IFS teams up with Anthropic, Siemens, Boston Dynamics, 1X, showcases industrial AI use cases

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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IFS CEO Mark Moffat said industries need to embed AI into physical operations in manufacturing, utilities, defense systems and supply chains to better compete globally and become autonomous. IFS launched new AI-powered products as well as partnerships with Anthropic, Siemens, Boston Dynamics and 1X Technologies. 

Speaking at IFS' Industrial X Unleased conference in New York, Moffat outlined a vision of Industrial AI -- Applied. "We're at a fork in the road," said Moffat. "This isn't a tech cycle. It's a fundamental choice about whether AI becomes the backbone of the industries that power our world or remains a toy."

Moffat said the $10 trillion flowing into industrial infrastructure and assets is a positive, but there's a gap because little of those funds are going into applying knowhow about running a factory, fixing a turbine or preventing a wildfire. Moffat said IFS is looking to be the control layer that connects industry infrastructure and AI.

"Generic AI misses the context and deep understanding of the physical world," said Moffat, who laid out the IFS strategy combining AI, enterprise applications and processes and industrial use cases.

Moffat said:

"We're moving from demos and brochures and marketing into as my son would call it, IRL, in real life, in the real world. AI that can orchestrate physical operations and supply chains and plants in real time, AI that can unleash a new 10x capacity from the workforce."

The IFS CEO said that investment into AI is staggering, but there's a big gap. "There's a gap between the application of financial capital into the practical, real world AI going into factories, fixing turbines and preventing wildfires. We've focused on the practical application of this technology and bringing it into the real world," said Moffat.

Building an industrial AI ecosytem

IFS' approach is to orchestrate robotic workers, designed for dangerous jobs, human experts, and digital AI workers as one integrated system. To reach this goal, IFS is teaming up with the likes of Anthropic, Siemens and Boston Dynamics and layering AI throughout its enterprise software platform that includes enterprise resource planning, asset management, field service applications and energy and resources software.

At its Industrial X Unleashed conference in New York, the company announced the following:

An Anthropic strategic partnership that will bring Claude models to industrial applications with tooling for specific use cases. For instance, IFS and Anthropic unveiled a voice-first offline AI for frontline technicians working in extreme conditions. According to IFS, 70% of the industrial workforce works in areas where connectivity in spotty.

IFS said its Anthropic partnership revolves around its IFS Nexus Black unit and its IFS Nexus Black Industrial AI applications, which are powered by Claude models. Key items include:

• The partnership combines the expertise of IFS Nexus Black, a team that adapts AI to industrial AI specific use cases, and Claude models.
• IFS launched Resolve, which gives technicians and field workers to Claude trained on use cases in aerospace and defense, construction and engineering, manufacturing, energy and utilities.
• Resolve will help frontline workers predict and prevent faults faster with multi-modal data, connect technicians to the right parts with optimized scheduling and streamline workflows.
• IFS is looking to use AI to address industries dealing with aging infrastructure as well as expertise.
• William Grant & Son, the distiller behind Grant's whisky and Hendrick's gin, used Resolve to cut downtime and revamp operations.

IFS outlined a partnership with Siemens Grid Software to use IFS as part of its intelligent grid infrastructure updates. Siemens and IFS said they will combine Siemens' grid planning, electrification and smart infrastructure applications with IFS's enterprise asset management, field service management and AI scheduling optimization software.

Key items include:

• IFS software will be integrated into Siemens Gridscale X applications.
• The goal is to provide a platform that will create a path to an autonomous, self-optimizing grid operations stack.
• The integration will be modular and designed to be deployed without rip-and-replace projects.

Boston Dynamics, IFS and 1X Technologies said they will collaborate to integrate robotic and humanoids into industrial workflows. The partnership with Boston Dynamics combines the robotics company's autonomous inspection robots with IFS.ai to enable decision-making in the field. According to the companies, the Boston Dynamics and IFS collaboration can address labor and skills shortages for industrial customers.

Key items include:

• The companies showcased a Boston Dynamics Spot robot doing inspection and feeding multimodal data to IFS.ai for analysis and decision-making.
• IFS.ai takes that data and triggers workflows in the field.
• The collaboration focuses on field operations in manufacturing, energy, utilities, mining and other asset-intensive sectors.
• Use cases include autonomous inspections to reduce human exposure to hazardous environments, efficiency for faster response times, and uptime improvements.

Under the 1X Technologies partnership, IFS and 1X will collaborate to combine humanoid robots with IFS.ai. The companies said they will develop and deploy production-ready robotics packages for manufacturing, utilities, aviation and other industries. Here's a look at the 1X-IFS plan:

• IFS and 1X will aim to create a unified digital-physical operational environment that combines robotics and enterprise business processes.
• The two companies will work with customers to industrial and validate humanoid robot use cases including factory automation, IoT powered operational data collection and field service and maintenance.
• IFS and 1X said that integrated offerings will be commercially available in 2026.

