When I was spending time with a client last week, it really stood out to me how far we’ve come lately in clarifying AI offerings. Thanks to the rampant pace of AI development over the past couple of years, sometimes the fundamentals of product management and product marketing have been deprioritized. For example, plenty of product messaging and feature descriptions have been released when they’re still at a notoriously high level across the board. But that is starting to change. I take this as a reminder that early adopters are far more tolerant of a product’s rough edges than the general market. It’s a positive sign that we are now seeing more maturity and wider adoption of enterprise AI software products. I believe it’s also a sign that things will slow down somewhat as pilots and prototypes move towards production.

I have been spending more time with NVIDIA agentic blueprints, and I can say that the detail and effort taken to document how to get the blueprints up and running is pretty impressive. This also stands out because these agents can be deployed on-premises or in a cloud. By contrast, most agentic efforts so far have been limited to a specific cloud or a SaaS platform. I am hoping that this level of deployment choice is a sign of things to come, rather than an exception.

2025 has started off with a lot of AI investing. Whether it’s ServiceNow’s acquisition of Cuein, new VC rounds at AI startups, or even the massive commitment from the Trump administration for U.S. AI data centers, it seems like investors are finally getting off the sidelines. That’s a good thing in general, but it will signal a shift in priorities for existing product teams. If you are a developer, I would expect more focus on ease of use, consumability, and samples versus net-new innovations and APIs.

Lastly, while this news is a bit old, I did want to address the hoopla that was made when Satya Nadella of Microsoft made his comments about the future of SaaS. It seems that in the tech world there is nothing we like more than declaring things dead. However . . . that’s not what he said. What will really happen is that the business-logic and user-experience layers of SaaS will be massively changed by agents. But the overall value proposition of SaaS platforms will likely remain intact.

When I was spending time with a client last week, it really stood out to me how far we’ve come lately in clarifying AI offerings. Thanks to the rampant pace of AI development over the past couple of years, sometimes the fundamentals of product management and product marketing have been deprioritized. For example, plenty of product messaging and feature descriptions have been released when they’re still at a notoriously high level across the board. But that is starting to change. I take this as a reminder that early adopters are far more tolerant of a product’s rough edges than the general market. It’s a positive sign that we are now seeing more maturity and wider adoption of enterprise AI software products. I believe it’s also a sign that things will slow down somewhat as pilots and prototypes move towards production.

I have been spending more time with NVIDIA agentic blueprints, and I can say that the detail and effort taken to document how to get the blueprints up and running is pretty impressive. This also stands out because these agents can be deployed on-premises or in a cloud. By contrast, most agentic efforts so far have been limited to a specific cloud or a SaaS platform. I am hoping that this level of deployment choice is a sign of things to come, rather than an exception.

2025 has started off with a lot of AI investing. Whether it’s ServiceNow’s acquisition of Cuein, new VC rounds at AI startups, or even the massive commitment from the Trump administration for U.S. AI data centers, it seems like investors are finally getting off the sidelines. That’s a good thing in general, but it will signal a shift in priorities for existing product teams. If you are a developer, I would expect more focus on ease of use, consumability, and samples versus net-new innovations and APIs.

Lastly, while this news is a bit old, I did want to address the hoopla that was made when Satya Nadella of Microsoft made his comments about the future of SaaS. It seems that in the tech world there is nothing we like more than declaring things dead. However . . . that’s not what he said. What will really happen is that the business-logic and user-experience layers of SaaS will be massively changed by agents. But the overall value proposition of SaaS platforms will likely remain intact.

In the opening days of his new term, President Trump announced a massive $500 billion AI project called Stargate. Supercomputers and AI datacenters will be built by key partners that include OpenAI, Oracle, SoftBank, and MGX. The project will use technology created by Arm, Microsoft, NVIDIA, Oracle, and OpenAI. SoftBank is the lead financial manager, OpenAI will handle model development and training, and Oracle will manage the data aspect. The project’s objective is to maintain U.S. leadership in AI and to create advanced AI in the form of artificial general intelligence. AGI will be able to perform a wide array of tasks with human-like intelligence, and potentially revolutionize fields like material science, medicine, and environmental science.

With an initial investment of $100 billion, construction has already begun in Abilene, Texas, where Microsoft is building an AI supercomputer. The investment is slated to ramp up to $500 billion by 2029. The plan is to establish 10 datacenters of 500,000 square feet each, with intentions to expand to another 10 across the U.S. once further site evaluations are complete. This project should create significant economic and security benefits for the U.S., especially because it emphasizes national and military security, aiming to enhance capabilities in data analysis, surveillance, and cybersecurity to safeguard against strategic threats.

