Results

AWS Q2 sales growth 17.5%, nears $124 billion annual revenue run rate

AWS Q2 sales growth 17.5%, nears $124 billion annual revenue run rate

Amazon Web Services' revenue in the second quarter jumped 17.5% to $30.9 billion, which is good for an annual revenue run rate approaching $124 billion.

The parent company of AWS reported second quarter net income of $18.2 billion, or $1.68 a share, on revenue of $167.67 billion.

Wall Street was looking for second quarter earnings of $1.33 a share on revenue of $162.09 billion.

Not surprisingly, AWS delivered the most operating income for the parent company. AWS operating income in the second quarter was $10.2 billion. Amazon's North American commerce business delivered second quarter operating income of $7.5 billion on sales of $100.1 billion, up 11%. International operating income in the second quarter of $1.5 billion on revenue of $36.8 billion, up 16% from a year ago.

AWS run rate compares to $50 billion for Google Cloud and $75 billion in annual sales for Microsoft Azure.

CEO Andy Jassy said AI is affecting every part of Amazon's business from Alexa+ to AI models such as Nova and DeepFleet and options on AWS. "Our AI progress across the board continues to improve our customer experiences, speed of innovation, operational efficiency, and business growth," said Jassy.

On a conference call with analysts, Jassy said Amazon will continue to invest to expand its data center infrastructure to meet demand. 

Jassy said electricity and chip availability were big hurdles for building AI infrastructure. 

"I don't believe that we will have fully resolved the amount of capacity we need for the amount of demand that we have in a couple of quarters. I think it will take several quarters, but I do expect that it's going to get better each quarter," said Jassy. 

In the second quarter, Amazon spent $31.4 billion in capital expenditures. That sum is representative of what Amazon will spend in the third quarter. 

Constellation Research analyst Holger Mueller said:

"The questions about whether AWS would slow down are answered. The opposite is the case, and there's likely more growth next quarter as AWS gets momentum with Agent Core, its agentic AI platform. Similarly, AWS' just released S3 vector option is practically a money printing machine. Huge amounts of AI relevant data sit in S3 buckets and can now be leveraged for next-generation AI-powered enterprise applications." 

Key items highlighted included:

As for the outlook, Amazon projected third quarter sales of $174 billion to $179.5 billion, up 10% to 13% from a year ago. Operating income in the third quarter will be between $15.5 billion to $20.5 billion.

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Lessons from Hawaiian startups

Lessons from Hawaiian startups

Constraints lead to innovation, ecosystems matter and artificial intelligence can lead remote startups punch above their weight.

Those are some of the takeaways from a panel at Constellation Research's ARX conference in Honolulu.

The Hawaii startups on the panel included:

Here's a look at the takeaways:

Constraints breed innovation. A common theme from the Hawaii leaders was that you can innovate with constraints. In fact, constraints actually cause innovation. "Constraints breed innovation," said Kitajima. He added:

"Hawaii is the most isolated population in the world, but it's super diversified. We have very limited resources. It's extremely expensive with energy costs. Everything is expensive. But in those kinds of environments, innovation happens. My message to you is that in the future the next big companies may come from places like Hawaii and places you're not expecting."

Hawaii has its own constraints for sure. Companies with many resources also try to manufacture constraints by limiting budget and developing flat structures.

David is the disruptor to Goliath. Lagon said the reason constraints matter is because they inspire creativity and that human element to being an underdog. "You read all these disruption stories," said Lagon. "It's the David, right, not the Goliath. Sometimes if you're too over resourced, then you lose that."

The fundamental science to product innovation continuum. Sullivan's company is focused on fundamental science and deep problems and migrating them to products. Sullivan said he has teams within the company focused on parts of the product cycle. Sullivan's blue team is focused on the science and hard problems with social impact. The green team is focused on processes that take an idea to scale.

Sullivan said:

"Going from the blue zone to the Green Zone is not a straight line. Going from one zone to the next is treacherous journey. It's really difficult. To get a product to market, you got to really shift gears."

Co-development. Sullivan said Oceanit works with universities, governments and large developments. This approach is even more critical given the funding crunch at universities. "University funding pipeline is starting to thin out so we've created a co-development model," said Sullivan. "We build a pipeline based on fundamental science.

Talent challenges with a smaller market. Freese said one of his biggest challenges for his company is finding people to fill roles. Freese's company is using computer vision and AI improve fishery management and ocean conservation and finding expertise in machine learning and AI is a challenge.