IFS saw its annual recurring revenue surge 22% from a year ago with cloud revenue growth of 31%. The company's industrial AI stack includes IFS.ai, which is designed to embed industry-specific intelligence across its applications, Nexus Black, an AI innovation accelerator, and IFS Loops, a portfolio of agentic digital workers.

The company also acquired 7Bridges in the third quarter for AI-powered supply chain, logistics and transportation optimization tools as well as TheLoops, an AI workforce provider.

In October, IFS outlined 10 digital workers with 50 agentic AI skills as it builds to a roadmap of 100 skills that will be embedded into manufacturing, energy, utilities, telecom, construction, aerospace and defense and service industries. IFS Loops Digital Workers are designed to manage complex workflows and make decisions that continually optimize processes.

The big picture

Here's a look at the high-level takeaways from IFS' conference in New York.

• Moffat's take is that AI can retool industrial infrastructure while maintaining jobs. Manufacturing already faces sever worker shortages.
• According to Moffat, there are multiple trends pointing to the power of AI and industries including aging industrial infrastructure, labor shortages and retiring expertise and the need for automation and faster decision-making. IFS’ core industries include aerospace and defense, energy utilities, construction and engineering, manufacturing and telecommunications.
• IFS is emphasizing industrial AI and that approach can stand out in a stagnant ERP space.
• The company is also highlighting real-world deployments throughout its conference and showing real business impact. IFS is showcasing blue-collar AI and how new technology can empower frontline workers.
• IFS is focused on building ecosystem alliances that can bridge the digital and physical worlds. These partnerships include Microsoft, Nvidia, Siemens, Anthropic and Boston Dynamics to name a few.
• The company is looking to productize its AI applications rather than build custom-built systems, but will use forward deployed engineers to speed up deployments.
• Mohamed Kande, Global Chairman at PwC, said industrial AI will be required to get returns from the $1.7 trillion invested in AI infrastructure. "Do you deploy today the old way, or do you use industrial AI to power all that infrastructure? Imagine being a company investing all of this money and you build something that doesn't have the right artificial intelligence in it. What happens in three or five years?" said Kande, who said boards of directors and CEOs are increasingly comfortable with placing big AI bets. 

Constellation Research's take

Constellation Research CEO R "Ray" Wang said:

"While frontier AI models and infrastructure platforms grab headlines, the critical missing piece has been the orchestration layer, the industrial operating system that embeds AI directly into mission-critical workflows. Customers seek deep domain expertise from their trusted AI partners, especially in manufacturing, utilities, aerospace, and energy. The AI Age isn’t about adding AI features to legacy software. It’s about architecting the control plane for the next generation of intelligent industrial operations where autonomous execution happens at scale, in real-time and achieving decision velocity for tangible business outcomes."

Constellation Research analyst Holger Mueller said:

"AI is changing everything and with physical AI and robotics it changes the manufacturing process. Vendors like IFS need to cater to a mix mode shop floor where humans and robots work together with the common goal of delivering projects at high quality and on time. Laying the groundwork for the agentic / robotic factory is a key step for enterprises that like will happen sooner than later."

Cisco delivers strong Q1, starts to capture AI infrastructure spend

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 reported better-than-expected first quarter results, raised its outlook and said it landed more AI infrastructure orders from hyperscalers and posted strong networking growth.

The company reported first quarter net income of $2.9 billion, or 72 cents a share, on revenue of $14.9 billion, up 8% from a year ago. Non-GAAP earnings for the first quarter were $1 a share.

Wall Street analysts were expecting Cisco to report non-GAAP fiscal first quarter earnings of 98 cents a share on revenue of $14.78 billion.

CEO Chuck Robbins said Cisco saw "widespread demand for our technologies."

Mark Patterson, Cisco CFO, said that the company's "relevance in AI continues to build and we have a multi-year, multi-billion-dollar campus refresh opportunity starting to ramp, with strong demand for our refreshed networking products."

By division, Cisco said its first quarter networking revenue was up 15% with observability up 6%. Security and collaboration were down 2% and 3% from a year ago, respectively. Security revenue was hampered by Splunk, which saw more customers opt for cloud deployments. That mix shift affected results this quarter, but is a long-term benefit for Cisco.

• Cisco tunes network portfolio, gear for AI agents, introduces AgenticOps
• Cisco launches quantum lab, unveils quantum networking chip

As for the outlook, Cisco projected second quarter revenue of $15 billion to $15.2 billion with non-GAAP earnings of $1.01 to $1.03 a share. For fiscal 2026, Cisco projected non-GAAP earnings of $4.08 a share to $4.14 a share on revenue of $60.2 billion to $61 billion.

