Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Alibaba's first quarter revenue fell 4% to $33.47 billion amid tough e-commerce competition, but its cloud business picked up momentum.

The company reported first quarter earnings of $3.34 billion, down 27% from a year ago. Alibaba is facing competition from rivals such as PDD and JD.com. Alibaba has had success with its Alibaba International Digital Commerce (AIDC) business, which includes retail and wholesale marketplaces including Lazada, AliExpress, Trendyol, Daraz, Miravia and Alibaba.com. First quarter revenue for AIDC was up 32% from a year ago.

Even though Alibaba's various e-commerce businesses were mixed the Cloud Intelligence Group gained a bit of momentum after a few quarters of flat growth. First quarter revenue for the cloud unit was $3.65 billion, up 6% from a year ago.

Alibaba said it saw "double-digit public cloud growth and increasing adoption of AI-related products." AI-related product revenue grew at a triple-digit pace. The company said it "will continue to invest in customers and technology, particularly in AI infrastructure, to increase cloud adoption for AI and maintain our market leadership."

In the quarter, Alibaba released Qwen 2.0, a series of large language models.

Alibaba CEO Eddie Wu said:

"In our cloud segment, we continue to pursue high-quality revenue and effectively execute our integrated cloud plus AI development strategy. This quarter, Alibaba's overall revenue, excluding Alibaba consolidated subsidiaries, grew 6%, with public cloud revenue maintaining double-digit growth. AI-related product revenues sustained a triple digit growth continuing to increase its share of public cloud revenue. We're seeing more major customers choosing Alibaba Cloud as their computer infrastructure for AI development. At the same time, Alibaba's proprietary large language models are gaining wider adoption.

We'll strengthen synergies between cloud and AI products, not only supporting existing customers and implementing new AI capabilities on Alibaba Cloud, but also enabling AI native enterprises to scale and succeed on our platform. We're committed to capitalizing on both opportunities.

Three, we'll continue to invest in R&D and AI CapEx to ensure the growth of our AI-driven cloud business."

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Cisco reported better-than-expected fourth quarter results, but networking revenue was down 28% from a year ago. Cisco also said it will cut 7% of its global workforce and take pre-tax charges of $1 billion with most of that sum recognized in the first quarter. 

The company reported fourth-quarter earnings of 54 cents a share on revenue of $13.6 billion, down 10% from a year ago. Non-GAAP earnings were 87 cents a share.

Wall Street was expecting Cisco to report non-GAAP fourth quarter earnings of 85 cents a share on revenue of $13.54 billion.

For fiscal 2024, Cisco reported earnings of $2.54 a share on revenue of $53.8 billion, down 6% from a year ago.

As for the outlook, Cisco projected first quarter revenue of $13.65 billion to $13.85 billion with non-GAAP earnings of 86 cents a share to 88 cents a share. Fiscal 2025 revenue will be between $55 billion to $56.2 billion with non-GAAP earnings of $3.52 a share to $3.58 a share.

• Cisco and Nvidia: Networking partners or frenemies?
• Cisco rolls out AI infrastructure with Nvidia, aims to track generative AI deployments
• Cisco closes Splunk purchase, previews integrations ahead
• Cisco Q3, Q4 outlook better as company preps Splunk integration

The company's results have been boosted by the acquisition of Splunk with total fiscal year subscription revenue of $27.4 billion, or 51% of total revenue. Annualized recurring revenue ended the fiscal year at $29.6 billion, including $4.3 billion from Splunk.

CEO Chuck Robbins said customers' are through with their indigestion with networking gear and demand was balanced. 

"Across the technology portfolio, demand was incredibly balanced. We saw a double digit growth in security, double digit growth in collaboration, and then in the networking space. The switching and the enterprise routing businesses were both high single digit growth. And the wireless business was up double digits. 

Enterprise customers are now actually upgrading their infrastructure in preparation for AI. And in some cases, they're taking some of the dollars that they've set aside for AI to actually spend it on modernizing their infrastructure to get ready for that." 

By the numbers:

• Networking revenue for the fourth quarter was $6.8 billion, down 28% from a year ago.
• Security revenue in the fourth quarter was $1.787 billion, up 81%, due to Splunk.
• Observability revenue was $258 million, up 41% from a year ago, due to Splunk.
• Collaboration revenue was flat for the fourth quarter at $1.02 billion.
• Services revenue in the fourth quarter was $3.78 billion, up 6% from a year ago.

Speaking on an earnings conference call, Robbins said the company saw a strong close to the quarter. Cisco also named Jeetu Patel chief product officer overseeing Cisco and Splunk products.

