We have a proliferation of brand-new companies building data capabilities on the backs of Snowflake or Google Cloud or DataBricks," said Best.

Best said Amazon is a great example of retail as data feedback loop. AWS was created as a proprietary infrastructure for predictions, modeling inventory and logistics based on demand signals. "The use cases we see emerging wit generative AI began with proprietary algorithms that were developed by Amazon or Walmart," said Best.

The SoundCommerce bet. Best said SoundCommerce is scaling on two core constructs. First, every retail business decision has the potential to be a generative AI prediction that can be tactical as well as strategic. And then there's the data capabilities that can be game changers. "We think data capability enables customer lifetime value. There's a connection of individual tactical decisions that can be tied together to drive long-term customer relationships," said Best.

Data management will be mission critical for retailers since the biggest barrier to AI adoption isn't the algorithm, but having the data cleaned and properly structured for AI consumption. SoundCommerce has a set of models for omnichannel, acquisition and retention marketing, products and promotions and fulfillment. "The challenge we see across industries is readiness and the heavy lifting to get the data ready in the first place," said Best.

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Intel Q2 outlook weaker than expected

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Intel's second quarter outlook was below expectations even though its first quarter was better-than-expected.

The chipmaker, which is trying to catch up in AI processors, said it expects second quarter revenue between $12.5 billion to $13.5 billion, well below the $13.61 billion Wall Street expected. Intel also projected non-GAAP earnings of 10 cents a share in the second quarter, well below estimates of 25 cents a share.

Intel's outlook overshadowed better-than-expected first quarter earnings. The company reported a first quarter net loss of 9 cents a share on revenue of $12.7 billion, up 9%. Non-GAAP earnings in the first quarter were 18 cents a share. Analysts expected Intel to report first quarter earnings of 14 cents a share on revenue of $12.78 billion.

CEO Pat Gelsinger said the company was making "steady progress." "We are confident in our plans to drive sequential growth throughout the year as we accelerate our AI solutions," he said.

By unit:

Intel's Product revenue group had first quarter revenue of $11.9 billion, up 17% from a year ago.
Client Computing Group showed growth of 31% to revenue of $7.5 billion. Intel said more than 5 million AI PCs have shipped since December.
Data Center and AI had revenue of $3 billion, up 5%.
Intel Foundry revenue was $4.4 billion in the first quarter, down 10%.
Altera and Mobileye saw first quarter revenue declines of 58% and 48%, respectively.

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Alphabet shows Q1 strength in Google Cloud, initiates dividend

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Alphabet's Google Cloud business is now pushing a $40 billion annual revenue run rate as the company overall delivered strong first quarter results.

Alphabet reported first quarter revenue of $80.54 billion, up 15% from a year ago, with net income of $23.66 billion, or $1.89 a share. Wall Street was expecting Alphabet to report first quarter earnings of $1.50 a share on revenue of $78.7 billion.

CEO Sundar Pichai said first quarter results were driven by strength in search, YouTube and Google Cloud. "We are well under way with our Gemini era and there’s great momentum across the company. Our leadership in AI research and infrastructure, and our global product footprint, position us well for the next wave of AI innovation," said Pichai.

Ruth Porat, CFO and Chief Investment Officer of Alphabet, said the company's margins were expanding due to "ongoing efforts to durably reengineer our cost base."

The company also initiated a dividend of 20 cents a share to be paid on June 17. The company will pay quarterly cash dividends going forward. Alphabet also said it will repurchase up to $70 billion of shares.

On a conference call, Picahi said:

"We are already seeing developers and enterprise customers enthusiastically embrace Gemini 1.5 and use it for a wide range of things beyond Gemini for your build to other useful models, including our Gemma open models, as well as image and visual models and others."

Pichai added that Google is leveraging its infrastructure edge. "We have developed new AI models and algorithms that are more than 100 times more efficient than they were 18 months ago," he said.

Pichai also said Alphabet will continue to invest heavily in AI infrastructure.

"The increases in our capital expenditures this will fuel growth in cloud help us push the frontiers of AI models and enable innovation across services," said Pichai. "We have clear paths to AI monetization through ads and cloud as well as subscriptions."