Key points from Cisco executives from the earnings call:

• Robbins said, "we see a solid pipeline through the rest of the year." He said Cisco is seeing strong demand for routers, optical networking and switches. "We are beginning to see inferencing use cases where we are winning there," said Robbins. Four hyperscalers inked big deals with Cisco, which is also landing neo-cloud providers.
• "We expect Cisco's AI opportunity across sovereign neo-cloud and enterprise customers to ramp in the second half of fiscal year '26," said Robbins.
• The upside in AI infrastructure from enterprises should continue, said Robbins. "We know many customers still have a lot of work to do to ensure they have the modern, scalable, secure networking when they're supporting their AI goals," said Robbins.
• "We're also seeing consistent progress across our industrial IoT portfolio, including new ruggedized equipment, with orders growing more than 25% year over year. In Q1 infrastructure, we expect this demand to increase, driven by onshoring of manufacturing to the United States, the increase of AI workloads at the network edge and the emergence of physical AI infrastructure orders," said Robbins.

Anthropic to spend $50 billion on AI infrastructure via Fluidstack partnership

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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Anthropic said it will invest $50 billion in building its own AI data centers in a partnership with Fluidstack. The first data centers will be built in New York and Texas with more sites on deck.

The move comes after Anthropic announced it would use Google Cloud TPUs as well as AWS Trainium2 supercluster. Anthropic also uses Nvidia processors. The multi-cloud and multi-GPU approach was differentiated relative to OpenAI's spending spree on operating its own data center. Now Anthropic has decided that it has to roll its own AI infrastructure too.

• AWS fires up Project Rainier, Trainium2 cluster for Anthropic
• OpenAI, Anthropic increasingly diverge as strategies evolve
• Anthropic to use Google Cloud TPUs as it diversifies capacity

According to Anthropic, the Fluidstack partnership will focus on custom-built infrastructure designed for the large language model provider's workloads and R&D.

Like most announcements covering AI infrastructure, Anthropic was sure to mention the project will create 800 permanent jobs and 2,400 construction jobs and play into US AI leadership. The data centers will power up throughout 2026.

Dario Amodei, CEO of Anthropic, said the company is getting closer to AI that can accelerate scientific discovery and solve complex problems. "These sites will help us build more capable AI systems that can drive those breakthroughs," he said.

For Fluidstack, the deal with Anthropic is a big win. Fluidstack counts Meta, Nvidia, Samsung, Dell, Honeywell and others as core customers.

Holger Mueller, an analyst at Constellation Research, said: "Clearly, Anthropic is charting a different course compared to OpenAI - the question is - what is the price for the flexibility? That is - how much does the portability need for Anthropic.  Hopefully it's not only a cost arbitration game."

IBM launches IBM Quantum Nighthawk processor

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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IBM launched its most advanced quantum processor, IBM Quantum Nighthawk, and announced its IBM Quantum Loom, an experimental processor that demonstrates all of the processor components for fault-tolerant quantum computing.

Big Blue announced the roadmap additions at its annual quantum developer forum.

The news from IBM lands as Quantinuum launched its Helios system and Google highlighted its advances. In addition, pure play quantum computing companies have been able to build up their balance sheets as they develop systems.

• Google claims quantum advantage with Willow breakthrough
• Quantinuum launches Helios quantum computer, touts fidelity, enterprise customers

For IBM, the goal is to deliver quantum advantage by the end of 2026 and fault-tolerant quantum computing by 2029. IBM has offered frequent updates about its quantum computing roadmap with two in 2025.

• IBM outlines quantum computing roadmap through 2029, fault-tolerant systems
• IBM sees quantum advantage 2026, fault tolerant quantum in 2029
• IBM launches Flex Plan for quantum computing, aims to expand use cases

Here's a look at the key announcements from IBM.

IBM Quantum Nighthawk is designed to complement the company's quantum computing software stack and architecture to deliver quantum advantage. IBM Quantum Nighthawk will be delivered by the end of 2025.

Key points:

• The quantum processor will be 120 qubits linked together with 218 tunable couplers. Nighthawk will have more than 20% more couplers compared to IBM Quantum Heron.
• Nighthawk will be able to execute circuits with 30% more complexity than Heron with low error rates.
• IBM's latest architecture gives users the ability to explore more demanding problems that require up to 5,000 two-qubits gates.
• By the end of 2026, IBM said IBM Quantum Nighthawk will deliver up to 7,500 gates and up to 10,000 gates in 2027.
• By 2028, Nighthawk systems could support up to 15,000 two-qubit gates with more than 1,000 connected qubits extended through long-range couplers.