To truly evaluate the project, we need more details. It appears that Microsoft initially planned to build a supercomputer exclusively for OpenAI with a $100 billion price tag, and that plan morphed into the much larger $500 billion national plan complete with supercomputer and multiple datacenters. We will need more information to fully understand the plans, how they evolved, and how they are being implemented.

Brightcove is smartly leveraging Amazon Q to address the complex technical queries from its global client base. (If you need a refresher on Amazon Q, take a look at this writeup we did last year.) By using Amazon Q, Brightcove aims to empower its support team to reduce research time and significantly improve the customer service experience, particularly for intricate issues like video embedding.

While acknowledging the potential of generative AI, Brightcove wisely emphasizes a cautious approach with rigorous testing and expert scrutiny to ensure accuracy and build trust. This focus is increasingly important in today’s AI landscape, where many companies get caught up chasing cost cuts and automation. Brightcove seems to recognize that the true potential of generative AI often lies in augmenting human capabilities and fostering deeper customer understanding.

Equipping its support team with Amazon Q will likely speed up response times and enable more effective problem-solving and stronger customer relationships. This is a strong way to leverage AI for a competitive edge. Brightcove’s strategy, with its emphasis on accuracy and human-centered implementation, highlights how accuracy, trust, and scalability can be key to maximizing the benefits of this technology to improve CX.

Adding on to my colleague’s contributions about Project Stargate, here are a few of my thoughts:

• It didn’t take long after this historic announcement for the questions to raise. If Stargate is going to be an AI venture, what exactly is the product or service being offered? Is it an AI cloud? Purely an R&D platform? Ten separate 500,000-square-foot datacenters racked with AI-specific infrastructure sounds an awful lot like a cloud to me, but maybe it isn’t.
• Is this more about creating 100,000 high-paying jobs, with the understanding that the market will find use for these datacenters in quick order? While Oracle’s Larry Ellison spoke at a high level about being able to create cancer therapies and vaccines, I’m a little confused about how this plays out specifically.
• While the investment of $500 billion over five years is incredible, what is the projected time until the first customer, partner, user, or consumer is actually using Stargate? It seems likely that the ROI on this investment will be a bit further out.
• While we know that Oracle, OpenAI, and Softbank are working in partnership with NVIDIA and Microsoft — what does this environment actually look like?
• What does Stargate mean for the cloud market? Anything? Is Stargate reserved only for the largest of large use cases that would typically require an on-premises cluster? Or is the net being spread wider to enable a real incubation across the spectrum — from the largest-of-large to the smallest-of-small companies?

I am a big fan of the government recognizing the need for the United States to stay far ahead of its competitors in any area of technology, and certainly AI is hugely important. As a country, we are already investing far more than the rest of the world combined. However, as a person who is inherently skeptical of anything the U.S. government does in the longer term, I would like to understand better the how, why, what, when, and where of Project Stargate.

When HPE launched silicon root-of-trust back in 2017, it was a game-changer in the server market. By examining and responding to the millions of lines of code that execute before a server even boots an operating system, the company provided what was the most secure server in the industry. And by integrating this with its integrated lights out (iLO) management, HPE created servers that could not only detect malware at the lowest levels, but also take corrective actions to mitigate the impact.

Since 2017, the threat landscape has evolved considerably. Quantum lockers and AI-driven malware kits create a new set of challenges that require a new way of securing platforms. Here’s the question: is the infrastructure supporting our most critical workloads evolving to meet these challenges? We’ve seen the silicon vendors respond. Now as server platforms prepare to refresh with the newest CPUs, I’ll be curious to see whether the baseboard management controllers and hardware-based security mechanisms deliver the required protection.

Customer data platforms (CDPs) are like superheroes for enterprises looking to delight their customers. They collect data from various sources and organize it in a centralized location, ensuring that everyone has access to the same insights about each customer. This collaborative approach provides better teamwork and deepens customer understanding. Armed with this comprehensive data, marketing teams can deliver personalized campaigns that make customers feel valued and encourage repeat business. CDPs serve as treasure troves of customer data, simplifying data sharing and utilization for teams.

But CDPs aren’t without their challenges. They can be tricky to scale up, especially for big companies with lots of data. And if the data isn’t accurate, it can mess up everything. Many CDPs don’t have the best analytics tools, which can make it hard to figure out what’s working and what’s not. And integrating CDPs with old systems can be a real pain.

Despite these challenges, the CDP market is growing fast. It’s expected to reach $72 billion by 2033, which is a huge increase from $7.82 billion in 2024. This growth is happening because businesses want to do a better job of engaging with their customers and making them feel valued. They also want to be able to use data to make smart marketing decisions. And they’re starting to use AI, automation, and machine learning to make CDPs even better.