However, generative AI and AI agents can fill that void through automation.

Grow an ecosystem. Sullivan said Hawaii has an innovative culture but lacks capital--even though Mark Zuckerberg, Larry Ellison and Marc Benioff--own big chunks of the state. Hawaii also has government ties with the military. "It is a naturally innovative community by any stretch. We excel in innovation, but confidence in ourselves becomes an issue in policy, education and investment," said Sullivan. "What we're trying to do with social engineering is develop talent and create an environment for capital. All of the business CEOs and local businesses have to become part of the solution."

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Tsunami of Insights from #ARX2025 | CRTV Episode 110

Tsunami of Insights from #ARX2025 | CRTV Episode 110

ConstellationTV ep. 110 tunes in LIVE from Turtle Bay in O'ahu during our Analyst Relations Experience (#ARX2025) with co-hosts Larry Dignan and Martin Schneider. Larry interviews CR's Chief Distiller Esteban Kolsky about his new offering, called "The Board", for board members and executives to help them make better decisions. Next, Liz Miller and Holger Mueller joined the hosts to recap the ARX conference.

Despite an imminent (but harmless) tsunami threat 🌊 our analysts unpacked discussions on #agenticAI, the role of AI in changing the pace of innovation, and the future of analyst relations.

Watch the full episode below!

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Meta signals expense surge for AI infrastructure, talent; Q2 shows it can afford it

Meta signals expense surge for AI infrastructure, talent; Q2 shows it can afford it

Meta raised its capital spending outlook for 2025 as it throws money at AI researchers as it chases superintelligence.

The good news for Meta is that it can afford the spending spree.

CEO Mark Zuckerberg riffed on superintelligence and barely defined the term. One thing is clear: Meta has the resources to make a big bet on superintelligence.

Meta said its 2025 capital expenses will be $66 billion to $72 billion. That range is narrowed down from the previous $64 billion to $72 billion range. At the midpoint, capital expenditures in 2025 will be $30 billion higher than a year ago.

The Meta second quarter earnings report landed as Zuckerberg outlined a vision for superintelligence that is personal to individuals, likely powered by smart glasses and definitely not open source.

Zuckerberg said:

"Personal devices like glasses that understand our context because they can see what we see, hear what we hear, and interact with us throughout the day will become our primary computing devices.

We believe the benefits of superintelligence should be shared with the world as broadly as possible. That said, superintelligence will raise novel safety concerns. We'll need to be rigorous about mitigating these risks and careful about what we choose to open source. Still, we believe that building a free society requires that we aim to empower people as much as possible."

Zuckerberg also noted that Meta has the resources to deliver on superintelligence. And you can't argue that point.

In the second quarter, Meta delivered net income of $18.34 billion, or $7.14 a share, on revenue of $47.52 billion, up 22% a year ago. Wall Street was looking for second quarter earnings of $5.92 a share on revenue of $44.8 billion.

The key numbers:

  • Daily active people across Meta properties were 3.48 billion on average for June, up 6% from a year ago.
  • Average price per ad was up 9%.
  • Meta had 75,945 employees as of June 30.
  • Reality Labs lost $4.53 billion in the second quarter.

As for the outlook, Meta is projecting third quarter revenue of $47.5 billion to $50.5 billion. The company said it is early in the planning process for 2026, but expenses will be higher due to infrastructure costs. The second biggest expense driver will be employee compensation for technical talent.

Constellation Research analyst Holger Mueller said:

"Meta is growing nicely and funding what Zuckerberg' superintelligence" efforts. While the previous focus on metaverse was a defensive move. What is clear is that AI has not hurt Meta - with ad impressions and average price per ad growing up. The question will be: How many Zuckerberg ambitions can the Meta business find? We will know soon."