Quantum advantage will be reached by the end of 2026 and be verified by the broader ecosystem. IBM contributed three experiments for quantum advantage to be verified by the broader ecosystem.

Qiskit, IBM's quantum computing software, will get a new execution model to enable fine grain control and a C-API for HPC-accelerated error mitigation.

IBM will deliver a C++ interface to Qiskit to help developers bridge HPC and quantum computing. By 2027, IBM noted that it will extend Qiskit with computational libraries for machine learning and optimization.

The company also said that it will move toward a large-scale fault-tolerant quantum computer by 2029. The effort will be led by IBM Quantum Loon, an experimental processor. Key items for IBM Quantum Loon:

• Loon has a new architecture to implement and scale components for high-efficiency quantum error correction.
• IBM has proven it is possible to use classical computing hardware to accurately decode errors in real-time (less than 480 nanoseconds) using qLDPC codes. That ability will be coupled with Loom to scale high-fidelity superconducting qubits.

The company said that it will scale its 300mm quantum wafer fabrication in the Albany NanoTech Complex in New York. The lab will be used to expand its quantum processor development and wafer manufacturing.

AMD sees big growth over next 3 to 5 years, AI boom continuing

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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AMD projected compound annual revenue growth rates of 35% over the next three- to five years and said demand for AI infrastructure and its chip portfolio is strong.

CEO Lisa Su said during AMD's investor day that the pace of AI infrastructure spending and pace of change is higher than she's ever seen before. "We see a tremendous opportunity ahead to deliver sustainable, industry-leading growth," said Su.

Su in a question and answer session noted that that compound annual growth rate may be front loaded over the three- to five-year time horizon. "We're giving a three to five year TAM and the outer years have a little bit less visibility than the near term years," said Su. "We would expect the near term years to grow faster than 80%."

• AMD's data center, PC units shine in Q3
• OpenAI, AMD ink big GPU deal: What it means for the rest of us
• IBM, AMD eye hybrid quantum computing, supercomputer architectures

AMD is seeing strong interest in its AI accelerators. "There is a desire for significant amount of compute. We are working with the supply chain today to make sure that we have the broad ability to support all the compute that's required," said Su.

Here's a look at long-term growth targets over the next three to five years.

• AMD sees non-GAAP earnings topping $20 a share with non-GAAP operating margins of more than 35%.
• AMD's data center business will grow at a 60% compound annual growth (CAGR) rate with 10% for its PC and gaming and embedded units.
• The company sees its EPYC CPU server chip portfolio gaining more than 50% market share. In data center AI, AMD sees CAGR of more than 80%.
• PC market share will top 40%.

On the product front, AMD executives outlined the AMD Instinct roadmap including Helios systems with AMD Instinct MI450 Series GPUs followed by the MI500 in 2027.

The company also touted its next-gen Venice CPUs and AI networking offering for scale-up and scale-out workloads.

Su was asked about the risk to AI infrastructure spending, notably how much of it has to be funded by OpenAI. Su said AMD "is quite disciplined how we plan these things" and that the company is "comfortable that we know how to do it."

She added that the companies that are funding AI infrastructure such as Google, Microsoft and AWS are well funded. There's also sovereign nations spending heavily. Su said:

"All of the other large hyperscalers who are talking about raising their forecasts are extremely well funded. Their balance sheets are really strong, and the fact that they are choosing to invest more in AI should be a good indicator to the audience that they see value in it."

Regarding OpenAI, AMD's Su said:

"The reason that we are so forward leaning on this is it is great for us in terms of just the amount of learning that we get from engaging at gigawatt scale with a customer that's on the bleeding edge of foundational models. We're doing this in a very structured way. This is a very unique moment in AI and we shouldn't be short sighted. If the AI usage grows as much as we expect there's going to be plenty of financing."

QAD | Redzone acquires Kavida.ai to add procurement AI agents

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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QAD | Redzone said it has acquired Kavida.ai in a move that will bring AI agents to its procurement and supply chain workflows.

Terms of the deal weren't disclosed.

For QAD | Redzone, the Kavida.ai purchase will accelerate its Champion AI roadmap. Kavida.ai's knowhow in procurement agent training and inbox-to-ERP automation will be integrated into the company's portfolio. QAD | Redzone consists of three core interconnected offerings:

• QAD, an ERP system focused on midmarket manufacturers.
• Champion AI, a set of AI tools that works across the platform to enable the manufacturing workforce.
• Redzone, a system to bring data, AI and automation tools for frontline workers to speed up decisions.