"Our products will come together in a more integrated way than ever before, positioning us to deliver incredibly powerful outcomes for our customers," said Robbins. "Looking ahead, we remain laser focused on growth and consistent execution as we invest within an AI cloud and cybersecurity to focus on these key priority areas."

Robbins noted:

• The company saw strong product growth even with "persistent macro uncertainty."
• Public sector demand was "particularly strong" driven by federal spending in the US.
• "We signed several $100 million plus transactions in the quarter with global enterprises who are leveraging the breadth of our technology platforms to modernize and automate their network operations and deploy next generation machine learning and AI applications."
• "In our networking portfolio, data center switching also saw double digit product order growth and enterprise routing, campus switching and wireless orders were also strong."
• "We have now crossed $1 billion in AI orders with web scale customers."
• "Three of the top four hyperscalers are deploying our Ethernet AI fabric, leveraging Cisco validated designs for AI infrastructure. We expect an additional $1 billion in AI product orders in fiscal year '25."

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Sakana AI, an artificial intelligence startup based in Tokyo, has launched a new generative AI model called AI Scientist that aims to automate scientific research discovery processes.

In a blog post, Sakana AI said The AI Scientist enables large language models (LLMs) to perform research independently. Sakana AI and researchers from the University of Oxford and University of British Columbia released a paper on The AI Scientist.

The paper outlines an AI-driven system for automated scientific discovery for machine learning that includes generating novel research ideas, writing code, executing experiments, summarizing results and presenting findings via text and visualizations.

Constellation ShortListâ„¢ Pre-Built Large Language Models For Generative AI

Sakana AI also said The AI Scientist has an automated peer review process to evaluate papers, write feedback and improve results. The process can be repeated to develop ideas.

According to Sakana AI, The AI Scientist is also designed to be compute efficient with a full paper delivered at about $15 per paper. There are flaws in the first version of papers, but the initial demonstration highlights how LLMs can be applied to scientific discovery.

Here's an overview of how The AI Scientist works.

The AI Scientist has a bevy of limitations in that it needs an existing code base as a starting point, needs computer vision and can be prone to errors. Nonetheless, the initial paper on The AI Scientist highlights an interesting use case.

Constellation Research analyst Holger Mueller said:

"Science is an interesting use case, as it lends itself very well to AI. There's plenty of public data out there, and the profession follows a rigid standardized methodology to come to scientific insights. Similarly, the process of validating and challenging insights is highly structured. Of course, anybody trying to automate scientists will face a lot of scrutiny, and we'll have to prove that it can deliver value of a daily scientist. Research. Human supervision will of course be a critical aspect, but from its overall characteristics, science lends itself very well to be automated by artificial intelligence."

Related:

• Enterprises start to harvest AI-driven exponential efficiency efforts
• GenAI may be the new UI for enterprise software
• Education tech in turmoil amid genAI: Why consolidation is next
• 14 takeaways from genAI initiatives midway through 2024

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Google's Made by Google event featured Pixel 9 devices with a heavy dose of its Gemini models as well as its tech stack that extends from cloud to edge.

Yes, Google's event was technically about devices, better cameras and other features, the main event really revolved around genAI and natural language experiences.

I'm pondering the implications of Google's Pixel event in terms of that genAI as UI theme. Will this experience melding happen on devices first and then extend to enterprise software? It remains to be seen, but as Airbnb recently noted: The phone UI hasn't changed a whole lot even with genAI.

• Airbnb CEO: GenAI's impact on app experiences minimal so far
• Enterprise software vendors shift genAI narrative: 'GenAI is just software'
• Uber Q2 boosted by algorithm improvements
• Rocket Companies Q2 highlights genAI, AI returns
• GenAI may be the new UI for enterprise software

Rick Osterloh, Senior Vice President of Platforms and Devices, outlined the Google AI stack and vision. 

"We're fully in the Gemini era, with AI infused into almost everything we're doing at Google across our full tech stack. Our integrated AI strategy means we're in control of where we're heading. 

We're innovating with AI at every layer of the tech stack, from the infrastructure and the foundation models to the OS and devices and the apps and services you use every day. It's a complete end to end experience."

The other theme here with Google's Pixel event is the integration of Android and Gemini. The upshot here is that Google wants to charge you for advanced Gemini feature. The problem is users can wind up with AI sprawl. GenAI isn't a streaming service. Do you roll with Gemini or OpenAI?