Zuckerberg said the following on Meta's earnings conference call:

  • "I've spent a lot of time building this team this quarter. And the reason that so many people are excited to join is because Meta has all of the ingredients that are required to build leading models and deliver them to billions of people. The people who are joining us are going to have access to unparalleled compute as we build out several multi-gigawatt clusters. Our Prometheus cluster is coming online next year, and we think it's going to be the world's first gigawatt-plus cluster. We're also building out Hyperion, which will be able to scale up to 5 gigawatts over several years, and we have multiple more titan clusters in development as well. We are making all these investments because we have conviction that superintelligence is going to improve every aspect of what we do."
  • "AI is significantly improving our ability to show people content that they're going to find interesting and useful. Advancements in our recommendation systems have improved quality so much that it has led to a 5% increase in time spent on Facebook and 6% on Instagram, just this quarter."
  • "For developing superintelligence, you're not just going to be learning from people because you're trying to build something that is fundamentally smarter than people. So it's going to need to learn how to -- or you're going to need to develop a way for it to be able to improve itself."
  • "For the leading research on superintelligence, you really want the smallest group that can hold the whole thing in their head."
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Microsoft delivers strong Q4, Azure delivers $75 billion in annual revenue

Microsoft delivers strong Q4, Azure delivers $75 billion in annual revenue

Microsoft delivered better-than-expected fourth quarter earnings results as Azure ended the fiscal year with $75 billion in revenue, up 34%.

The company reported fourth quarter net income of $27.2 billion, or $3.65 a share, on revenue of $76.4 billion, up 18% from a year ago.

Wall Street was expecting Microsoft to report fourth quarter earnings of $3.38 a share on revenue of $73.84 billion.

Microsoft also put some numbers on Azure revenue. CEO Satya Nadella said “Azure surpassed $75 billion in revenue, up 34 percent, driven by growth across all workloads.” For comparison, AWS is on an annual run rate of $116 billion in annual revenue. Google Cloud annual revenue run rate is currently at $50 billion

CFO Amy Hook said Microsoft Cloud revenue was strong in the fourth quarter, up 27% to $46.7 billion.

By the numbers:

  • Azure and other cloud services revenue in the fourth quarter surged 39%.
  • Microsoft 365 Commercial Cloud products and cloud services revenue in the fourth quarter was up 16%.
  • Microsoft 365 Consumer products and cloud services revenue was up 21% in the fourth quarter.
  • LinkedIn revenue was up 9% from a year ago.
  • Dynamics product and cloud services revenue was up 18%.
  • For the fiscal year, Microsoft reported net income of $101.8 billion, or $13.64 a share, on revenue of $281.7 billion.

On a conference call with analysts, Nadella said:

  • "Azure surpassed $75 billion in annual revenue, up 34%, driven by growth across all workloads. We continue to lead the AI infrastructure wave and took share every quarter this year. We opened new DCs across 6 continents and now have over 400 data centers across 70 regions."
  • "Every Azure region is now AI-first. All of our regions can now support liquid cooling, increasing the fungibility and the flexibility of our fleet. And we are driving and riding a set of compounding S curves across silicon, systems and models to continuously improve efficiency and performance for our customers."

Constellation Research analyst Holger Mueller said:

"Microsoft is growing well but with three speeds. Intelligent Cloud is striving for 30%, Productivity and Business processes is going for 20% and More Personal Computing is aiming for 10%. Microsoft is translating revenue growth well into profit, as it has  three speeds on cost as well. R&D is about 10% of revenue, sales & marketing is at 5% and Microsoft noticeably reduced general & administrative costs. Now let's see if Microsoft can build on this in its fiscal Q1."

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Palo Alto Networks buys CyberArk in $25 billion bet on identity

Palo Alto Networks buys CyberArk in $25 billion bet on identity

Palo Alto Networks said it will acquire CyberArk in a deal valued at $25 billion, or $45 a share, in a move that'll integrate identity security into its platform.

In a shareholder letter, Palo Alto Networks CEO Nikesh Arora said the deal is part of a plan to double the company's revenue. "After spending many years observing and studying the Identity Security landscape, the time is now to re-shape the $29 billion Identity Security category and lead from the front," said Arora, noting that security and identity technologies will converge.

AlsoPalo Alto Networks acquires Protect AI, aims to secure AI ecosystems

Arora said:

"We are witnessing another inflection point driven by the emergence of AI agents, creating new AI security categories and reshaping the way Identity Security is delivered, with a significant proportion of attacks driven by credential theft, we feel Identity Security needs to change."

Key points of the deal include:

  • Palo Alto Networks enters an identity security market with CyberArk, which has 10,000 customers and $1.32 billion in annual revenue.
  • The deal is a bet that identity and security will converge due to AI agents and the need to secure humans, machines and agents.
  • CyberArk's technology will be integrated into Palo Alto Networks' Strata and Cortex platforms.
  • Palo Alto Networks with CyberArk is a bet that the combined companies can take share in the legacy identity access market.

