BT150 Spotlight: Karen Higgins-Carter on practical AI approaches

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 is a big opportunity across enterprises in multiple sectors and boards of directors need to think through practical use cases that drive returns, said Karen Higgins-Carter, a former CIO who serves on multiple boards.

Higgins-Carter, a member of Constellation Research's BT150, has been a CIO multiple times including stints at Gilbane Building Company, Webster Bank, MUFG Americas and various GE divisions.

I caught up with Higgins-Carter to talk shop. Here are some of the takeaways.

AI as common opportunity. Higgins-Carter said that boards need to recognize that AI returns cut across multiple sectors and enterprises "AI is a common opportunity across sectors," explained Higgins-Carter. "What comes to mind first is that AI is for a technology, or heavily digital company, but AI also applies to warehouses, planes, trucks, logistics and construction. Sectors with a physical product have a tremendous amount of information flow that is involved in the delivery of the product. Same with pharma and same with financial services. So, make sure that you're thinking about the opportunity holistically and not necessarily from a biased point of view about what business you are in."

What AI doesn't change. Higgins-Carter said boards need to explore AI for their businesses, but in the end businesses are all about creating value in the short- and long-term. "You have to manage unit economics and that doesn't change with AI," she said. "I think boards are interested in practical experimentation, but they're more interested in AI from a strong point of business acumen. What are the practical opportunities in the experiments and inherent risks."

Buy vs. build AI approaches. "If you don't already participate in the technology sector you don't have building your own AI as a core competency," said Higgins-Carter. "Starting with buying and partnering with other companies to develop a capability is a place to start. You can get near-term value. Then you can explore how AI is operationalized within your company; what roles stay and what roles go away?"

Over time, Higgins-Carter said enterprises can figure out AI's implications for workforce planning, culture and commercialization. Companies need to think about opportunities for real differentiation and whether it makes sense to create in-house AI applications.

The buy approach has served Higgins-Carter in her CIO roles largely because the industries were not focused on technology, which was more of an enabler. "I've seen the model that corporations can contribute their knowledge of the industry, their data and develop something unique," she said. "You have to partner with others that bring the depth of technology knowledge to go with industry knowledge."

Hype vs. reality with AI. Higgins-Carter said enterprises are seeing AI automate tasks, but not necessarily roles. Companies are being smarter about tailored approaches to AI, top use cases and process optimization as well as risks. "I think the focus has changed from running a lot of prototypes  to taking a few use cases all the way through the process," she said.

Digital labor and agentic AI. Higgins-Carter said agentic AI will initially support the workforce, but the reality is that it will reshape jobs. "It's a moral obligation for teams to prepare the workforce for that eventuality," said Higgins-Carter. "How people can participate in the future is fundamental to human capital management."

More CxO interviews:

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• BT150 member Dr. Jonathan Reichental: How autonomous vehicles could change how cities are designed
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• SuperNova Award interview with Joe Prota: How IBM used Adobe Firefly to speed up ideation and iteration
• BT150's Ashwin Rangan on CIO evolution, technology curves and what it means for genAI deployments
• BT150 Spotlight: Sunitha Ray on the difference between enterprise AI and genAI

BT150 zeitgeist: AI, efficiency, vendor angst and finding the right IT structure

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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A big push to cut costs via IT spending and automation is likely to put a squeeze on enterprise vendors with some companies pondering exit ramps from current suppliers. Can you write your own ERP and CRM systems using generative AI?

Those are some of the core themes that were surfaced on Constellation Research's January 2025, BT150 call. Ray Wang, Constellation Research CEO, noted that 2025 is going to be interesting from a technology spending perspective because the US government's push to cut costs has carried over to the enterprise.