Kavida.ai will bring procurement automation agents to Champion AI with the aim of freeing up about half of a buyer's workday by eliminating manual post order and supplier collaboration work. The Kavida.ai PO, RFQ, and Sales agents will become immediately available to QAD | Redzone customers and Kavida.ai's founders, Anam Rahman and Sumit Sinha, will assume leadership roles.

Rahman and Sinha said in a blog post that the company was founded nearly five years ago to address a big issue in manufacturing--many enterprises run on email and spreadsheets.

According to QAD | Redzone, the plan is to add Kavida.ai's procurement agents to its platform to drive manufacturer productivity.

Sanjay Brahmawar, CEO of QAD | Redzone, AI needs to deliver value quickly. "By integrating Kavida.ai’s technology and team, we’re helping our customers unlock value faster — automating critical workflows, improving supply-chain reliability, and giving every buyer, planner, and supplier a powerful digital co-pilot," he said.

Here’s a look at the flow of a Kavida.ai agent.

CoreWeave's great AI infrastructure race

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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CoreWeave said it is dealing with ongoing supply chain issues as demand far exceeds capacity and revenue expected in the fourth quarter will slip to the first quarter. Nevertheless, CoreWeave's bet is that self-building its AI infrastructure will be a winning strategy in the future.

Michael Intrator, CEO of CoreWeave, said on the company's third quarter earnings call that there are multiple delays but the biggest issue is at the powered-shell level. Powered shell refers to a facility where the power and exterior are completed, but the interior isn't finished.

"There's plenty of power right now, and we believe that there will be ample power for the next couple of years. But really the challenge is the powered shell," said Intrator.

CoreWeave's third quarter had a bevy of moving parts to consider and also reflected emerging skepticism about capital expenditures for AI infrastructure. Although AWS, Alphabet and Microsoft all said capital spending would continue to surge for AI infrastructure, Wall Street openly questioned Meta's plans. In Meta's case, it could simply be a case of metaverse traumatic stress disorder, but the focus of AI spending is turning to returns.

Consider the following for CoreWeave milestones in what is a frenetic pace of scaling:

• In the third quarter, CoreWeave had revenue of $1.4 billion, up 134%.
• Revenue backlog at the end of the third quarter was $55 billion.
• CoreWeave's will deliver more than 1 gigawatt of contracted capacity to customers within the next 12 to 24 months.
• The company landed third quarter compute contracts with Meta and OpenAI.
• A planned merger with Core Scientific is officially off and Intrator said the price was simply too high.
• CoreWeave is diversifying its stack with the acquisition of OpenPipe, which is a platform for training AI agents, and Marimo, a developer workflow company. CoreWeave also acquired Monolith for an industrial AI play.
• The company launched a unit to land US government customers and added Jon Jones, an AWS alum, as its first chief revenue officer.
• CoreWeave also launched AI Object Storage, which optimizes the storage layer for AI workloads. CoreWeave's storage platform has topped $100 million in annual recurring revenue.

However, CoreWeave's buildout comes at a price. In the third quarter, CoreWeave delivered a net loss of $110.1 million, an improvement on the $360 million net loss a year ago. Net interest expense in the third quarter was $310.55 million, up from $104.4 million a year ago. Operating income margin was 4% compared to 20% a year ago.

CoreWeave is obviously betting that if it builds the infrastructure customers will come. "AI adoption is progressing beyond the frontier AI labs and hyperscalers. Broader global demand and our recent large wins are driving diversification of our revenue base," said Intrator, who noted customer wins including CrowdStrike, Rakuten and NASA.

Jones, who was the head of startups and venture capital at AWS, will look to add AI natives that will grow with CoreWeave.

No CoreWeave customer in the third quarter represented more than 35% of the company's revenue backlog. The customer base is still concentrated, but well below the 85% level at the start of 2025. Sixty percent of CoreWeave's revenue backlog is with investment grade customers.

• CoreWeave acquires Monolith, eyes industrial AI use cases
• Coreweave's Q2: Takeaways on AI training, inference demand
• CoreWeave raises $7.5 billion in debt financing for AI data center buildout

The race

In many ways, CoreWeave symbolizes much of the AI infrastructure market in that there's a race between investor patience and scaling amid fears that overcapacity may loom.

Intrator said supply chain issues may be a risk. "While we are experiencing relentless demand for our platform, data center developers across the industry are also enduring unprecedented pressure across supply chains. In our case, we are affected by temporary delays related to a third-party data center developer who is behind schedule. This impacts fourth quarter expectations," he said.

The customer affected by the current delays agreed to adjust the delivery schedule and extend the expiration date.

CoreWeave said 2025 revenue will be between $5.05 billion to $5.15 billion with adjusted operating income of $690 million to $720 million and more than 850 megawatts of active power.