The deal is expected to close in the second half of Palo Alto Networks' fiscal 2026. Palo Alto Networks said the deal will be accretive to free cash flow in fiscal 2028 following the first full year of the close.

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Writer launches Action Agent as it scales enterprise platform

Writer launches Action Agent as it scales enterprise platform

Writer released Action Agent, an "autonomous AI superagent," that's designed to carry out multi-step work using deep research, computer use and tools with enterprise controls.

Action Agent is in open beta and available for Writer customers with no extra cost. Writer has been building its platform for specialized agents for multiple industries and has a foundational model family called Palmyra.

For enterprise agentic AI, Writer is a company worth following since its models are outperforming generalist large language models. The company, which has a Model Context Protocol (MCP) gateway, has integrations across 80 enterprise systems including Salesforce, Google Workspace and third party data platforms. It has been busy of late. A quick recap:

  • The company named Dan Bikel its first Head of AI. Bikel, an alum at Meta, Google and LinkedIn, will oversee Writer's in-house lab. In his most recent role, Bikel led applied research for AI agents at Meta.
  • Writer released its adaptive reasoning LLM Palmyra X5 model with a 1M context window.
  • The company launched AI HQ, a centralized hub designed to enable enterprises or orchestrate AI agents and workflows. The platform includes Agent Builder. Early beta users include Uber, Salesforce and Franklin Templeton.
  • Writer raised $200 million in Series C capital for a $1.9 billion valuation.

Key points about Action Agent include:

  • Action Agent browses and interacts with web pages, connects to data providers and builds and deploys software.
  • It executes on multi-step plans.
  • Action Agent generates images, visualizations and structured files.
  • It also provides an audit trail.

Holger Mueller, an analyst at Constellation Research, said:

"Writer is nicely evolving its architecture suite going with the evolution of enterprise adoption from specific tools to the ability to connect multiple agents to achieve higher automation levels with an uber agent."

Writer's platform and what you need to know

Action Agent is the latest installment of the Writer platform. Here's what you need to know.

Writer is focused on enterprise AI agents. The company's platform goes from prototype to build to deployment.

Palmyra LLMs. Writer has a family of Palmyra models including X5, its latest reasoning model, and a set of specialized ones focused on medical, finance and creative.

Knowledge Graph, Writer's approach to RAG. Knowledge Graph automatically syncs and updates from data sources.

The ecosystem. Writer has a large ecosystem of systems integrators, cloud, and technology partners including Accenture, KPMG, AWS, and Google Cloud. Its Action Agent will soon be able to connect to over 80+ enterprise apps and third party data sources via a secure MCP server, enabling IT-governed access to 600+ agent tools in total.

Customers. Writer counts multiple Fortune 500 companies as customers including Ally Financial, Uber, Prudential, Kenvue, Marriott and others.

The roadmap. Writer is aiming to develop self-evolving models that can learn from experiences and adjust to feedback from people.

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Figma files for IPO: What you need to know

Figma files for IPO: What you need to know

Figma filed for an initial public offering, named ServiceNow CEO Bill McDermott to its board and highlighted its revenue growth as investors got to see why Adobe wanted to buy the company for $20 billion.

According to a filing with the Securities and Exchange Commission, Figma reported first quarter net income of $44.9 million on revenue of $228.2 million, up 46% from a year ago. As of March 31, Figma had 1,031 customers contributing at least $100,000 per year in revenue. The company announced the IPO filing and McDermott joining the Figma board on its blog.

Figma, which saw a $20 billion acquisition by Adobe fall apart due to regulatory concerns, said it is targeting the teams that bring together applications, websites and digital experiences. What remains to be seen is how Figma winds up competing with Adobe, the company that tried to acquire it.

One thing is clear: Figma, which will trade under the ticker "FIG," is hitting the IPO market at a good time. Coreweave and Circle have had strong debuts. CEO Dylan Field has 56.6 million Class B shares and 51.1% voting power ahead of the IPO. 