In other words, enterprises are going to look for exponential efficiency and that's going to lead to some creative moves. One creative move that's being pondered is using generative AI to write applications on the fly. Items that surfaced on the BT150 call, which operates under Chatham House rules, include:

• A tug of war between software vendors and customers. Enterprises need to drive down cost structure, but vendors are trying to jack up rates. AI and automation will play a big role in how this battle plays out. The enterprises that figure out how to rewrite core systems or relegate them to inexpensive plumbing will win.
• Foundational systems are too expensive. Previously, the idea was to build on top of legacy ERP and CRM systems, but the foundational costs are too expensive. One enterprise has an internally built CRM tailored to its business because of costs. AI and low code means it's easier than ever to move off of established platforms to save money. "There's a tremendous opportunity for us to reset the table here," said one BT150 member.
• Pegging the IT budget to a percentage of sales. One BT150 member noted that through the retirement of tech debt and using ServiceNow to integrate best-of-breed suppliers, the IT budget is now pegged to a bit more than 1% of sales from about 6%. Process automation is driving that optimization.
• Invest in minimal amounts on IT and more on change management. Multiple BT150 members said that change management needs more investment if companies are going to transform and optimize.
• Structure of technology budgets, spend and interface with the business leads is critical. Multiple BT150 members noted that it was critical to get the right IT model in place and sell it to other C-level executives to cut costs. One CxO described a model of layers that include management, operations, administrative and technology. Technology supports the other three and needs a plan to continually cut overhead.
• AI model training portability. With AI being embedded everywhere, enterprises are realizing that the training is critical. There will be a push toward keep the training and moving the models. Enterprises don't want to be locked into a model.
• The fires in Los Angeles County are going to surface issues with disaster recovery as infrastructure will need to be backed up and replaced. Disaster recovery strategy isn't the headliner now, but will be worth monitoring.

More from the BT150 calls:

• 2024 in review: What we learned from our BT150 CxOs
• BT150 zeitgeist: AI agent questions, SaaS ate the opex and job woes
• BT150 Zeitgeist: GenAI projects and culture, performance management
• 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

Physical AI, world foundation models will move to forefront

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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Physical AI and the foundational models that go with them will be a recurring theme across enterprises now that Nvidia has laid out its Cosmos world foundation models.

Speaking at CES 2025, Nvidia CEO Jensen Huang launched its own physical AI models with the aim of popularizing use cases across industries and robotics. Huang said Cosmos is needed to jump start the next generation of foundation models. After all, training world foundation models (WFMs) is expensive.

"The ChatGPT moment for robotics is coming. Like large language models, world foundation models are fundamental to advancing robot and AV development," said Huang.

WFMs will use data, text, images, video and movement to generate and simulate virtual worlds that accurately models environments and physical interactions.

Cosmos models have been trained on 9,000 trillion tokens from 20 million hours of real-world human interactions, environment, industrial, robotics and driving data, said Huang. WFMs will usher in a new era where AI can "proceed, reason, plan and act."

The play for physical AI is robotics and autonomous vehicles, said Huang. "There are no limitations to robots. It could very well be the largest computer industry ever," said Huang. "There's a very serious population and workforce situation around the world. The workforce population is declining, and in some manufacturing countries it's fairly significant. It's a strategic imperative for some countries to make sure that robotics is stood up. Human robots will surprise everybody with how incredibly good they are.”

Nvidia's launch of WFMs may jump start physical AI efforts that have been percolating. Consider:

• Archetype AI raised $13 million in seed funding to develop its WFM called Newton. Archetype AI's funding round was led by Venrock and included Amazon Industrial Innovation Fund, Hitachi Ventures, Buckley Ventures and Plug and Play Ventures. Archetype AI’s primers on physical AI are worth a read.
• Google DeepMind is putting together a team to create generative AI models to simulate the world.
• Odyssey raised a $18 million Series A round to train generative AI models for film, gaming and world. The funding is initially being used to capture data from the real world to train models.
• AI on Demand in Europe is also working on physical AI models. "What distinguishes Physical AI systems is their direct interaction with the physical world, contrasting with other AI types, e.g., financial recommendation systems (where AI is between the human and a database); chatbots (where AI interacts with the human via Internet); or AI chess-players (where a human moves the chess pieces and reports the chess board state to the AI algorithm)," said AI on Demand.