Here are the moving parts from the Made by Google launch:

• Gemini Live will roll out to Gemini Advanced subscribers. Gemini will give a natural language window into multiple applications including Keep, Tasks, Utilities and YouTube Music as well as Gmail and Calendar. Gemini will also pull in context as needed. The only catch here is that Gemini works with Google apps, but ultimately needs to extend into third party apps too.
• Pixel 9 devices will be powered by Google's Tensor G4 silicon that was designed with Google DeepMind. The Tensor G4 will run Gemini Nano with Multimodality so the phone can understand text, images and audio. Google also launched the Pixel 9 Pro Fold. Pixel 9 devices start at $799.
• For now, Google is layering Gemini and genAI throughout its devices and features ranging from camera to Pixel Studio to Circle to Search.

Add it up and Google's Pixel event is notable as a comparison to Apple Intelligence. Neither has leveraged genAI to change the mobile UI paradigm, but consider the Pixel launch more like a first installment. The larger story over time will be Pixel as edge device and the leverage Google has with its tech stack. 

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Generative AI projects largely depend on change management and culture to move from pilot to production, according to BT150 members. In addition, performance management is an area where AI could be a big help.

Those are some of the takeaways from Constellation Research's August BT150 meetup.

Generative AI, budgets and proof-of-concepts. With 2024 nearly 75% complete, the jury is still out on generative AI proof-of-concepts getting to production. Anecdotally, the lack of returns for many genAI projects is an issue since AI took funds from other critical enterprise projects.

One issue for genAI projects is that there are vanity proof-of-concept genAI projects. These projects were championed more for resume building than use cases. In addition, many companies have lacked the data strategy to execute well on AI.

BT150 members noted that in their experience culture for change and change management was a more important indicator of success than vendors, models and other technology.

BT150 recaps

• BT150 zeitgeist: Dear SaaS vendors: Your customers are pissed
• BT15O CXO zeitgeist: Vendor procurement, cool projects and margin compression
• BT150 CXO zeitgeist: Data lakehouses, large models vs. small, genAI hype vs. reality
• BT150 CXO zeitgeist: Low marks for SAP RISE, process automation, change management, AI risk
• BT150 CXO zeitgeist: AI trust, AI pilots to projects, VMware angst, projects ahead
• BT150 nominations | 2024-2025 class

A BT150 member said AI-driven management coaching software has been helpful to employees and has boosted performance at a double digits clip. This bot works though scenarios and difficult conversations. The upshot is that one return from AI products may be behavior change and thinking through scenarios differently. CxOs could start thinking in terms of AI apps beyond just doing tasks faster.

Performance management software is a tough sell even if it has returns. "Performance management is highly relevant at this time; however, time are tough, the economy sucks and no want wants to pay for anything," said one BT150 member. "Everyone realizes there's a huge problem with performance management, but not one use case was allowed to go forward. Our use cases are either things that save money, increase margin or are customer facing."

Perhaps the biggest issue is that enterprises can't measure the ROI for effectiveness well. Instead, enterprises fall into the trap of efficiency where cuts are more important than effectiveness.

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

Enterprise software could become disrupted as new AI and data driven entrants smell opportunity by either serving as an overlay to the acronym-laden soup of systems or replacing them.

The disgruntlement with enterprise software has been brewing throughout 2024. Forced migrations, multi-cloud cross-selling, copilot upcharges and lack of value are forcing the issue.

Here's a look at the themes bubbling up in 2024.

• Enterprise software customers are annoyed with their vendors.
• Customers need more value but they are mostly seeing cross-sells designed to raise annual contract value for vendors.
• Migrations and upgrades are challenging and a hard sell due to the lack of value. The enterprise software ecosystem is using the same integrator heavy playbook even as they allegedly simplify with AI.
• GenAI may be the new UI, but enterprises are trying to sell you copilot silos.
• Enterprise software vendors are mostly data stores with a UI and could be relegated to systems of record. Vendors will move to keep your data on their respective platforms.
• Workflows are the new battleground.
• There's a need for platforms that can bring automation, AI and process intelligence to legacy investments.

Add it up and I can only conclude that the table is set for disruption. If this were Game of Thrones, you could say winter is coming for enterprise software.

Palantir this week outlined Warp Speed, built on the company's Artificial Intelligence Platform (AIP), to target manufacturers. The Palantir argument is that traditional ERP systems were built for the CFO and don't support modern requirements.

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

Speaking on Palantir's second quarter earnings conference call, CTO Shyam Sankar said the US needs to reindustrialize, but is hampered by outdated enterprise systems. It's why SpaceX and Tesla have built their own ERP systems. The new reality also means companies like Rivian and Uber build more of their enterprise software than buy it.

