Cisco to cut 5% of workforce, Q3 outlook weak

Larry Dignan

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. ...

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Cisco said it will cut 5% of its global workforce in a bid to "realign the organization and enable further investment in key priority areas." The company outlined the restructuring as it reported its second quarter results.

The networking giant reported second quarter revenue of $12.8 billion, down 6% from a year ago, with earnings of 65 cents a share. Non-GAAP earnings were 87 cents a share.

Wall Street was expecting fiscal second quarter earnings of 84 cents a share on revenue of $12.7 billion. The biggest concern going into Cisco's report was the weak demand for networking gear as customers deploy systems that have been purchased.

For the third quarter, Cisco said revenue will be $12.1 billion to $12.3 billion with non-GAAP earnings between 84 cents a share to 86 cents a share. Analysts had expected adjusted earnings of 92 cents a share on $13.09 billion in sales. For fiscal 2024, Cisco said revenue will be between $51.5 billion to $52.5 billion with non-GAAP earnings of $3.68 a share to $3.74 a share.

Cisco said that it will take charges of $800 million for the layoffs and most of the hits will be recognized in the third quarter.

Chuck Robbins, CEO of Cisco, said the second quarter was solid, but the company has to focus its investments on future growth. "Our innovation sits at the center of an increasingly connected ecosystem and will play a critical role as our customers adopt AI and secure their organizations," he said.

Robbins said:

"We're seeing a greater degree of caution and scrutiny of deals given a high level of uncertainty. As we discussed last quarter, and subsequently saw in other technology provider results, customers are starting to deploy the elevated levels of products shipped in them in recent quarters. This is taking longer than our initial expectations."

Robbins added that telecom and cable companies are pulling back on spending. He said that customers will take another quarter or two to digest shipments already received. "Our team is also partnering closely with customers to assist with this heightened focus on deployments at Cisco equipment on hand," said Robbins. 

The Cisco CEO said he's optimistic about AI workloads boosting demand, but noted that "we're still in the early stages of AI workloads."

Constellation Research analyst Chirag Mehta said Cisco's future growth is pegged the the acquisition of Splunk, which could close in the third quarter.

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Mehta said:

"With the acquisition of Splunk, Cisco is poised to elevate the cybersecurity landscape, potentially merging their respective portfolio of products, and enhance them with advanced AI capabilities. This strategic move not only amplifies Cisco's commitment to cybersecurity but also paves the way for unparalleled benefits to a vast spectrum of its customers. As networks and security converge, Cisco's focus on cybersecurity will be paramount to unlock sustained growth in an ever-evolving digital landscape."

Cisco's second quarter networking revenue was down 12% from a year ago with security and collaboration up 3% each. Observability revenue was up 16%, but from a smaller base. Services revenue was up 4%.

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Cisco’s results land a few days after Arista Networks reported fourth quarter earnings, which were strong. Arista Networks, which counts Meta Platforms and Microsoft as its largest customers, reported fourth quarter revenue of $1.54 billion, up 21% from the same period last year. Net income was $613.6 billion, or $1.92 a share. Non-GAAP earnings were $2.08 a share.

Arista Networks CEO Jayshree Ullal noted that generative AI will create a networking infrastructure upgrade cycle. “AI at scale needs Ethernet at scale. AI workloads cannot tolerate the delays in the network, because the job can only be completed after all flows are successfully delivered to the GPU clusters. All it takes is one culprit of worst-case link to throttle an entire AI workload,” said Ullal.

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How Uber's tech stack, datasets drive AI, experience, growth

Larry Dignan

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. ...

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Uber has leveraged its technology stack and data architecture to create a flywheel that can expand into multiple new markets. Uber is data incorporated and likely a glimpse into future business models.

At  Uber's Investor Day, CEO Dara Khosrowshahi and the company's executive team outlined the company's growth plans, a $7 billion buyback and its data flywheel.

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Here are some key takeaways from Uber's 90-minute session with analysts.

Uber's technology stack is its secret sauce. Khosrowshahi said the technology stack is the company's biggest advantage.

"While it may not always be visible to the casual user or investor, this really is our secret sauce. Whether you're ordering a ride or delivering much of the underlying tech and tech enabled operations, identity maps payments, fraud detection, ordering, dispatching, pricing and more. They're all shared across Uber," he said. "In fact, around 75% of our engineering resources are focused on these shared elements. This advantage is also self-reinforcing as the lessons we learn in one business can be applied elsewhere and technical investments we make in one area accrue to the whole platform."

The tech stack and platform drive the datasets that drive AI. Khosrowshahi said Uber's shared architecture and platform drives the data loop that enables it to build predictive models, personalized offers, generative AI assistants and tools that can be used in new markets including advertising, travel, last mile delivery and enterprise.