Nitin Agrawal, CoreWeave CFO, said 2025 capital expenditures will be between $12 billion to $14 billion and the 2026 figure will more than double. Interest expense in 2025 will range from $1.21 billion to $1.25 billion.

Agrawal said:

"In Q4, we will be bringing online some of the largest scale deployment in our company's history. This will have a near-term impact on adjusted operating margin due to the timing difference between when data center costs are first incurred and when we start recognizing revenue.

We expect 2025 interest expense in the range of $1.21 billion to $1.25 billion, driven by increased debt to support our demand-led CapEx growth, partly offset by an increasingly lower cost of capital."

Intrator was asked about CoreWeave's strategy to self-build infrastructure. He said CoreWeave has diversified providers and the ability to self-build data centers makes it a larger player in the supply chain. Intrator added that CoreWeave does work with third-party data center providers, but self-building is "about derisking deliver across the broader portfolio."

"We just look at self-build as an additional piece of the puzzle. It puts us closer to the physical infrastructure. It embeds us deeper into the supply chain around the world so that we have firsthand information," said Intrator. "We just think that you need to be on both sides of this fence in order to be as effective as you can be derisking what is a complicated supply chain environment."

Add it up and CoreWeave is going to be a fascinating business school case study. Is CoreWeave's balance sheet just a pile of debt or growth capital? Can CoreWeave remain differentiated in three to four years? Will CoreWeave build out is AI software stack to play a larger revenue role?

The CoreWeave saga will be a fascinating two- to three-year race. Why? CoreWeave has no debt maturing until 2028.

Constellation Research analyst Holger Mueller said:

"CoreWeave showed outstanding growth with revenue growing 150%+ YoY. It is also showing the skeptics that it is not a money loosing business - as EPS improved year over year. Another quarter like this and CoreWeave should be in the black for Q4 on an adjusted basis. With that demonstrated - the focus needs to shift on CoreWeave managing to keep the growth going with supply chain challenges as it secures capital, delivers data center capacity and runs customer workloads well. At the moment, the first concern with CoreWeave is delivering data centers. We will see if all of this issue is addressed in Q4."
 

Google Cloud, KPMG outline lessons learned from Gemini Enterprise deployments

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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KPMG is both a partner and a customer for Google Cloud and that dual role is honing methodologies, use cases and approaches for AI agent deployments.

On a webinar for analysts, Google Cloud and KPMG walked through the early lessons learned from deploying Gemini Enterprise.

At Google Cloud Next in April, KPMG said it would expand its AI partnership with Google Cloud. KPMG said it would use Google Cloud to scale its multi-agent platforms to transform business processes and integrate Gemini Enterprise to boost internal productivity.

Specifically, KPMG is leveraging Gemini Enterprise and Vertex AI with other services. Google Cloud is also being used to build AI capabilities and agents for KPMG Law US.

Stephen Chase, Global Head of AI & Digital Innovation at KPMG, said the firm adopted Gemini Enterprise across the workforce with 90% of employees accessing the system within two weeks of launch. "We believe this is the fastest adopted technology our firm has had and we are in a regulated industry," said Chase. "We went into it with the idea this was going to be part of our overall transformation. It was never about individual use cases. It was about sparking innovation."

• Virgin Voyages: Lessons learned from scaling Gemini Enterprise AI agents
• Google Cloud launches Gemini Enterprise, eyes agentic AI orchestration
• Google Cloud Q3 revenue surges 34% as backlog hits $155 billion
• Google Public Sector: AI agents and the future of government

KPMG and Google Cloud teamed up on the Gemini Enterprise deployment to hone best practices for regulated deployments.

Hayete Gallot, President of Customer Experience for Google Cloud's global, multi-billion-dollar commercial business, said scaling AI agents is about building repeatable processes and methodologies to scale.

"Beyond the models, it's really about how you're going to build those multi-agent systems," said Gallot. "We've done a lot of work to help our customers from the learnings we've had in building those multi agents. We've packaged that through our ADK (Agent Development Kit) so they can build their own agents."

Gallot added that Google Cloud is investing in the ecosystem and partners so customers can scale agentic AI. She said that Gemini Enterprise is an example or providing pre-built agents for coding and research while giving the leeway to build and connect to other AI agents.

"The more the ecosystem is on a common set of tools and protocols, the better it is to build those multi agent experiences," said Gallot.

KPMG's internal Gemini Enterprise deployment

Chase walked through the early lessons from the KPMG internal adoption of Gemini Enterprise. Among the key points:

Understand the data and regulatory issues and take a measured approach. Chase said KPMG had a good data foundation and understanding of the regulatory issues. "We took a measured approach to rolling it out and testing," said Chase. "We were doing the evaluations on what we were seeing versus what we thought we might see. Were we getting the right data and responses? Was the connector delivering back what we expected with the right controls? We spent a lot of time testing upfront."