"Warp Speed, built on AIP, on our industrial AI, and with ontology is the modern American manufacturing operating system that reimagines how to bend atoms better with bits," said Sankar. "Warp Speed is conceived as an operating system for the modern American manufacturer. It touches not just ERP, but MES (manufacturing execution system), PLM (product lifecycle management), PLCs (programmable logic controllers) and it's interacting with the factory floor. We think there's an opportunity to reimagine this."

Sankar said Warp Speed will appeal to next-gen manufacturers instead of large ones that have already bet on legacy systems. These next-generation enterprises are focused on leveraging data for competitive advantage. "It's not actually believable that the same ERP manufacturing system that you use to build rockets is what you'd use to build rackets," said Sankar.

Manufacturers need to be thinking about driving value instead of creating services revenue for systems integrators, said Sankar. "We live in a world today where it's $1 of license for $9 of implementation that never seems to quite work," he added.

Palantir appears to be focused on next-gen manufacturers so it's not quite taking SAP head on. However, SAP is hellbent on being more than a system of record and said Business AI is driving deals. The real battle in the future between companies like Palantir and SAP will revolve around data ontologies.

Speaking at C3 AI's annual Transform conference in March, CEO Thomas Siebel said AI can make enterprise systems more valuable, by riding on top of them. Siebel noted that the enterprise software stack needs to be reinvented, but it won't be by replacing systems. "No way no how is SAP going away. Sorry, the bad news is SAP isn't going away," said Siebel.

"Your accounting software and HR software isn't going away. It's not a replacement market for the entire enterprise application software stack. But generative AI certainly does change the entire enterprise application software stack."

Siebel said that if you fast forward to 2027 no CEO or CFO is going to survive without predictive insights from AI-driven systems. Siebel was more in the genAI as UI camp. He noted that the human-computer interface is going to be reshaped by genAI and replace the crappy user experience that hasn't changed in enterprise software for more than two decades.

My take

As with everything in life, your view of this issue will depend on timeline. It's clear to me that enterprise software is hitting a wall on multiple fronts and the tension between margin compression and margin preservation is palpable.

In the short term (3 years), it's highly likely enterprises are stuck with what they have. If you can't migrate off of VMware just remember how hard it is to migrate off your legacy systems. As a result, the plan for many enterprises is to box these systems out with an abstraction layer.

That abstraction layer--facilitated by generative AI as a front end--will give enterprises more control over the UI and leverage some of that internal data Palantir's Sankar was talking about. Some companies, especially the next-generation of enterprises that build over buy, will get to that genAI as UI moment quickly. Others will need to develop their data ground games and move more into generative AI.

One note about the abstraction layer with genAI as the new UI is that it may take a while.

Palantir and C3 AI are coming at the enterprise disruption from a big data and AI perspective. ServiceNow can also be an abstraction layer and has invested more in process intelligence.

"Our Gen AI strategy is focused on infusing intelligence into the flow of work, end-to-end across the enterprise, every department, every persona," said ServiceNow CEO Bill McDermott on the company's latest earnings call. "With our native integrations, we already help people orchestrate across different systems and data sources. Now we can train the machines to do the low value work so the people can up level to the knowledge work."

Beyond the next three years, Siebel's vision about enterprise software makes sense. Enterprise software has become stagnant and more about sales playbooks than value. What is missing from many potential disruptors is the process intelligence and automation that would go with the AI and data.

Here are a few developments to watch as this enterprise software disruption theme plays out:

• Data lock-in. Every cloud vendor and enterprise software provider wants your data on their platform. Your data has been in play for years. Yes, SAP wants to keep you on its data ontology. Salesforce wants you in Data Cloud. And guess what? Palantir and C3 will want your data on their ontologies too. A neutral vendor in your stack is great, but also a pipe dream
• How does this enterprise software disruption play out for data platforms? Enterprises are more likely to consider Databricks and Snowflake as their platforms to remain agile. Both have pushed open source and visions to ensure customers keep their data.
• Vendors without your data may become critical. ServiceNow automates your workflows, but doesn't have your data. That reality may become more critical to enterprise technology strategy.
• M&A. Look for a series of acquisitions revolving around orchestration, process automation and automation. Some vendors will have to buy their way into this abstraction layer concept.