Disruptive technologies are nice but keep the business goals in front. Khosrowshahi said the company's strategy is simple: "To build best in class products and then amplify them with the power of the platform."

He added that Uber can acquire customers at a lower cost and generate higher customer lifetime values. "We want to bring in new consumers through our mobility and delivery apps and then convert them into Multi Product consumers both within and across segments," said Khosrowshahi.

Data and customer experience drives multi-product usage. Khosrowshahi said that more than a third of Uber users now use multiple products. "The mathematical advantage for Uber lies in the fact that consumers who use multiple products on average spend 3.4 times more than those who don't," he said. The simple truth is that in the end, math wins, and compounding only amplifies the win."

Khosrowshahi said:

"The more products and services we add to the platform, the more data we have and the more opportunities we have to make that particular pitch really compelling for the consumers at the right time. With the right incentives. And with shared identity and payments across all of our apps we can make it super simple, super easy to move from one app to the other, or one service to the other."

Loyalty programs also play into multi-product usage. Uber One now has 19 million members. Those members not only drive the data flywheel, but also spend more--3x more. 

Simply put, frequency matters and Uber's growth depends on using multiple products and membership.

Khosrowshahi said:

"We have a product team that is focused on driving essentially AI driven offers to put in front of consumers. In the morning it might be coffee. In the evening, it might be dinner. If we see you reserve an on-demand trip to an airport we may say you can reserve next time. All of this is going to be driven by AI so we just have more shots on goal than anyone else."

The platform and data strategy also drives the supply side of the equation. The running theme throughout Uber's Investor Day is that it needs to continue to recruit drivers organically on its own platform on multiple services. "The platform gives us a much more cost-efficient way of finding drivers than through external channels," said Khosrowshahi. "So, for example in the US, converting an existing courier into a driver is about half the cost of finding a new driver through third parties. This is a huge opportunity. In fact today 20% of first time drivers in the US have come from a courier pool."

This organic recruitment across multiple services is critical since Uber is expanding into healthcare transportation as well as a bunch of other areas.

Andrew MacDonald, Senior Vice President of Uber's Mobility and Business Operations, said the driver experience on the platform is critical. "Our tech teams have shipped hundreds of improvements to improve the driver experience," said MacDonald.

Uber's expansion into advertising is just starting. "The power of our advertising platform stems from what Uber users tell us every time they use our apps where they want to go and what they want to get. And as a result, we've got the unique ability to bring together both location based and shopping data with closed loop attribution across our mobility and delivery channels for both performance and brand campaigns," said Khosrowshahi.

In fact, Uber is combining consumer signals with its AI to automate offers on the fly.

Khosrowshahi said Uber will follow the data to balance advertising load and customer experience. "We run a certain percentage of our audience with no ads on a long-term cohort basis. And then we compare that audience behavior to audiences who are receiving ads and we make sure that the experience there isn't significantly different," he said. "You don't want to build an ad business that penalizes the customer experience."

Continuous improvement on costs is the mantra. Uber CFO Prashanth Mahendra-Rajah said data, technology and the platform can be leveraged to drive costs out of the business. He said:

"There's a term in the Uber lexicon called operating cost structure or OCS. OCS is a collection of capturing all costs between revenue and EBITDA. There is a team within Uber to  grind out incremental cost efficiencies out of that OCS. When you think of what's in that cost bucket it breaks down into variable and fixed."

Examples of how the OCS team expands margins include implementing technology to optimize routing, incentivizing customers to use different payment options, reducing fraud costs with data and AI.

Akamai launches Gecko, aims to combine cloud compute, edge networks

Larry Dignan

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. ...

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Akamai announced plans to embed cloud computing across its edge network via an initiative called Gecko (Generalized Edge Compute) in a bid to grab AI inferencing, multiplayer gaming, streaming media, analytics and spatial computing workloads.

The company historically has been known as a content delivery network (CDN) provider but has moved into distributed cloud services. Akamai's bet is that cloud and edge networks will converge over time instead of being treated separately.

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For Akamai, the timing for Gecko is right given that more companies want to bring compute and inferencing to where data resides with lower latency. Given Akamai's distributed content delivery network locations, adding a traditional compute stack to the mix makes sense.

Dr. Tom Leighton, CEO of Akamai, said the Gecko initiative builds on top of the Linode acquisition and its existing distributed cloud network. "With Gecko, we’re furthering that vision by combining the computing power of our cloud platform with the proximity and efficiency of the edge, to put workloads closer to users," said Leighton.