There are other efforts to develop WFMs and physical AI use cases. Huang obviously thinks physical AI is a huge market across multiple industries. It's also likely that all of the sensor data available from Internet of things sensors will also be handy.

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Stanford University and Robotics at Google have compiled datasets on physical concepts and properties.

Accenture is also all-in on the physical AI and WFM effort. In Accenture's Technology Vision report for 2025, Karthik Narain, Group Chief Executive of Technology and CTO at Accenture, said:

"We are reaching a watershed moment as the power of generative AI is applied to physics and the field of robotics. Gone are the days of narrow, task-specific robots that require specialized training. A new generation of highly tuned robots with real world autonomy that can interact with anyone, take on a wide variety of tasks, and reason about the world around them will expand robotic use cases and domains dramatically."

What remains to be seen with physical AI will be the various intersections with WFMs. At AWS re:Invent 2024, a few executives noted that IoT is cool again and going to fuel industrial AI use cases.

Physical AI also sounds like it'll rhyme with active inference, a theme at Constellation Research's Connected Enterprise 2024. Denise Holt, Founder and CEO AIX Global Media, said active inference will "create digital twins of everything."

Holt said the agents that power active inference leverage sensor data from IoT, cameras and robotics and measure it against a real-world model. "We will have smart cities, autonomous systems, improving the efficiency of everything, global supply chains, personal and critical systems," said Holt.

Rest assured that physical AI in practice will combine multiple disciplines and technologies. Get up to speed because your board may be asking about physical AI soon.

CxOs need to focus on quantum computing readiness, not the noise

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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Quantum computing vendors IonQ and D-Wave Quantum touted strong bookings for 2024 and the fourth quarter and said systems are showing near-term usefulness and commercial potential. The disclosures were timed to counter Nvidia CEO Jensen Huang's take that useful quantum computing systems were 15- to 30-years away.

Huang made the quantum computing comments at CES 2025 in a Q&A with analysts. Specifically, Huang said:

"Someday we'll have very useful quantum computers. We're probably five or six orders of magnitudes away, 15 years for useful quantum computers and that would be on the early side. 30 years is probably on the late side. If you picked 20 years a whole bunch of us would believe it."

Huang's comments cratered quantum computing stocks such as IonQ, Righetti Systems and D-Wave were halved in intraday trading. Quantum stocks took off in late 2023 after Google touted quantum computing gains and AWS announced services to help enterprises prepare for use cases. Even after the stock hit, quantum companies are overvalued due to the immaturity of the market.

For instance, D-Wave said 2024 bookings will top $23 million, up 120% from a year ago. Fourth quarter bookings will be at least $18 million, up 500% from a year ago. Keep in mind that bookings take time to convert to revenue. For the nine months ended Sept. 30, D-Wave reported revenue of $6.52 billion.

D-Wave CEO Dr. Alan Baratz said, "Jensen Huang has a misunderstanding of quantum" and D-Wave's approach is useful today.

• Practical quantum computing advances ramp up going into 2025
• Quantum computing all in on hybrid HPC with classical computing

IonQ CEO Peter Chapman said the company will hit the high end of its bookings and revenue guidance for 2024. Chapman added that IonQ expects to be profitable with revenue approaching $1 billion by 2030. Chapman said IonQ's current quantum systems are "already providing insight to solutions for customers today" with more powerful systems coming in 2025.

More: IonQ’s bet on commercial quantum computing working, acquires Quibitekk | IonQ's quantum computing bets: Quantum for LLM training, chemistry and enterprise use cases

IonQ is the most mature of the pure play quantum companies with 2024 revenue between $38.5 million and $42.5 million.