By unit:

Google Search revenue was $46.16 billion, up from $40.36 billion a year ago.
YouTube revenue was $8.09 billion.
Google Network revenue was $7.4 billion.
Google Ad revenue was $61.66 billion.
Google Services’ operating income was $27.9 billion.
Google Cloud operating income was $900 million on revenue on $9.57 billion.
Other bets loss was $1.02 billion on revenue of $495 million.
Alphabet took a hit of $2.3 billion in the first quarter for real estate optimization and severance.

Key points from Porat:

"Cloud segment revenues were $9.6 billion for the quarter, up 28% reflecting significant growth in GCP with an increasing contribution from AI and strong Google Workspace growth, primarily driven by increases in average revenue per seat."
"With respect to Google Cloud performance in q1 reflects strong demand for our GCP infrastructure and solutions as well as the contribution from our workspace productivity tools. The growth we're seeing across cloud is underpinned by the benefit AI provides for our customers."
"Looking ahead, we remain focused on our efforts to moderate the pace of expense growth in order to create capacity for the increases in depreciation and expenses associated with the higher levels of investment in our technical infrastructure."
Cap-ex in the first quarter was $12 billion driven by investment in technical infrastructure, servers and data centers. "We do expect the quarterly capex throughout the year to be roughly at or above the $12 billion cash capex we had here in q1," said Porat.

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Microsoft Azure revenue in Q3 up 31%

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Microsoft reported a strong third quarter with revenue growth was 17% with Microsoft Cloud revenue up 23% from a year ago. Azure and other cloud services revenue growth was 31% driven by AI services.

The company reported third quarter net income of $21.9 billion, or $2.94 a share, on revenue of $61.9 billion. Wall Street was expecting Microsoft to report third quarter earnings of $2.84 a share on revenue of $60.89 billion.

All eyes were on Azure and AI services. Here's the money slide:

Satya Nadella, CEO of Microsoft, said: "Microsoft Copilot and Copilot stack are orchestrating a new era of AI transformation, driving better business outcomes across every role and industry."

CFO Amy Hood Microsoft had good sales execution for Microsoft Cloud.

Hood said capital expenses including finance leases were $14 billion to support cloud demand and AI infrastructure.

As for the outlook, Hood said fourth quarter revenue for intelligent cloud will be between $28.4 billion to $28.7 billion. She said demand is outpacing capacity. For the fiscal 2025 year, revenue will grow at a double digit clip with higher capital expenditures than fiscal 2024. Hood said:

"We continue to focus on building businesses that create meaningful value for our customers, and therefore significant growth opportunities for years to come. For FY 2025 that focus and execution should again lead to double digit revenue and operating income growth, to scale to meet the growing demand signal for our cloud and AI products. We expect FY 25 capital expenditures to be higher than FY 24 expenditures over the course of the next year are dependent on demand signals and adoption of our services. So we will manage that signal through the year."

The quarter in review:

Key points from Nadella on the earnings conference call include:

More than 65% of the Fortune 500 now use Azure OpenAI Service.
Azure Arc has 33,000 customers.
"We are seeing an acceleration in the number of large Azure deals from leaders across industries," he said.
Half of Azure AI customers also use Microsoft's data and analytics products.
Microsoft Fabric has more than 11,000 paid cusotmers.
"We're also seeing increased usage and density from early adopters including a nearly 50% increase in the number of copilot assisted interactions per user in teams, bridging group activities and business processes, workflows and enterprise knowledge," he said.
AI features are accelerating LinkedIn's Premium subscription growth.

Hood said commercial remaining performance obligation in the third quarter was $235 billion and 45% of that will be recognized in the next 12 months.

By business:

Microsoft said revenue in productivity and business processes was $19.6 billion, up 13%, with strength in Office 365 Commercial.
LinkedIn revenue was up 10%.
Dynamics product and cloud services sales were up 19%.
Revenue in intelligent cloud was $26.7 billion, up 21% from a year ago.
Windows revenue was up 11%.