On Akamai's earnings conference call, Leighton said:

"Traditional cloud providers support virtual machines and containers in a relatively small number of core data centers. Gecko is designed to extend this capability to our edge PoPs, bringing full stack computing power to hundreds of previously hard-to-reach locations. Deploying our cloud computing capabilities into Akamai's worldwide edge platform will also enable us to take advantage of existing operational tools, processes, and observability, enabling developers to innovate across the entire continuum of compute and providing a consistent experience from centralized cloud to distributed edge."

As part of the Gecko initiative, Akamai outlined a three-phased plan to embed compute in locations underserved by hyperscale cloud providers. The first nine locations for Gecko include Bogotá, Colombia; Denver, Colorado; Houston, Texas; Hamburg, Germany; and Marseille, France.

Akamai added that it has been conducting early trials of Gecko with enterprise customers and identifying use cases.

Here's what you need to know about Akamai's Gecko effort:

• Akamai plans to bring full-stack computing to its edge locations and leverage the networks tools, processes and observability technologies.
• Developers will be able to build applications for the cloud and edge as the networks and compute converge.
• Gecko's network will be global. Akamai said that it has deployed Gecko-architected regions in Hong Kong SAR; Kuala Lumpur, Malaysia; Querétaro, Mexico; and Johannesburg, South Africa. Santiago, Chile is planned to launch by the end of the first quarter.
• Akamai plans to add hundreds of cities to its cloud computing footprint to build beyond its 10 Gecko locations and existing 25 core computing regions.
• The second phase of the Gecko buildout will add containers to those regions. The third phase will include automated workload orchestration and a consistent user experience across core computing regions and the edge.

Leighton said:

"We accomplished what we set out to achieve last year in terms of infrastructure deployment, product development, jumpstarting our partner ecosystem, onboarding the first mission critical apps from some major enterprise customers, and achieving substantial cost savings as we moved our own applications from hyperscalers to the Akamai Connected Cloud."

Leighton said Akamai's cloud computing business is gaining customers in social media and software. Akamai also plans to be its own customer reference. He added:

"We also derived significant cost savings by migrating several of our own applications from hyperscalers to Akamai Connected Cloud. Our bot manager and enterprise application access solutions were among the first to migrate. Together, these products are used by over 1,000 customers and they generate over $300 million in annual revenue for Akamai."

Separately, Akamai reported its first quarter earnings. The company reported fourth-quarter net income of $1.03 a share on revenue of $995 million, up 7% from a year ago. Security and compute revenue was 61% of total revenue in the quarter. Non-GAAP earnings in the quarter were $1.69 a share.

Akamai's cloud compute business revenue in the fourth quarter was $135 million, up 20% from a year ago. Content delivery revenue was $389 million, down 6% from a year ago, amid traffic declines and large customers that were renewing contracts. Security revenue was $471 million, up 18% from a year ago.

For 2023, Akamai reported net income of $3.52 a share ($6.20 a share non-GAAP) on revenue of $3.81 billion, up 5%.

Constellation Research's take

Constellation Research analyst Holger Mueller said:

"If one was wondering why Akamai acquired Linode in 2022 and Guardicore in 2021 – it is clear – cloud and security revenue were 60% of revenue in 2023  and security revenue was almost 50% of Q4 revenue. Akamai's traditional delivery revenue base declined. Akamai manages to cross sell effectively, adding a better platform for digital experiences beyond content delivery. But growth is pedestrian, and Akamai is one of the very few – if not the only vendor I comment on – who has a higher G&A than S&M and R&D. The question is whether Akamai can grow its content delivery business in 2024 or will it have to rely on security and compute. The Akamai vision remains compelling and offers CxOs an experience platform at the edge that can do it all."

Shopify eyes offline, B2B markets for commerce growth

Larry Dignan

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. ...

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Shopify is best known for its online commerce platform, but future growth is increasingly coming from offline brick-and-mortar merchants and point-of-sale terminals as well as new channels such as business-to-business companies.

Those takeaways were highlighted on Shopify's fourth quarter conference call. For those keeping score at home, "offline" was mentioned seven times on Shopify's earnings conference call. "Generative AI" was mentioned three times although Shopify Magic, the brand for genAI tools at Shopify, was mentioned six times.

Shopify reported fourth quarter revenue of $2.14 billion, up 24% from a year ago, with net income of $657 million. For 2023, Shopify reported revenue of $7.06 billion with net income of $132 million. For the first quarter, Shopify projected revenue growth of the low 20 percent range.

To keep that growth going, Shopify is looking to grab more of the commerce stack and that means moving offline as well as being the mobile payments and online vendor. Shopify launched new point-of-sale (POS) terminals as well subscriptions for brick-and-mortar merchants. The company is also expanding its enterprise footprint.