For comparison, Righetti Computing revenue for the nine months ended Sept. 30 was $8.52 million.

• Quantum Computing Platforms
• Quantum Computing Software Platforms
• Quantum Full Stack Players

What's a CxO to do?

Like any emerging technology, there's the Wall Street story, which often runs ahead of reality, and the enterprise planning story.

The enterprise story revolves around cloud providers providing quantum computing instances to test. And companies in certain industries--chemistry, biology and finance--can make use of quantum today.

CxOs need to plan ahead and think through use case for quantum computing. And established enterprise vendors are increasingly becoming quantum technology players. For instance, IBM is networking its Heron quantum systems together and can provide value today. Either way, quantum computing is going to be accessible via your cloud providers such as AWS, Microsoft Azure and Google Cloud whether they offer their own quantum systems or access pure play offerings.

Holger Mueller, an analyst at Constellation Research, has been covering quantum computing in recent years. Mueller said it makes sense for CxOs to stay focused on learning the technology (there are multiple approaches) and use cases that can add value. The rest is noise.

He said:

"If GenAI did not happen, we would all talk about quantum. With quantum encryption already working and delivering its first use case, more use cases are around the corner. All eyes are on IBM to see if the coupling of the Heron processors will work. If it does, quantum use cases will become relevant for enterprises in 2025. Planning and simulation are the horizontal use cases, protein folding and chemical engineering are the vertical ones. CxOs need to get their enterprise ready by moving the relevant data to their cloud of choice (and before assessing the quantum plans of that cloud vendor)."

More:

• IBM upgrades IBM Quantum Data Center with Heron
• IBM Boosts the Software Side of Quantum
• Quantinuum, Microsoft claim quantum reliability breakthrough
• Microsoft claims hybrid quantum breakthrough with Quantinuum, partners with Atom Computing
• Quantinuum raises $300 million, valued at $5 billion
• Quantum Brilliance, Oak Ridge National Laboratory aims to meld quantum computing, HP

Cohere launches North, aims to be an AI agent workspace

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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Cohere launched an early access program for North, an AI platform designed to make daily work more efficient. The aim for Cohere North is to combine large language models, search and agents in one secure enterprise work and collaboration platform that can run anywhere.

With the move, Cohere, which was founded in 2019, is downplaying the LLM arms race yet could wind up being more effective for enterprise customers. The company refers to its North strategy as one focused on "meaningful AI. Other foundational model players are also working to diversify. Anthropic is adding collaboration features as it expands use cases for its Claude models. OpenAI is also expanding but has largely focused on expanding into search to compete with Google.

• Agentic AI: Three themes to watch for 2025
• Agentic AI without process optimization, orchestration will flop

The upshot here is that foundational model giants are going to have to surround their LLMs with applications. Cohere will have its models but is making a push to be an AI workspace platform to improve the speed and quality of everyday tasks. Cohere in July raised $500 million in funding for a $5.5 billion valuation and now has a more rounded offering.

According to Cohere, North will combine the following:

• Cohere models with other frontier models.
• Search discovery and automation.
• A multimodal AI search system called Compass to extract information from images, slides, spreadsheets and documents.
• Enterprise-grade security and privacy standards.
• A turnkey approach designed to speed up time to value relative to do-it-yourself approaches.
• The ability to be deployed in public and private clouds as well as on premises.

Cohere North, which is taking enterprise applications for early access, is a bet that AI returns are directly tied to workforce adoption. If teams have an easier way to use AI for everyday tasks, they're more likely to adopt it. Royal Bank of Canada and Cohere have partnered to co-develop North for Banking, a platform connects RBC's procedure and policy documents and processes with AI models.

In the blog post, Cohere said North is "optimized to run in private–including air-gapped–environments so that organizations can safely integrate all their sensitive data in one place." Cohere added that its vertically integrated stack will enable North to enable AI agents to perform complex tasks across enterprise silos.