Constellation Research analyst Liz Miller said:

"The S-1 is truly the first step in the next chapter for Figma’s growth and diversification as the player to beat in design software, capturing the hearts and minds of users and increasingly, the ecosystems that are dependent on digital product optimization. With this new direction in mind, the IPO and the growth of their board with industry icons like Bill McDermott add to the sheen of Figma’s pride in disruption. This is a team that is focused on bringing disruption to the design market by delivering the modern, digital, collaborative tools that their users dream of, not the tools their users have to learn and muddle through. Take introductions of tools like Figma Slides. On paper, investors may look and say, OK, yeah…slides. But it got users out of their seats craving the innovation, the ease of use and the disruption.

Figma has grown its partnerships and integrations to extend the very idea of creation and collaboration to seamlessly work beyond the confines of Figma. The partnership and native integrations with UserTesting are a great example of this where products can gather realtime user feedback and investigate with a customer panel, update product and push directly to Figma. The integration turns to struggle resolution into an optimization opportunity without pain, friction and multiple team handoffs and timely approval rounds. Other recently unveiled partnerships include folks like HCL TX portfolios. That HCL TX partnership allows web and mobile designers to directly address web and application appearance from within Figma while integrating with HCL Software’s Customer Data Platform to deliver personalized experiences. I’d expect to see more opportunities to extend Figma design and collaboration through partnerships in the future."

Here's what you need to know about Figma.

The price. Figma priced at $33. The range was 37 million shares priced between $30 and $32 each. That range was raised July 28 from $25 and $28 each.

The target market. Figma targets cross-functional teams behind products including designers, product managers, researchers, marketers, writers and a bevy of non-designers. Figma has 13 million active monthly users with 95% of the Fortune 500 using the company's platform.

Revenue growth. Figma said 2024 revenue was $749 million, up 48% from a year ago, with a net loss of $732.1 million.

The product. Figma started as a browser-based design system and has evolved into a broad platform to move products from idea to design to production.

The AI strategy. Figma said in its IPO filing that AI is transforming the product development process. Figma said:

"We believe AI is fundamentally transforming the product development process by making it possible for anyone to quickly turn an idea into a functional prototype, or in some cases, a working product. Figma Make is our product for this new paradigm. Instead of going from idea to wireframe to mock-up to prototype iteratively, Figma Make lets users go directly from prompt to working prototype, at which point they can immediately validate an idea and choose to iterate on it, or discard it altogether. While it’s possible for AI to get to working software with a simple prompt, we believe that the most important differentiator is craft — ensuring that the product looks, feels, and works 'just right.'"

The model. Figma is a software subscription business where access is sold annually or monthly per seat with options for viewers, collaborators, content creators and developers. The sales process is automated and self-serve with starter plans to move users down the funnel. A direct sales team is focused on businesses. Figma said over time it will consider add-on pricing and models based on feature usage.

According to Figma, the seat model is a risk factor as AI is integrated into its software. New pricing and packaging launched in March are another risk factor.

Employees. Figma said it has 1,646 employees as of March 31, up from 1,014 at the end of 2022.

Cloud infrastructure. Figma runs on AWS and the company noted that its hosting costs are expected to increase as its customer base grows.

Named customers. Figma's named customers in the IPO filing includes ServiceNow, Netflix, Stripe and Duolingo among others.

 

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HOT TAKE: NiCE Calls their Shot to the CX Stands with Cognigy Pickup

HOT TAKE: NiCE Calls their Shot to the CX Stands with Cognigy Pickup

When Babe Ruth “called his shot”, the confidence and intention shown captivates fans generations after the Great Bambino gestured to center field. It could be argued that in the NiCE pickup of conversational AI darling Cognigy, the CCaaS leader has called its shot that it intends to soar well beyond the current field of contact center and service. Where the ball lands is still a question, but that isn’t stopping NiCE from taking that swing.

For well over a year, pressure has been building in two distinct directions for both NiCE and Cognigy to make a move. First, CCaaS watchers have been questioning who would pick up the Cognigy jewel for their own service crown. In every analyst summit or event, at least one analyst would muse if NOW was the time to put in a bid for Cognigy. With customers as diverse as Adidas, Lufthansa, Mercedes-Benz, DHL and Bosch, more than one big brain has wondered who could entice Cognigy to make an exit through acquisition.

But more recently, those watching the growth of the AI market have questioned when and if Cognigy would be able break beyond contact center to deliver AI-driven self-service agents for other front line customer experience hot spots like Sales, Commerce and Marketing. But, before we get into that, here’s what we know about the deal so far.