As a result, Shopify POS delivered offline revenue of $441 million, up 5x from 2019. Shopify added big brands like Carrier, Nike Strength, Banana Republic Home and others to its customer roster.

Harley Finkelstein, President of Shopify, said on the company's conference call:

"In order to discover new customers and build deeper connections with existing ones, you need to be online, offline and everywhere in between. And this is one of our superpowers, and why merchants of all sizes are coming to Shopify to build their own future. Starting with our off-line channel. Our go-to-market efforts, combined with enhancements to our product offering continue to resonate with more merchants that operate both off-line and online presences."

Finkelstein said Shopify is already the e-commerce platform for many big brands but offline operations were elsewhere. As digitally native brands move to physical stores, Shopify becomes the infrastructure. Other merchants are looking to Shopify to consolidate vendors. The goal for Shopify is to be "the unified commerce operating system for merchants whether they come to us to sell online, offline or anywhere in between."

Similar to the offline retail play, Shopify is looking at B2B merchants so it can connect merchants.

"In 2024, we will continue to focus on growing our merchant base by catering to businesses who conduct only B2B transactions that were differentiated B2B offering," said Finkelstein. "We are building on our commitment to help merchants sell to all of their customers from a single unified commerce platform with upgrades to our B2B offering, including headless B2B storefronts, and support for sales reps in the admin, among others, as we look to establish our B2B offering as a leader in commerce."

Shopify said it plans to build out the offline and B2B efforts and target core verticals. Finkelstein said Shopify's headless commerce platform capabilities, composable commerce and broad software stack is also appealing to enterprises.

Add it up and Shopify's battle is likely with vendors focused on smaller businesses such as Square, Toast and Clover. But as Shopify moves up the stack it'll run into Oracle Micros and NCR Voyix.

More commerce:

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Otter.ai's Meeting GenAI aims to turn meetings into knowledge repositories

Larry Dignan

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. ...

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Otter.ai launched Meeting GenAI, a set of tools aimed that mine meetings across multiple platforms to create insights.

The announcement is notable for a few reasons. First, Otter.ai Meeting GenAI can work across multiple collaboration silos such as Microsoft Teams, Google Workspace and Zoom. In addition, Otter.ai's Meeting GenAI play could be a way for enterprises to gauge meeting zeitgeist and form knowledge repositories based on meeting notes and summaries.

The company said Meeting GenAI will be included in its plans ranging from the free Otter Basic plan to the $20 a month per user Otter Business plan.

Meeting GenAI from Otter.ai includes:

• Otter AI Chat across all meetings with the ability to get answers and generate emails and status updates. The big idea behind Meeting GenAI is that it can focus on multiple meetings and leverage past meetings to collect insights from multiple platforms. Otter.ai added that Meeting GenAI will be able to answer questions like what was missed while on vacations. Users will be able to ask questions, generate content and get insights.
• AI Chat in Channels, which will give the ability to chat between Otter AI Chat and team members for alignment. With AI Chat in Channels, Otter.ai is looking to make its assistant a team collaboration tool.
• AI Conversation Summary View, which will identify action items in real time and deliver a narrative summary. The goal with this tool is to eliminate post-meeting confusion while ensuring accountability.

My take

Being able to summarize and transcribe meeting across the enterprise and multiple platforms is a savvy move by Otter.ai because it leans into its biggest advantage—it's vendor and platform agnostic. Tech vendors—and many enterprises—want collaboration on one stack such as Zoom, Google Meet and Teams/Microsoft 365, but the reality is that workers use multiple platforms. The other reality is that we're already overtaxed on meeting so anything I can do to get a cross platform digital twin comes in handy.

I also am an Otter.ai subscriber and it is useful when you have to be in two places at once or two conference calls at same time.

What remains to be seen is whether Otter.ai can convince enterprises that the cross-platform recordings, transcriptions and summaries are worth it. Enterprises are about to become tired of copilot add-ons, costs and overall sprawl. Otter.ai will have to demonstrate returns outside being part of a bundle, but its move to include Meeting GenAI in existing plans is a nice start.

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I will say that some high-level dashboard of all meetings, a word cloud and quick summary could give CEOs and CXOs a quick view of what's happening. The benefits of the new Otter.ai features are also obvious to individual workers. Makes me wonder if we'll all start bringing our own copilots to work.  

Datadog expands large customer base, but outlook falls short for 2024

Larry Dignan

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. ...

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Datadog continues to land large customers with more than $1 million in annual recurring revenue, but the company's outlook for fiscal 2024 and the first quarter was below expectations.