Cohere is also taking aim at the retrieval augmented generation performance of much larger players such as Microsoft Copilot and Google Vertex AI.

What's next? Cohere appears to be going vertical. Cohere CEO Aiden Gomez said in a post:

"Since we developed each part of the technology stack underpinning North, the platform can be tailored to suit the unique needs of any business. This granular level of control is essential for customizing AI solutions to match each organization's needs such as industry-specific terminology and internal knowledge. Additionally, with our industry-leading focus on privacy and security, North is well suited for regulated industries where companies simply cannot risk their proprietary data."

In other words, the RBC partnership will likely be replicated across other industries.

At NRF 2025, it's a retail AI agent parade from enterprise vendors

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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Retailers are about to get swamped with AI agents that are designed to boost efficiency and enhance customer experiences if news out of NRF 2025 is any indicator.

NRF 2025 in New York has a heavy dose of AI across the supply chain to distribution to customer experiences. Adobe in its holiday shopping report noted that consumers embraced generative AI chat bots as customer assistants and led to a 1,300% increase in traffic to retail sites.

IBM in a recent study found that retail and consumer product companies are shifting spending toward AI. IBM found that retail and consumer product companies will allocate an average of 3.32% of their revenue to AI to enhance customer service, supply chain and marketing.

With that backdrop, here's a roundup of what's being pitched to retailers for 2025.

Salesforce launched Agentforce for Retail with features that couple in-store and digital shopping. Agentforce for Retail includes new prebuilt skills for order management, guided shopping, appointment scheduling and marketing skills for loyalty promotion creation. Salesforce also launched Retail Cloud with Modern POS. 

Google Cloud launched Agentspace for Retailers, which gives retailers one place to combine the company's agentic AI tools, Gemini models and search. In addition, Google Cloud outlined Vertex AI Search for Commerce and Google Cloud Gen AI Catalog and Content Enrichment. Google also said it would bring its Vision AI to physical retailers via a partnership with Everseen Partners. Also: Google Cloud launches Agentspace to create, deploy agents

Oracle launched the latest version of its Xstore Point of Service, which is updated with a new interface and workflows with analytics for store associates. Xstore also has a new architecture built on Oracle Cloud Infrastructure and Oracle Autonomous Database. The system can be deployed on various flavors of cloud infrastructure or on-premises. 

HPE launched new HPE Aruba Networking gear for retailers including the HPE Aruba Networking 100 Series Cellular Bridge for wide area networking access, the HPE Aruba Networking CX 8325H switch for compact spaces and HPE Aruba Networking 750 Series wireless access points. HPE also launched a new management console designed to optimize retail indoor and outdoor networks and detect Internet of things anomalies. 

Blue Yonder launched its December platform update that features "AI-centric data modeling." Blue Yonder added integrated demand and supply planning with AI and machine learning updates to combine supply chain, financial operations and risk and opportunity scenarios. A few items in the December release include:

• One view of online and in-store inventory.
• Support for vertically integrated retailers.
• Enhanced returns orchestration with integrations between warehouse management, commerce and returns management.
• AI tools added across the platform to cover inventory optimization and planning for demand, business and supply chain.

Talkdesk announced Talkdesk AI Agents for Retail, which aims to bring agentic AI to retail customer service. These conversational agents are designed to handle retail processes autonomously and take action.

Crisp, a company that specializes in retail analytics, launched AI Blueprints, a suite of open-source AI templates for consumer product group companies. The AI Blueprints aim to automate manual tasks, simplify processes and harmonize data. The AI Blueprints are free and run within Databricks and Google Cloud with Snowflake on deck.

Planalytics announced Xtreme, a set of applications that uses AI to forecast consumer demand to significant weather events such as hurricanes, flooding, heat waves and cold snaps. Planalytics also co-authored a report with NRF on climate proofing retail.