Co-innovation. Chase said Google Cloud and KPMG engineers worked together on operating agents in the consulting firm's security environment. "We were helping actually shape how agents are built in Google Enterprise and used that to build trust in AI in our transformation program," said Chase.

Use cases. Chase said the first problem KPMG was trying to solve--and it's critical in a services firm--was enterprise search. "I need good answers and I need to get them right now," said Chase. "Solving that problem was one of the reasons people gravitated to the system."

NotebookLM as a go-to tool. Chase said NotebookLM got a lot of play internally and about 11,000 notebooks have been shared after a month and a half. Gemini Enterprise's Deep Research AI agent is also getting a lot of usage.

Data quality is everything. Chase said KPMG also worked through data quality issues to make sure responses returned were correct and kept client confidential information private.

Beware of AI sprawl. Chase said one of the things plaguing AI deployments is that they're installing more AI than people can consume.

Client facing deployments

KPMG is also deploying Gemini Enterprise at its enterprise accounts.

Chase said KPMG is looking to take its best practices and make them available broadly to clients.

"Ultimately, clients will share the agents they build with each other. And some of those will be industrialized," said Chase.

Once agents are industrialized they can be distributed and "spark innovation at the edge and core and everything else we're doing," said Chase. "That's what our clients are really interested in."

The other key item in Gemini Enterprise deployments is that it's a horizontal system that "fits really nicely in a heterogeneous environment," said Chase.

"Gemini Enterprise doesn't have to be in a monolithic environment," added Chase.

Gallot said that Google Cloud has revamped its technical teams to be hands on and focus on methodology. "We're building a lot of consultative capability in our front end so our people can spark ideas with customers. We have developed a methodology to help our customers to go from idea to production," she said. "It's technology, methodology, catalog and people."

Enterprises are currently looking for knowledge in agentic AI deployments. Chase said the key issues for clients are:

• Data security and broader cybersecurity.
• Data management.
• Use cases. "We have a dedicated process that we go through to pull use cases from both client work and what we're doing internally," said Chase.

For KPMG, the next step after collecting use cases for processes such as finance, procurement and various operating areas, say consumer lending at a bank, is to create reusable starter kits.

"We're all headed toward orchestrating agents and what we're working on now is the building blocks to get us there," said Chase.

These building blocks are then shared across KPMG's tax, audit and advisory service lines. Every client will have different circumstances, but KPMG's goal is to have common areas that can be adapted. Sharing those lessons will make it easier to generate returns.

"We get a lot of questions in the enterprise and if they're going to invest we need to help demystify AI agents and share lessons," said Chase.

A look at the intersection of AI and customer experience

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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Artificial intelligence and customer experience are a common intersection on earnings conference calls as enterprises. Companies are looking to connect the dots between lifetime value of a customer, driving revenue and hybrid approaches that meld technology and humans.

Here's a look at some of the CX efforts detailed in recent days.

Uber: Lifetime experience

Uber is on track to support about 14 billion rides in 2025, but the goal is to drive cross platform usage and engage consumers over a long period. Think lifetime experience over lifetime value even though the two categories are closely related.

Note the nuance of lifetime experience messaging from Uber CEO Dara Khosrowshahi. Lifetime value of a customer is a common metric that revolves around the total predicted revenue a company can get from an entire relationship. LTV is transactional.

Lifetime experience is a view that Uber can go from providing rides to multiple services over time. In theory, lifetime experience could be more valuable and lead to deeper customer relationships.

"At its core, Uber is a trips machine built to make rides and deliveries happen affordably at scale," explained Khosrowshahi. "While an exceptional trip experience will always be core to who we are, we’re now expanding our focus beyond the next trip—to consumers' entire lifetime experience with Uber. Taking this lifetime view means thinking more holistically about how people engage across our platform—sometimes making investments that may reduce short-term results but strengthen long-term loyalty, or prioritizing actions that benefit the platform overall, even if one business line bears an immediate cost."

Khosrowshahi said Uber One is one program designed to encourage cross-platform engagement. Consumers who engage across Uber's services have 35% higher retention rates and spend three times as much as those who don't.

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Lifetime experience also accounts for new services Uber may add in the future. Today, it has a small base of consumers using multiple services, but you could play out a scenario where rides, delivery and maybe healthcare are delivered over a lifetime. On the flip side, Uber is seeing 9.4 million gig workers work across the platform for rides and delivery. Most Uber workers are focused on one task.