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Enterprises Must Now Cultivate a Capable and Diverse AI Model Garden

Dion Hinchcliffe

Vice President and Principal Analyst
Constellation Research

Title: About Dion Hinchcliffe Dion Hinchcliffe is an internationally recognized business strategist, bestselling author, enterprise architect, industry analyst, and noted keynote speaker. He is widely regarded as one of the most influential figures in digital strategy, the future of work, and enterprise IT. He works with the leadership teams of large enterprises as well as software vendors to raise the bar for the art-of-the-possible in their digital capabilities. He is currently Vice President and Principal Analyst at Constellation Research. Dion is also currently an executive fellow at the Tuck School of Business Center for Digital Strategies. He is a globally recognized industry expert on the topics of digital transformation, digital workplace, enterprise collaboration, API…...

My conversations with Chief Information Officers (CIOs) in 2024 continues to show they remain under steady pressure from corporate boards to rapidly harness the strategic potential of Artificial Intelligence (AI) ahead of their competition, while heading off disruption due to the market changes AI is causing. This expectation places a significant burden on CIOs to not only understand and integrate AI into their existing IT portfolios faster than the technology is actually maturing, but also to do so in a way that genuinely meets diverse business demands across departments, divisions, and geographies. The challenge is particularly daunting because no single AI model or vendor offers anything close to a one-size-fits-all solution. Therefore, CIOs must navigate a complex technology landscape, selecting from an array of AI models from a fast-evolving set of offerings that can address specific needs while aligning with the enterprise's overall technology strategy.

Developing a robust AI model portfolio—often visualized these days as a diverse 'garden' of different AI models, large and small—requires capability development, enthusiastic experimentation, thoughtful planning and strategic foresight. AI models not only have to be able to provide good answers, they must also be selected to be cost-effective, as measured by metrics like cost per kilo-inference, as well as technologically sound to integrate seamlessly into the broader IT infrastructure. Additionally, these models must align with strict corporate governance standards, including adherence to centralized IT, AI, and cybersecurity policies, safeguarding of personally identifiable information (PII), and compliance with regulatory requirements. This task involves a delicate balance of technical acumen and strategic management, ensuring each model performs efficiently and ethically within the corporate framework.

To effectively manage this complex integration and ensure AI models deliver tangible business value, the role of the CIO has emerged as virtually the only leader in the organization with the resources, insights, and ability to deliver well on enterprise-wide AI. The CIO must champion the strategic deployment of AI technologies across the enterprise, fostering a culture of innovation while navigating the technical and ethical challenges involved. By effectively overseeing the development of a healthy and effective AI model mix, the CIO ensures that the enterprise not only keeps pace with technological advancements but does so in a way that is both sustainable and aligned with the company's long-term goals. This strategic leadership is essential for translating AI investments into competitive advantages, fulfilling both the board's expectations and the company's operational needs.

Organizations Must Become Fluent in a Wide Variety of AI Models

The single largest differentiator in AI is now the specific model that is used to produce inferences for the business. While the biggest discriminator currently is the size of the model, due to the cost of training and operating them, domain specific models are the next big frontier, especially in high-value use cases in healthcare, finance, and insurance. Organizations must have enough models to meet different business requirements, but not so many they cannot properly oversee or support them. A smart mix of models of various capabilities, with as few gaps as possible, is therefore a top level requirement.

Aspect	Small Models	Medium Models	Large Models
Abilities	Handle specific tasks; limited complexity	Broader capabilities; moderate complexity	Advanced capabilities; high complexity, multimodal
Limitations	Specific, narrow use cases; struggles with complex tasks	Improved generalization but has limitations	Best generalization; can handle very complex reasoning tasks
Use Cases	Mobile apps, embedded systems, simple web services	Consumer applications, enterprise solutions, moderate analytics	Large-scale analytics, high-end services, sophisticated multimodal applications
Range of Parameters	Tens of thousands to a few billion	Few million to tens of billions	A hundred billion to trillions
Typical Hardware	CPUs, low-end GPUs	Mid-range GPUs, TPUs	High-end GPUs, TPUs, specialized AI hardware
Training Cost	Low	Moderate	High
Inference Speed	Fast	Moderate	Can be slower due to model size, depends on optimization
Data Efficiency	Requires less data to train, but less effective with new data	More data-efficient than small models, adaptive	Highly data-efficient, very adaptive, highest zero-shot probability
Flexibility	Low; often task-specific	Moderate flexibility; can adapt to various tasks	High; highly adaptable to new tasks and environments
Cost of 1,000 Inferences	$0.001 - $0.10	$0.10 - $0.50	$0.50 - $10.00
Examples	MobileNet, Orca 2, GPT-J, Phi-3, Falcon 7B	ResNet-50, BERT-Base, T5-XXL	GPT-4, PaLM, LaMBDA, Gemini Ultra, BaGuaLu