The company, which features a cloud application monitoring and security platform, delivered a strong fourth quarter. The company had 396 customers with more than $1 million in annual recurring revenue, up from 317 a year ago.

Datadog reported fourth quarter earnings of 15 cents a share, or 44 cents on a non-GAAP basis, on revenue of $589.6 million, up 26% from a year ago. For 2023, Datadog delivered revenue of $2.13 billion, up 27% from a year ago, with net income of 14 cents a share.

That solid performance was overshadowed by its 2024 outlook. Datadog projected non-GAAP earnings per share of $1.38 to $1.44, below estimates of $1.78 a share. Datadog said 2024 revenue will range from $2.55 billion to $2.57 billion compared to estimates of $2.59 billion.

Constellation ShortList™ Digital Performance Management

The first quarter guidance was also below expectations with non-GAAP earnings of 33 cents a share to 35 cents a share compared to estimates of 39 cents a share. Datadog projected first quarter revenue of $587 million to $591 million compared to estimates of $586.24 million.

Datadog will hold an Investor Day in New York Feb. 15.

Rootstock's Badarinath on next-gen manufacturing, AI and supply chain

Larry Dignan

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. ...

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The manufacturing industry and its supply chains are being rewired and could ultimately evolve into data-driven signal chains.

Those are some of the takeaways from Raj Badarinath, Chief Marketing & Product Officer at Rootstock.

DisrupTV caught up Badarinath to talk shop. Badarinath has held multiple positions at cloud companies and large enterprise vendors such as PeopleSoft/Oracle, Capgemini and Infosys. Here are the highlights of the conversation.

Disrupting the ERP market. "This is a space--a $57 billion industry--that's begging for disruption," said Badarinath. "There's a market demand because the typical manufacturer put their ERP system in when (CIOs) had their first-born child and now the kid is graduating and they're still on the same ERP. There's a generational shift here with moving to the cloud."

Badarinath said it makes sense for enterprises to have Rootstock sitting natively on top of the Salesforce platform, which powers manufacturing.

Globalization and the supply chain. Badarinath said globalization had been the theme for 50 years and that's ending now. "Re-globalization is what we're looking at now," said Badarinath. "From a US standpoint, there was a pandemic and the supply chain got stuck. There's distrust in a model where China is the single manufacturing hub for the world. There's a China plus one strategy that's going on and that's rewiring and re-globalizing supply chains."

Badarinath added that every state entity is doubling down on investment in the manufacturing sector because it's the bellwether for GDP growth. "This investment is not in your grandfather's manufacturing. We're talking EV technology, renewables, battery technology," said Badarinath. "In the next five years Western countries will be very different from where we were 5 years ago. Manufacturing is not going back to how the world was 20 or 30 years ago."

• Return on Transformation Investments (RTI)

Manufacturing moving from transactions, automation and transportation to insights and signal intelligence. Badarinath said the data value chain in supply chains and manufacturing is critical.

"There is an interesting split in manufacturing right now. The front end of manufacturing has been digitized. The digitization of the demand side has received investment," said Badarinath. "The supply side is still very people oriented, logistics and moving physical atoms. The bits on the demand side are moving faster. What we try to do is build a decision layer on top of the data, collect the data and create signal intelligence."

Badarinath said when the data is in one place, machine learning, generative AI and AI can be used. "When it comes to structured and predictive analytics manufacturers are looking for real ROI use case. We believe that bringing the signals together and creating a decision platform is the future," he said. "The whole process of bringing demand capacity and supply to one platform is what we're calling the signal chain."

AI in manufacturing. Badarinath, who noted that Rootstock's annual conference will spend a lot of time on AI, said the manufacturing industry is methodical and deliberate and AI will be no different. "Manufacturers want to make sure they are looking at all the dimensions before making choices," he said.

Manufacturing's next generation. AI will assume a bigger role in manufacturing because there won't be a choice. Many people with institutional knowledge will be retiring and AI can help bring some of that experience into systems.

Manufacturing will also change because it requires less people due to automation.

"I think manufacturers will have to shift whether they like it or not because markets are changing. ERP will have to be reimagined in the AI era," said Badarinath, who noted that experiential knowledge, historical data and real time signals will all be built in. "We're working with our customers and saying let's build an AI that matters to you. The hype of AI will not work in manufacturing. It has to be real."

Battling giants. Badarinath said Rootstock is battling incumbents and it couldn't be in conversations without being on Salesforce's platform. "People are making decisions on platforms and what platform they trust," he said. "As Salesforce becomes much more well known in the market, the next generation wants something cooler. the idea that we can seamlessly extend the CRM investment into ERP resonates."