Insights archive

• Enterprise software vendors shift genAI narrative: 'GenAI is just software'
• The generative AI buildout, overcapacity and what history tells us
• Enterprises start to harvest AI-driven exponential efficiency efforts
• GenAI may be the new UI for enterprise software
• Education tech in turmoil amid genAI: Why consolidation is next
• 14 takeaways from genAI initiatives midway through 2024
• OpenAI and Microsoft: Symbiotic or future frenemies?
• AI infrastructure is the new innovation hotbed with smartphone-like release cadence
• Don't forget the non-technical, human costs to generative AI projects
• GenAI boom eludes enterprise software...for now
• The real reason Windows AI PCs will be interesting
• Copilot, genAI agent implementations are about to get complicated
• Generative AI spending will move beyond the IT budget
• Financial services firms see genAI use cases leading to efficiency boom

Larry Dignan

Editor in Chief of Constellation Insights Constellation Research

About Larry Dignan: Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning. He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.&nbsp; <br>Constellation Insights does the following: Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and… Read more

DigitalOcean Holdings' strong second quarter results highlight how a new breed of cloud compute providers are gaining traction due to AI workloads and access to Nvidia GPUs.

On a conference call with analysts, CEO Paddy Srinivasan said annual recurring revenue for AI and machine learning products are up more than 200% year over year with help from the Paperspace acquisition a year ago. DigitalOcean also saw revenue contributions from managed hosting as well as new customers.

In its second quarter, DigitalOcean reported net income of $19 million, or 20 cents a share, on revenue of $192 million, up 13% from a year ago. DigitalOcean as well as providers like CoreWeave are increasingly gaining cloud traction for AI workloads. In addition, bitcoin mining companies, notably Core Scientific, that have data centers with GPUs are also gunning for AI workloads to expand.

• Equinix, Digital Realty: AI workloads to pick up cloud baton amid data center boom
• The generative AI buildout, overcapacity and what history tells us
• AI infrastructure is the new innovation hotbed with smartphone-like release cadence

Constellation Research analyst Holger Mueller said DigitalOcean is on the right path. He said:

"Boutique cloud provider Digital Ocean had another good quarter, growing 13%, fueled by innovation, access to Nvidia GPUs, Xeon compute and more. More impressive is that Paddy Srinivasan and team have turned the ship towards profit, a nearly $70 million net swing from a loss to a profit of $33 million in the first half of the 2024. Digital Ocean now manages to turn $1 out of $20 of revenue into net income. Not a bad turnaround – and definitely what investors want to see."

DigitalOcean has launched "GPU droplets" that allow customers to slice Nvidia H100 instances by 1, 8 or more GPUs, use case and budget limitations. The company also launched global load balancers as well as managed OpenSearch. DigitalOcean said 2024 revenue will be about $770 million to $775 million. To scale, DigitalOcean has hired a Chief Product and Technology Officer, Chief Ecosystem and Growth Officer and Chief Revenue Officer in recent weeks.

Srinivasan said:

"We continue to see very strong demand for our AI platform. To support that growing demand and to take the first step of our long-term data center optimization strategy, I'm very excited to announce that we will be opening a new state-of-the-art data center in Atlanta in Q1 of 2025.

This not only expands our geographic footprint, providing us cost effective additional coverage across the U.S. for our core workloads, but also gives us near term incremental space and power to support our AI strategy and growth."

The plan going forward for DigitalOcean is to provide easy access genAI and AI infrastructure similar to the way cloud computing did. That vision will also require a focus on software too, said Srinivasan. "Our longer-term AI vision is more software-centric, with the mission of making it easy for our approximately 638,000 current customers and other companies that look like them to leverage AI in their application stack without needing super deep AI and machine learning expertise," he said.

DigitalOcean's big bet is that AI instances focused on business value can be a long-term winner as the customer base shifts from large foundational model players. AI model builders and consumers will all have different requirements that need GPU capacity at different levels.

"Our AI strategy, which includes the GPU infrastructure, is tailor made for customers that are looking to consume AI, not necessarily build foundational models. When I talked about the GPU droplets, that's an abstracted version of the core GPU as a service," said Srinivasan. "We feel our strategy is going more up stack and enabling applications that derive business value from AI rather than focusing on model builders that are building and training foundational models. So, there's going to be different needs for customers that are looking to derive business value and build applications and platforms on top of our infrastructure."

Srinivasan added that today's generative AI cloud infrastructure market is all about Nvidia-powered instances used by foundational model builders. The next layer is going to be important too.

"The true business value is going to be when this infrastructure is leveraged to build platforms like simple example would be operating systems based on x86 architecture. And then you have applications, which are the ones that truly deliver business value for everyone. This AI wave goes up stack from one layer to the other, we feel there's a tremendous amount of need to democratize the access to these GPUs and also provide other software frameworks," said Srinivasan.