Zebra Technologies outlined the Zebra Mobile Computing AI Suite, a set of development tools to bring AI vision to Zebra Android Devices. Zebra also said the AI tools integrated into Zebra Companion, a suite of AI agents designed for retail frontline workers.

More retail:

• Lowe's betting on AI to drive customer experience, optimize multiple processes
• How Home Depot blends art and science of customer experience
• Scotts Miracle-Gro and UserTesting: 9 customer experience takeaways
• Walmart, Target highlight intersection of supply chain, customer experience
• SoundCommerce CEO Eric Best: 'Data capability enables customer lifetime value'
• Shopify increasingly becoming enterprise commerce OS, replaces incumbents
• Walmart's big tech, AI bets paying off going into 2024 holiday shopping
• Sam's Club CEO Nicholas on AI, frictionless commerce, focus on members
• How Wayfair's tech transformation aims to drive revenue while saving money

Here’s what Nvidia CEO Jensen Huang said about quantum computing, Project Digits and robotics

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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Nvidia CEO Jensen Huang said useful quantum computers are more than a decade away, Project Digits fills a big void for data scientists and developers and serves as a personal AI cloud, and human robotics will develop faster than expected.

Huang fielded financial analyst questions at CES 2025 and generated a few headlines worth noting. From a stock market perspective, Huang managed to tank quantum computing stocks, which have had a torrid run over the last two months.

We'll start with Huang's quantum comments, but in the near term the comments on robotics and Project Digits was arguably more actionable. Nvidia had a busy CES 2025 with agentic AI developments and Project Digits, which puts an AI supercomputer on a desktop. The upshot is AI inference and development may become distributed.

• Nvidia launches Project Digits, a desktop AI supercomputer
• Nvidia launches Cosmos models, aims to expand physical AI, industrial reach
• Nvidia moves to advance agentic AI use cases at CES 2025

Here's what Huang had to say:

Quantum computing strategy. Huang was asked about quantum computing and its usefulness in the near-term. Huang said that quantum computing can't solve every problem and that the technology is "good at small data problems."

"The reason for that is because the way you communicate with a quantum computer is microwaves. Terabytes of data is not a thing for them. There are some interesting problems that you could use quantum computers like generating a random number cryptography...

We're not offended by anything around us, and we just want to build computers that can solve problems that normal computers can't.

Just about every quantum computing company in the world is working with us now, and they're working with us in two ways. One, is quantum classical and we call it CUDA Q. We're extending CUDA to quantum and they use us for simulating the algorithms, simulating the architecture, creating the architecture itself.

Someday we'll have very useful quantum computers. We're probably five or six orders of magnitudes away, 15 years for useful quantum computers and that would be on the early side. 30 years is probably on the late side. If you picked 20 years a whole bunch of us would believe it. We want to help the industry get there as fast as possible and create the computer of the future."

• Practical quantum computing advances ramp up going into 2025
• Quantum computing all in on hybrid HPC with classical computing

Constellation Research analyst Holger Mueller said:

"It is no surprise Nvidia works with all Quantum vendor to help offload, prepare and operate workloads and data for quantum machines. Huang is a little off with the use case benefits. In Spring 2023 some quantum vendor showed and used real enterprise workloads. But he is right - the viability is limited. This may change in 2025 though - where all eyes are on IBM and there plan to couple multiple Heron systems." 

Blackwell demand. Huang said Hopper and Blackwell demand is strong on a combined basis.

The Blackwell system on a chip (SOC) in Project Digits. Nvidia worked with MediaTek on the Blackwell SOC and Huang was asked why the company didn't do the architecture itself. "MediaTek does such as good job building lower power SOCs," said Huang. "If we can partner to do something then we can do something else. MediaTek did a wonderful job. We shared our architecture with them and it was a great win-win and saved a lot of engineering."