Rootstock is starting in the midmarket with plans to go for larger enterprises in the next few years. 

SAP's Supervisory Board to nominate Ala-Pietilä as Plattner successor, Renjen to resign

Larry Dignan

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. ...

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SAP on Sunday shook up its Supervisory Board. SAP's Supervisory Board nominated Pekka Ala-Pietilä to the board as the successor to Chairman Prof. Dr. Hasso Plattner.

SAP and Dr. h.c. Punit Renjen said they have "mutually agreed to part ways because of a difference in perspective on the role of SAP Supervisory Board." In a statement, SAP didn't elaborate on the split. Renjen was designated to assume Plattner's spot on the Supervisory Board.

Ala-Pietilä (right) will stand for election for a two-year term at SAP's annual meeting on May 15. Renjen will officially resign on May 15.

SAP's Supervisory Board swap comes a month after the company added Muhammad Alam to the company's Executive Board to succeed Thomas Saueressig for the company's product engineering. Saueressig will focus on accelerating SAP customers adoption of the cloud.

The Supervisory Board shakeup comes as SAP is going through multiple transitions to move customers to the cloud, popularize AI across its business applications and manage a sometimes vocal user base. 

• SAP retools for generative AI, cuts 8,000 jobs, sets 2024, 2025 ambition
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• SAP buys LeanIX, aims to couple it with Signavio, system transformation
• SAP user group DSAG rips S/4HANA innovation plans, maintenance increases

Ala-Pietilä, 67, has been a member of SAP's Supervisory Board from 2002 to 2021. He was also President of Nokia from 1995 to 2005.

In addition, Ala-Pietilä will bring artificial intelligence heft to SAP's Supervisory Board. He was the Chairman of the EU Commission’s High-Level Expert Group on Artificial Intelligence from 2018-2020 and chair of Finland's Artificial Intelligence Program from 2017-2019.

Plattner, who has been Chairman of SAP's Supervisory Board since 2003, said Ala-Pietilä will bring "vision and a well-measured approach" to ensure SAP's success. Plattner also thanked Renjen for his contributions.

Ala-Pietilä's statement indicated he would be focused on developing and deploying SAP's Business AI.

Constellation Research analyst Holger Mueller said:

"Cultural change is hard for SAP. Outgoing Chairman and partial owner Hasso Plattner wants change but the executives installed tend to run into insourmountable challenges on the cultural side. From Shai Agassi to Vishal Sikka to Jennier Morgan and now Punit Renjen executives are installed as change agents who get initial backing but then overshoot on what is feasible and achievable in a multinational company. Ironically, it is back to Ala-Pietilä, who was passed over before, to start the transition to the post Plattner era."

GenAI trickledown economics: Where the enterprise stands today

Larry Dignan

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. ...

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

Generative AI is supposed to be a boon for vendors, enterprises and overall productivity and efficiency, but so far, the economic benefits have gone to just a few. Simply put, it is very early in tracking the trickledown effects of generative AI, but worth pondering the economic impacts.

There's little doubt that generative AI will have an economic impact both good and bad. Some enterprises are seeing returns as they move from pilots to production. It's no secret that software development is the premier genAI use case. Vendor spoils from generative AI are a bit harder to track because pure plays are hard to find. Beyond Nvidia and Supermicro there have been few generative AI winners among enterprise technology vendors.

Here are the moving parts to ponder regarding trickledown genAI economics.

• Nvidia and Supermicro are clear beneficiaries.
• Other vendors are anticipating demand pops that haven't showed up yet. These vendors often talk about pipelines and interest instead of revenue.
• Hyperscalers such as Microsoft Azure, Google Cloud and Amazon Web Services are seeing sequential revenue gains as the cloud optimization phase ends and genAI gooses workloads.
• Traditional enterprise hardware vendors--Cisco, Dell, HPE--are seeing demand in AI optimized systems as enterprises ponder hybrid workload approaches to large language models (LLMs).
• Component vendors that feed into those AI-optimized systems will see benefits. Think AMD, Intel and Western Digital to name a few.
• SaaS vendors may benefit from generative AI, but CXOs are already pushing back on the copilot upsell. Copilots are boosting SaaS contracts and CXOs will choose what workers get access to generative AI.
• Enterprises are in early stages of genAI deployments and many of them are focusing on the data management and architectures to innovate. As a result, data platforms including Snowflake and Databricks will benefit.

Here's a look generative AI's trickledown economics so far.

Nvidia wins (always).

To say expectations are inflated for Nvidia's fourth quarter earnings on Feb. 21 would be an understatement. However, Nvidia has been able to be at the right place at the right time with the research and development and GPUs for generative AI workloads.