What We Know About the Deal: NiCE has announced a definitive agreement to acquire the German-based AI leader, Cognigy, in a reported $955 Million deal which is expected to close by Q4 2025. NiCE is eager to retain the robust talent base at Cognigy and has an included time-bound hold back and additional depository notes. The deal will be funded with cash on hand and will be subject to standard regulatory approvals in the United States and Germany. While the just-under-a-billion price tag may be eye popping to some, others who have watched Cognigy grow in these past few years will note their healthy revenue growth, with a reported $36 million revenue and 1 thousand global customers in 2024.

What Makes Cognigy so Interesting: Cognigy has long been on Constellation Research’s radar, appearing on the shortlist for Conversational AI Platforms thanks to its low-code agent builder and capacity to develop and deploy automated workflows that integrate quickly into existing systems within the contact center. It also doesn't hurt that Cognigy customers tend to rave about being Cognigy customers. But perhaps more importantly for the future, Cognigy has demonstrated a flexibility and scale, intentionally connecting with number of core systems across the stack.

While the AI model and agent capabilities of Cognigy are wildly interesting, it is actually the data innovation that Cognigy brings to the table that could prove to be the greatest differentiator as the age of Agentic AI moves from experimentation to scale and continuous improvement. As data scarcity takes hold across CX…as agentic AI scales and demands new data structures to feed new knowledge graph centers…technical leaders will be demanding more available, immediately accessible and flexible data . With its Kubernetes based architecture and a flexible data schema on MongoDB, Cognigy has been able to move and scale quickly, seemingly unbothered by document models, data storage structure or rigidity of platform alignments. This is the exact flexibility and scalability that NiCE will need to reshape the data that powers its customer’s AI ambitions.

What makes NiCE so interesting: The acquisition also expands the conversation horizon for partnerships and friends in high places for NiCE who, under the new leadership of CEO Scott Russell, has appeared to be far more vocal and aggressive about its forward looking agenda that picks up and accelerates what Barry Cooper, President of NiCE’s CX Division, et al have long been driving and growing across both CX presence and enterprise recognition. With this move, NiCE arguably solidifies its footing in the enterprise stack, understanding that sometimes you will own the stack, but more often you will need to integrate into and around existing solutions that feel competitive in isolation, but must be aligned and integrated as part of a much, much larger whole. This is, from this vantage point, where others in the contact center market have stumbled in an attempt to own what they not fully be in a position to understand. This acquisition opens the door for NiCE to stand behind that goal of openness as a means to drive enterprise value regardless of stack, tools or competition.

For CX and Strategy Leaders: There are two separated “CX” conversations that will emerge from this acquisition. First is the CX in Contact Center space that will likely feel the impact of this deal first. Like most players in the space, NiCE will need to rise above the functional stack ownership squabbles that can plague vendors and trap them in a cycle of contact center navel gazing. Call it co-opetition, frenemies or best of breed stack building, NiCE and Cognigy will exist in a space where they will thrive through collaboration and coexistence with partners and competitors. For contact center leaders currently leveraging Cognigy in another ACD, WEM or even CCaaS environment, NiCE has committed to continued support and continued investment into Cognigy innovation in a stand-alone offering customers know, trust and rely on for conversational AI demands. The clear message for that customer is that this is not a moment to panic, but rather to scope where and how Cognigy can continue to integrate and connect.

For NiCE customers not currently leveraging Cognigy, the combined offering is on the way that fully integrates Cognigy into the footprint of NiCE CX One and MPower, potentially providing enterprises ready for a more flexible agentic studio to build to their own needs and demands in a low code environment.

Outside of the contact center is where this acquisition gets really interesting…especially for those organizations that are, say, ServiceNow customers or on AWS. With the flurry of partnership expansions and announcements that have come from NiCE since the start of the year, it isn’t hard to see where teamwork makes the dream work for expanded CX applications beyond contact center and support. Cognigy brings existing use cases in sales and revenue, envisioning unified customer engagement that spans the entirety of the CX landscape from sales, marketing and commerce back to service and contact center. Customers have already started to build connected and smart automations, workflows and self-service agentic flows. The challenge will be introducing solutions like NiCE to a new C-Suite buyer not aware of contact center tools and solutions.