Project Digits market. Huang was asked about Project Digits and Nvidia's PC ambitions. He said:

"I'm getting incredible emails from developers about Digits. There's a gaping hole for data scientists and ML researchers and who are actively building something and you don't need a giant cluster. You're just developing the early versions of the model, and you're iterating constantly. You could do it in the cloud, but just costs a lot more money. Personal computing exists so you can have a op-ex, capex trade off. A lot of developers are trying to get through using Macs or PCs and now they have this incredible machine sitting next to them. You could connect it through USB-C, or you could connect it using LAN, or Wi-Fi. And now it's sitting next to you. It's your personal cloud, and it runs the full stack of what you run on DGX. Everything runs exactly the same way.

"It's essentially your own private cloud. If you would like to have your own AI assistant sitting on this device, you can as well. It's not for everyone, but it's designed for data scientists, machine learning researchers, students."

What's the PC plan? Nvidia said Project Digits is focused but Huang wasn't going to say much about future PC efforts. "Obviously we have plans," he said.

The platform. Huang emphasized that Nvidia is building a platform for "every developer and every company." He said Nvidia can extend into World Foundation Models, robotics and computational lithography for TSMC. "We love the concept of a programmable architecture," he said.

Automotive and autonomy. "Every car company in the world will have two factories, a factory for building cars and the factory for updating their AIs," he said. "Every single car company will have to be autonomous, or you're not going to be a car company. Everything that moves will be autonomous."

Robotic systems and strategy. "There are no limitations to robots. It could very well be the largest computer industry ever," said Huang. "There's a very serious population and workforce situation around the world. The workforce population is declining, and in some manufacturing countries it's fairly significant. It's a strategic imperative for some countries to make sure that robotics is stood up."

"Human robots will surprise everybody with how incredibly good they are," said Huang.

Analysts also asked Huang about the robotic strategy. He said:

"Our robotic strategy for automotive and robot and human robots, or even robotic factories, are exactly the same. It is technologically exactly the same problem. I've generalized it into an architecture that can address a massive data problem."

AI assistants for coding. "If a software engineer is not assisted with an AI, you are already losing fast. Every software engineer at Nvidia has to use AI assistance next year. That's just a mandate," he said.

What models Huang uses. Huang said he uses OpenAI’s ChatGPT the most followed by Google’s Gemini models, especially the ones aimed at deep research. “Everybody should use these, Ais at least as a tutor,” said Huang. “Every kid should use AIs as a tutor. The continuous dialog is insanely good and it’s going to get better.”

Paychex acquires Paycor in $4.1 billion deal

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. ...

Read more

Paychex said it will acquire Paycor HCM in an all-cash deal valued at $4.1 billion.

Under the terms of the deal, Paycor shareholders will get $22.50 a share. The deal is expected to close in the first half of 2025.

The merger brings together two Constellation Research Shortlist vendors for payroll for North American SMBs and HCM suites focused on North America.

According to Paychex and Paycor, the combined company will boost its total addressable market by offering a suite of technology and services for HR, employee benefits, insurance and payroll.

Paycor has 2,900 employees and more than 49,000 customers. Paycor's HCM, payroll and talent software has typically served small businesses but has been moving toward larger enterprises.

John Gibson, CEO of Paychex, said Paycor "will enhance our capabilities upmarket, broaden our suite of AI-driven HR technology capabilities, and provide new channels for sustained long-term growth."

The combined company is expecting cost savings of more than $80 million in the short term and neutral to accretive to adjusted earnings in the first year post close. Paychex will gain from Paycor's partnerships and sales coverage and HCM features that will round out the HCM suite.

Going forward, Paychex and Paycor will invest in data and AI tools for customers.

Constellation Research analyst Holger Mueller said:

"There always have been consolidation trends in the HCM market – but the acquisition of Paycor by Paychex is a new apex. The SMB player buys the large enterprise player, resulting in a double automation portfolio – that is unrivaled – apart from UKG, which itself is the product of a merger. The acquisition is good news for the higher-end Paychex customer and for Paycor customers who have smaller operations and subsidiaries in Canada. It is now up to the Paychex management team to successfully integrate portfolios, go to market and more."