Nvidia is expected to deliver fourth quarter revenue of $20.23 billion with non-GAAP earnings of $4.52 a share. In the third quarter, Nvidia beat revenue estimates by $2 billion. For a bit of perspective, Nvidia quarterly revenue is approaching what it used to put up a year. In fiscal 2023, Nvidia revenue was $26.97 billion.

Until Supermicro crushed estimates with its AI-optimized gear, there weren't any other pure genAI winners. Supermicro's fourth quarter results indicate that there will be other winners. Keep in mind, Supermicro's gains are largely due to Nvidia-powered systems.

You can expect other vendors to start touting generative AI system demand. Dell and HPE have already said pipelines are being built for generative AI systems.

What's unclear at this point is how much of Nvidia's strength will trickle down to server makers. Most of the genAI buildout has revolved around hyperscale cloud players and Meta. Those hyperscale providers buy white box servers.

Rob Mionis, CEO of contract equipment manufacturer Celestica, said on the company's fourth quarter earnings call:

"We're still in the early innings of the upgrade cycle. It just started about a year or so ago. About 50% of any data center needs to be upgraded, I think we're still in the very early innings of that upgrade cycle. It's a multi-year process. It's certainly five plus years."

In addition, networking gear is being upgraded too. Generative AI is also driving upgrades for AI and machine learning workloads.

Meta's capital spending highlights how Nvidia will be poised to win. "By the end of this year, we'll have about 350,000 (Nvidia) H100s, and including other GPUs, which will be around 600,000 H100 equivalents of compute. We're well positioned now because of the lessons that we learned from Reels. We initially underbuilt our GPU clusters for Reels. And when we were going through that, I decided that we should build enough capacity to support both Reels and another Reels-sized AI service that we expected to emerge so we wouldn't be in that situation again," said Meta CEO Mark Zuckerberg.

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The boom is coming for these vendors (maybe).

AMD is seen as a major challenger to Nvidia and Intel will get some portion of the genAI pie too. These vendors see demand coming, but real revenue surges will take time to develop.

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AMD has increased its accelerated computing chip demand forecast from $2 billion to $3.5 billion. AMD CEO Lisa Su said:

"It really is mostly customer demand signals. So as orders have come on books and as we've seen programs moved from, let's call it, pilot programs into full manufacturing programs, we have updated the revenue forecast. As I said earlier, from a supply standpoint, we are planning for success. And so, we worked closely with our supply chain partners to ensure that we can ship more than $3.5 billion, substantially more depending on what customer demand is as we go into the second half of the year."

Arm Holdings appears to be another winner. Arm licenses its designs to chipmakers and CEO Rene Haas said on the company's third quarter earnings conference call that AI driving revenue growth. "We've seen a significant transition now continuing from our v8 product to our v9 product. Our v9 product garners roughly 2x the royalty rate of the equivalent v8 product," he said.

Haas added that Nvidia's Grace Hopper 200 are Arm v9 based as are custom data center chips from Amazon Web Services with Graviton and Microsoft Azure with Cobalt. Haas said:

"There's definitely growth coming from the data center side. So proof points such as Nvidia's Grace Hopper, the Microsoft Cobalt design, the work that AWS has been doing in Graviton. What we are seeing is more and more AI demands in the data center, whether that's around training or inference. And because the Arm solution in the data center, in particular, is extremely good in terms of performance per watt and the constraints that are on today's data center is relative to running these AI workloads puts a huge demand on power, that's a great tailwind for Arm."

Storage is also going to see trickle down generative AI gains.

Western Digital CEO David Goeckeler said on the company's fiscal second quarter earnings call:

"In addition to the recovery in both Flash and HDD markets, we believe storage is entering a multi-year growth period. Generative AI has quickly emerged as yet another growth driver and transformative technology that is reshaping all industries, all companies, and our daily lives...We believe the second wave of generative AI-driven storage deployments will spark a client and consumer device refresh cycle and reaccelerate content growth in PC, smartphone, gaming, and consumer in the coming years. Our Flash portfolio is extremely well positioned to benefit from this emerging secular tailwind."

Goeckeler said to date the investment is being made by hyperscalers for genAI and that'll expand to edge devices moving forward.

Hyperscale cloud players driving demand and will reap rewards.

Microsoft, Alphabet (Google) and Amazon earnings results made it clear that the cloud optimization phase has ended, and generative AI workloads were driving demand.