Parting Thoughts: The fact that both organizations feel this wasn’t just a technology fit but a joining of two cultures where both teams see extreme value in the other (in a private analyst-only briefing involving NiCE and Cognigy executives, Philipp Heltewig, Cognigy’s co-founder and CEO, noted that the culture fit is so strong, that he believes no other offer “even at 20% more” could have unseated NiCE) speaks more to a clear understanding from both leaders that the status quo in contact center is changing. As noted in blogs and random rantings and ravings made before, this analyst firmly believes that a convergence is afoot that will quickly separate the functional tools (as excellent as they may be) from the solutions that become core to a much more comprehensive and growth-driving experience stack that worries less about ownership, control and functional isolation and far more about the ability to deliver on the enterprisewide strategy known as customer experience.

At Constellation we clearly define CX as the intentional enterprisewide team sport that purposefully builds, grows and proactively maintains durable, profitable relationships with its customers. In this age of agentic AI, there is a new imperative to ensure that this cross functional team sport called CX is underpinned by an enterprisewide capability to deliver proactive, reactive and ambient experiences. This will require different systems of data, knowledge and engagement to win the day. NiCE has just shown us where they intend to lead and head. And who knows where this development and partnership journey could shift next…perhaps announcements with more enterprise level CX players like Adobe or Oracle? The combinations and permutations are endless, but to stake their claim in enterprise CX, understanding the power of openness, data and connected workflows and autonomous agents is one heck of a way to say you have arrived.

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DigitalOcean CEO Paddy Srinivasan on AI natives, inferencing, agentic AI adoption

DigitalOcean CEO Paddy Srinivasan on AI natives, inferencing, agentic AI adoption

DigitalOcean CEO Paddy Srinivasan said AI inferencing is becoming the dominant workload at the cloud provider, AI native companies have the potential to scale faster and leaner than ever before and agentic AI is in the early innings with a lot of loose ends to tie up.

Those are some of the takeaways from my conversation with Srinivasan. DigitalOcean has more than 640,000 paying customers and most of them are developers. In terms of enterprise reach, Srinivasan said the company is focused on digital native enterprises as well as AI native startups.

Srinivasan said the goal is to make AI infrastructure simple with transparent pricing.

Here are some of the takeaways from our chat.

The DigitalOcean stack. Srinivasan said the company has three layers with infrastructure featuring AMD and Nvidia GPUs, its Gradient AI Platform that provides middleware and building blocks for AI and a set of agents to provide an automated experience.

Focus on AI inferencing. "DigitalOcean focuses on inferencing, which requires a different configuration of GPUs, networking, storage, and compute resources compared to training," said Srinivasan. The CEO noted that the "half-life of GPUs for inferencing is longer, allowing for more diversity in GPU types."

The customer base. Srinivasan said there are two types of companies--AI native and digitally native. "AI natives are being born in AI to solve a problem using AI as the centerpiece," he said. "Everyone else is digital native trying to use AI as part of an existing application stack."

The needs of these two types of customers are quite different. AI natives are looking to build models or extend them or optimizing them for use cases and to solve business problems. "AI natives need inferencing at scale. They'll take a model, optimize it, run it on an infrastructure that is optimized for AI," said Srinivasan.

Digital natives have an existing business that's viable and are now trying to add AI. These companies are looking to introduce agents and use AI in workflows.

Srinivasan said:

"Digital natives need a platform that not only has LLMs, but also a variety of different ways to inject the data, build agents and evaluate them. They need the ability to version these agents and so forth. When the agents are running, you need the ability to have direct insights on how the agent is working. That's the platform layer."

AI native companies and scale. Srinivasan said AI native companies are scaling rapidly with far fewer developers and humans required. AI natives can also be disruptive in that they think different and are likely to upend traditional user interfaces. "They're scaling faster and they're scaling leaner in as an AI native company," he said.

Agentic AI implementations. DigitalOcean has released an SRE agent that "can triangulate logs, identify the source of issues, and even pinpoint the specific line of code causing problems." Srinivasan explained how this is "reimagining the developer's interaction with the cloud." In the end, DigitalOcean is trying to eliminate complexity for itself and the customer.

AI agent adoption. Srinivasan said the current stage of agentic AI is "between the top of the first inning and bottom of the second inning." Adoption will happen faster than previous technology cycles, but there's a lot of key parts still being defined in the enterprise.

LLM commoditization. Srinivasan views "the commoditization of LLMs as a catalyst for the industry, driving down the total cost of ownership and increasing adoption." He noted that "open-source LLMs have caught up with closed-source models for general-purpose inferencing needs." More: The rise of good enough LLMs

 

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