In a presentation, the focus of the deal was go-to-market efforts. Paychex and Paycor expect to win in more customer segments, sell Paycor's workforce management, talent acquisition and talent management tools to Paychex customers. Paychex's HR advisory and employee solutions will be sold to Paycor customers.

By the numbers:

• Paychex has $5.3 billion in annual revenue.
• Paychex has more than 2.3 million HR outsourcing worksite employees.
• Paychex HCM and payroll software has processed more than $950 billion financial transactions and is the leading 401(k) record keeper in the US.
• Paychex has 745,000 payroll clients.
• Paycor has $655 million in annual revenue and 90% of it is recurring revenue.
• Paycor revenue growth in fiscal 2024 was 19%.
• Paycor has 2.7 million active employees on its platform.

AI PCs may decentralize inferencing workloads

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

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

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The launch of Nvidia's Project Digits, a desktop AI supercomputer starting at $3,000 for data scientists and AI researchers, is just one example of a broader theme: AI training and inferencing is going to be decentralized to some degree.

Nvidia's news--delivered by CEO Jensen Huang at CES 2025--highlights a key use case revolving around offloading AI development with a high-powered system. The general idea is that users can develop and run inference on models on the desktop and then deploy to the cloud or data center.

For enterprises pondering on-premises data centers and wrestling with cloud costs, Nvidia's Project Digits isn't a bad idea--especially when you can network two of those systems together to run up to 405-billion parameter models.

"With Project Digits, developers can harness the power of Llama locally, unlocking new possibilities for innovation and collaboration," said Ahmad Al-Dahle, Head of GenAI at Meta.

Constellation Research analyst Holger Mueller said:

"Digits poses lots of challenges, but if Nvidia overcomes them it has repercussions across the whole AI stack. Bigger has been better for AI and the question has always been this: Is it cost effective and can it be run on remote locations? Digits ends that discussion. It is Blackwell everywhere (and with that the soon to be announced Blackwell successor) as well."

But Nvidia's desktop supercomputer is just part of the mix.

Dell Technologies said it will be among the first to roll out systems powered by AMD's latest Ryzen processors. AMD launched Ryzen AI Max Pro, Ryzen AI 300 Pro and AMD Ryzen 200 Pro Series processors for business PCs. HP also outlined the HP ZBook Ultra G1a 14 mini workstation.

With the move, AMD is putting high-performance computing into thin laptops and looking to capture AI workloads on workstations. Ryzen AI Max Pro Series processors are designed for large engineering and models.

Simply put, there should be enough AI compute in the field that can be networked and leveraged. 

Intel also updated its Intel Core lineup with a focus on business AI applications. For good measure, Qualcomm outlined its Snapdragon X series, which will bring AI and Copilot+ PCs to the masses at a price around $600.

• Santa isn't bringing you an AI PC in 2024
• PC industry's big dream: AI enabled PCs spur upgrade cycle
• PC upgrade cycle to be driven by AI, but calling timing has been difficult

How will this develop?

These systems at CES could be construed as yet another effort to prod consumers and businesses to upgrade PCs. The AI PC upgrade cycle will happen, but it has clearly been delayed. Instead, look upstream to Project Digits, which will have a real AI workload purpose. Yes, enterprises will be more dependent on Nvidia but being able to develop and tweak models locally and then use data center resources is likely to be cheaper and arguably more sustainable.

With all the cool kids--data scientists and AI wonks--buying supercomputer workstations the rest of us will likely boost specs just to have the horsepower. Of course, the masses won't use that horsepower, but if a savvy enterprise can capture the compute at the edge there's real returns ahead.

There's already signs that enterprises are starting PC upgrades and more future-proof AI friendly systems are likely to seal the deal. For consumers, the AI PC upgrade cycle will be slower, but you can expect Qualcomm to capture some real share.