Amazon CEO Andy Jassy said cloud migrations are picking up again. "If you go to the generic GenAI revenue in absolute numbers, it's a pretty big number, but in the scheme of $100 billion annual revenue run rate it's still relatively small. We really believe we're going to drive 10s of billions of dollars of revenue over the next several years (with GenAI). It's encouraging how fast it's growing, and our offerings really resonate with customers," said Jassy, who noted that Amazon Web Services is preaching model choices to land genAI workloads. Also keep in mind that Amazon is leveraging genAI across its commerce and delivery businesses too.

On a conference call with analysts, Alphabet Sundar Pichai said Google Cloud is seeing strong usage of Vertex AI. "Vertex AI has seen strong adoption with the API requests increasing nearly 6x from the first half to second half last year," said Pichai. Pichai also said Duet AI was boosting productivity.

Microsoft CEO Satya Nadella added on his company’s most recent earnings: "We now have 53,000 Azure AI customers, over a third are new to Azure over the past 12 months."

Direct generative AI revenue for these hyperscalers will be hard to pin down. Why? These cloud providers will likely benefit from the workloads and usage from generative AI, which will drive compute, storage and managed cloud revenue. For instance, enterprises are likely to use custom processor options from AWS and Google Cloud for generative AI workloads that don't require Nvidia's price tag.

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• Microsoft updates Copilot with ChatGPT-4 Turbo
• Microsoft launches Azure Models as a Service
• Microsoft launches AI chips, Copilot Studio at Ignite 2023

Enterprises’ hybrid genAI deployments to benefit legacy providers.

Hyperscale cloud providers won't garner all the genAI revenue. Enterprises are already thinking of small language models, specific use cases and on-premise deployments for security reasons. Cisco, Dell and HPE should all see gains from generative AI deployments via sales of converged and hyperconverged systems.

These traditional vendors will also benefit due to inferencing at the edge.

Comments from these traditional vendors indicate that there will be an on-prem upgrade cycle too.

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Also keep in mind that enterprises eyeing generative AI are likely to turn to their preferred service partners. Accenture and Infosys are just a few services companies citing strong genAI demand. Accenture CEO Julie Sweet noted in December that its GenAI sales in the first quarter were $450 million, up from $300 million three months earlier. Sweet said:

"We are now focusing on helping our clients in 2024 realize value at scale. We are excited about the recent launch of our specialized services to help companies customize and manage foundation models. We're seeing that the true value of generative AI is to deliver on personalization and business relevance. Our clients are going to use an array of models to achieve their business objectives."

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SaaS, data platforms, the copilot game.

Trickle down generative AI economics is clearly making its way through the tech ecosystems for companies involved in the infrastructure buildout and those involved with strategy and expertise. Enterprise software providers will be the battleground to watch.

Before we get into the state of play for software companies, it's worth noting a few winners. Databricks is a winner. Snowflake is a winner. Palantir appears to be landing commercial accounts. MongoDB is surging as developers eye generative AI apps. I can take an educated guess and say ServiceNow and its use-case specific model approach is a winner. Salesforce's Data Cloud looks like a winner. But there are some big questions ahead: How many enterprise software vendors can realistically charge extra for generative AI capabilities? How many copilots am I willing to pay for? Why would I pay extra for what will be a standard feature in a few years? Won't generative AI be table stakes for any application in the future?

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Constellation Research analyst Dion Hinchcliffe noted on the most recent CRTV news segment that CXOs are already pushing back. The pushback is understandable. Simply put, copilots are blowing the budget. Meanwhile, the genAI approaches from Adobe, Workday and Zoom make more sense for customers in the long run.

Sure, Microsoft created a model where there's a $30 per user/month charge for copilot functionality. Some applications--such as GitHub Copilot--justify the upsell due to the returns provided. Other areas are going to be a tougher sell. Should an enterprise forgo the copilot upsell if they can't generate 50% returns? What's the number? And if generative AI drives consumption or economic value for vendors elsewhere (compute, storage, consumption) shouldn't the copilot tax be waived?

• Return on Transformation Investments (RTI)

Multiply that math across the entire software stack and CXOs have budget issues ahead. One thing is clear: Not every software vendor is going to be able to charge extra for a copilot or generative AI feature (even if heavily discounted).

My take: Trickledown economics for GenAI isn't going to make it to all enterprise software vendors. Not every enterprise vendor is going to be a generative AI winner.

From our underwriter

Hitachi Vantara and Cisco launched a suite of hybrid cloud services, Hitachi EverFlex with Cisco Powered Hybrid Cloud, that aims to automate deployments and provide predictive analytics. The combination aims to bring Hitachi Vantara's storage, managed services and hybrid cloud management and integrate them with Cisco's networking and computing stack. Hitachi Vantara and Cisco said customers will see a consistent experience across on-premises and cloud deployments.