Figure 1: The different sizes and capabilities of AI language models today

To successfully navigate the ever-changing landscape of AI requires a robust supporting ModelOps strategy with the accompanying creation of centralized AI model gardens/hubs, which are typically overseen by IT as well as the emerging Chief AI Officer, or whomever is in charge of enterprise-wide AI. This centralized infrastructure is critical for ensuring that AI initiatives are implemented effectively, ethically, and in accordance with regulations. By establishing an overarching ModelOps practice and fostering a collaborative culture around AI development, CIOs can empower their organizations to unlock the transformative potential of AI and achieve significant business value, while providing a central capability to operate, monitor, measure, govern, and secure AI models.

ModelOps: The Engine Room of Enterprise AI

ModelOps encompasses the entire lifecycle of AI models, from development and testing to deployment, monitoring, and governance. A well-defined ModelOps strategy ensures that AI models are operationalized efficiently and effectively. It streamlines workflows, fosters collaboration between data scientists and IT operations teams, and promotes the responsible use of AI.

AI Model Gardens/Hubs: A Single Source of Truth

AI model gardens/hubs serve as centralized repositories for vetted and approved public -- and especially private -- AI models. These hubs provide data scientists and business users with easy access to reusable AI models, reducing redundancy and development time. Additionally, AI model gardens/hubs facilitate governance by ensuring that all deployed models adhere to organizational standards and regulatory requirements.

The Need for a Sustainable AI Capability

The landscape of AI vendors, open source AI projects, and their models is constantly evolving. By building a strong internal capability for ModelOps and AI model management, CIOs can avoid vendor lock-in and ensure that their organizations are not dependent on any single provider. This future-proofs their AI strategy and empowers them to adapt to new technologies and business needs. ModelOps can also ensure the energy consumption of AI models is understood and well-managed, an issue that is gaining visibility and importance rapidly.

The Strategic Imperative for AI Readiness

While the real business impact hasn't even arrived yet, AI has clearly become a strategic technology. By taking a proactive approach to AI readiness and establishing a robust ModelOps practice, CIOs can ready their organizations to seize the transformative potential of AI. This not only positions them to achieve significant financial gains in the medium-term and operational efficiencies right away, but also enables them to deliver a superior customer experience to gain a competitive edge.

Key Components of the ModelOps Enterprise Approach to AI

The visual above depicts a ModelOps enterprise approach to AI, which outlines the various roles and stakeholders involved. Here's a breakdown of the key components and roles involved:

Enterprise AI Owner (CIO, CAIO, CDAO): Provides strategic oversight and leadership for the organization's AI initiatives.
AI Architect: Establishes technical standards and best practices for AI development and deployment.
Local AI Architect or SME: Understands the specific needs of different lines of business and translates them into actionable AI requirements.
Line of Business (LOB): Represents the various business units within the organization that can benefit from AI solutions. Identifies use cases and champions AI adoption within their departments.
Data Management for AI: Ensures that high-quality data is available to train and fuel AI models.
Model Development: Develops, tests, and iterates on AI models to meet specific business needs.
ModelOps Capability: Operationalizes AI models, including deployment, monitoring, and governance.
Governance, Monitoring, and Compliance: Oversees the ethical and responsible use of AI models and ensures adherence to regulations.
DevOps, DataOps: Integrates AI development and operations processes to streamline workflows.
FinOps: Manages the financial costs associated with AI model development, deployment, and maintenance.

While the allure of specific AI models and vendors remains a siren song, even as there are growing worries some organizations won't see immediate returns (which is typical of many advanced technologies), building a sustainable internal capability for ModelOps and AI model management remains crucial for long-term sustainability of the business. Preparing a strong foundation for data, AI models, and associated operations future-proofs the organization's AI strategy, allowing for adaptation to evolving technologies and business needs, ultimately maximizing the return on investment in AI and ensuring its readiness and strategic alignment with an organization's goals as vital opportunities arise.

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