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Khosrowshahi said: "Over the coming years, we will change both by converting couriers to drivers and vice-versa, and by further extending our flexible earnings model beyond rides and delivery. For example, we recently announced that we will be piloting digital tasks in the Uber Driver app, powered by Uber AI Solutions. The pilot will give drivers more ways to earn during downtime by completing tasks like uploading or tagging photos to help train AI models. Our ambitions here are much larger, and you will see us lean into this opportunity in the years ahead."

Takeaway: Consider lifetime experience efforts to drive traditional lifetime value of a customer.

Match: Growth depends on experience flywheel

Match Group CEO Spencer Rascoff said the company is leveraging AI across its brands, notably Tinder, Hinge and Match, to reboot growth with experiences that lead to outcomes--and presumably more revenue. Match is planning to launch a revamped Tinder in the Spring.

Rascoff refers to the turnaround as a reset, revitalize and resurgence. The reset is complete and the latter two parts are underway.

"We believe our business model thrives when user outcomes improve. Better outcomes, driven by higher quality experiences, better matches and more meaningful connections, build confidence in our product and drive new users through positive word of mouth. User success builds trust in the category and in Match Group's apps," said Rascoff. "By getting the user experience right, we will further deliver real success stories, which we use in marketing to amplify growth by driving new user acquisition and reactivations. Our marketing strategy, especially at Tinder and Hinge, is focused on fueling category consideration bringing in new and lapsed users through product-led storytelling that reflects real experiences happening across our brands."

• Match's big generative AI plans revolve around Tinder, Hinge, alleviating dating pain

Match estimates there are 250 million actively dating singles worldwide not currently on dating apps. Match is looking to reengage 30 million lapsed users and attract 220 million first timers. However, Match has a Gen Z problem. Enter a series of AI efforts with many of them revolving around trust and authenticity on the platform.

For instance:

• Tinder will get Chemistry, an AI-driven interactive matching feature that learns users from via questions and with permission learns from their camera roll to understand interests and personality. Chemistry is designed to combat "swipe fatigue" and surface a few highly relevant profiles each day. The feature is live in New Zealand and Australia.
• Hinge has AI-first features including Conversation starters, which are personalized prompts for first message. The tool has resulted in 10% more likes with comments and stronger engagement.
• Tinder's Face Check feature verifies that users are real and match their profile photos. It will roll out in the US and is required for new users in California, Colombia, Canada, India, Australia and Southeast Asia. "We have seen a 60% reduction in user views of profiles later identified as bad actors, and a 40% decrease in reports of bad actor activity," said Rascoff.

What was notable in Match's third quarter call is how experience experiments on the interface and new features have hampered revenue as well as user growth.

Takeaway: The lesson from Rascoff appears to be to play the long game with experience.

Hinge Health: Physical therapy experiences

Hinge Health sits at the intersection of digital health with its network of physical therapists. The challenge is providing experiences that are "about the elegant unification of digital and in-person care," said Hinge Health President James Pursley.

The company is leveraging AI to provide a digital PT experience with a hybrid approach that brings in humans when needed. Daniel Perez, CEO of Hinge Health, said the company is focused on multiple AI efforts that impact experiences. Hinge Health's third quarter revenue was $154 million, up 53% from a year ago.

"Everything we do is centered around the triple aim, using technology to transform outcomes, experience and costs in health care," said Perez.

AI experience efforts include:

• Robin, Hinge Health's AI care assistant, provides movement analysis. Robin is a 24/7 companion and when someone has a pain flare up, the AI assistant can gather data and details and alert physical therapists so care can be delivered faster. In the near future, Robin will be able to provide instant support and proactively check in with members.
• Hinge Health is using proprietary TrueMotion Vision technology to analyze movements. TrueMotion Vision captures joint angles, symmetry and endurance across a battery of movements. That data is combined with targeted questions to assess joint health.
• The company has leveraged AI internally to be more efficient on developing product features. Perez said the focus is on developer experiences. AI adoption is close to 100% and "we've seen a 32% improvement in developer experience scores from April through October," said Perez.

Takeaway: AI and automation improves experience, but the option for human touch matters to bring it home.

Comcast: Integrated approach

Comcast knows it has to improve its customer experience in the long run. Technology integration and AI will play a big role.

Speaking on Comcast's third quarter earnings call, Comcast President Michael Cavanaugh said the cable provider is using AI to self-optimize network performance and its own WiFi gateway to offer seamless performance.

"We're taking meaningful steps to simplify the customer experience across all channels. Our new AI engine now supports agents, technicians and customers through assisted chat, phone, our website and our AI-enabled Xfinity Assistant platform," said Cavanaugh. "We also launched a program that connects customers to a live agent in seconds, which is now available to half of our customer base. It's still early, but we're moving fast and executing with focus towards a simpler, smarter and more seamless customer experience."

Takeaway: Comcast sees tech support, ease of installation and customer service on the same continuum.

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