If you’re a business considering CDPs, consider your needs and how you’ll use them. Ensure the CDP can handle your data, integrate with your systems, and provide analysis tools for smart decisions. CDPs are promising solutions for business growth and success. Look for my upcoming Forbes article on the state of enterprise data, which will highlight CDPs.

Ericsson recently integrated large language models into its NetCloud management platform. Thanks to this, AI agents can process network data and technical documentation to generate configuration recommendations. What is unique is that the system performs this functionality without exposing sensitive information to third parties, and thus — by design — provides a higher degree of security and control for datacenter deployments. It is also worth noting that the architecture is agentic in nature and employs multiple agents to solve complex tasks, including troubleshooting connectivity issues, automating infrastructure provisioning, and translating business intent and requirements into network policies. Such tasks have required manual intervention in the past, and if Ericsson can successfully execute in this area, it could lead to incremental enterprise networking revenue opportunities for Ericsson’s customers.

It looks like manufacturers are getting serious about AI in 2025. They’re increasing their AI budgets to become more efficient and competitive. The good news here is that they’re mainly focused on using AI to help their employees, not replace them, aligning with the principles of Industry 5.0. To leverage AI, I suggest manufacturers modernize their ERP systems, improve their data management strategies, and upgrade management processes.

Transforming your business to cloud-based ERP systems is a key for taking advantage of AI. By modernizing, manufacturers can optimize their investments and reap the benefits of the new technology.

Last week I published a case study that highlights this ERP–AI connection and the importance of modernization for making it work: “Hearst Corporation Modernizes Oracle ERP with Strong Change Management and Data Management Practices.” As enterprises adopt AI-driven solutions, it’s crucial to balance the technology advancements with addressing the human and organizational aspects of transformation. Two key pillars — change management and data management — are essential for achieving actionable outcomes. Change management focuses on organizational and human factors, while data management ensures data completeness and quality, enabling accurate and timely insights. Without both, enterprises may struggle to modernize, integrate workflows, or make informed decisions.

For this case study, I had the chance to sit down with David Hovstadius, senior vice president of finance operations at Hearst Corporation, who emphasized the importance of these principles during Hearst’s transition to Oracle Cloud ERP some years ago — which continues to pay dividends as the company embraces generative AI today. By prioritizing change management and data management, the company laid a foundation that not only facilitated its ERP implementation, but also enabled continuous technological and process improvements as AI technologies emerged. For more details, check out the article linked above.

The new Samsung Galaxy S25 smartphone launch happened last week, and it demonstrated how Google, Samsung, and Qualcomm are working in lockstep not only in mobile but also in XR with Project Moohan. Witnessing the interplay of Gemini with the depth of the Moohan experience clearly demonstrates how the three companies are working together to deliver the best AI experience in mobile. For more context on this partnership, see my coverage of last month’s launch of the Android XR spatial OS.

NXP has announced the EdgeLock A30 secure authenticator. It’s a standalone chip compatible with many MCUs and MPUs, including NPX’s MCX and i.MX products. Its minuscule size (“smaller than a grain of rice”) and standard I2C interface make it easy to fit into small devices, and NXP’s comprehensive EdgeLock 2GO certificate services ease the commissioning process. Developers need integrated solutions that conform with new and upcoming security and privacy regulations — and customer concerns. For example, the EU’s Batteries Regulation (2023/1542) requires using a Digital Product Passport by 2027, including supply chain provenance, and the EdgeLock A30 is the basis for a scalable solution. The chip has a RISC-V processor and 16 kB of NVM for credential storage, is Common Criteria EAL6+ certified, and is available now.

Last July, I reported that IBM acquired two Software AG properties – StreamSets and webMethods. Software AG’s streamlining continues with the sale of Alfabet and a management buyout of Cumulocity, the company’s IoT division. Cumulocity, founded in 2012, is once again independent after eight years under Software AG. Founder and CEO Bernd Gross told WirtschaftsWoche, “We are moving towards independence as a scale-up,” and “The big IoT boom is still to come.” I’m expecting strategic changes that better align the company with physical AI trends, making it more of a solution enabler than a solution provider.

Verizon’s new AI strategy leans on its strengths in 5G with mobile edge compute (MEC) and fiber. This creates an opportunity for businesses and even cloud providers to move their AI applications as close to the edge as possible using available compute for inference and low-latency applications. I like to see Verizon leaning in this direction because the company has struggled to differentiate its offerings from those of AT&T and T-Mobile.

Cybersecurity researchers at Sophos have discovered that threat actors have exploited Microsoft Teams to spread malicious links and files, potentially leading to ransomware infections. These attackers use AI for social engineering, making the attacks harder to detect. Microsoft has acknowledged the issue and is working on a solution. While these findings highlight specific threats to Teams, they serve as a broader warning about the increasing risk of similar attacks across all collaboration platforms. The problem is likely not isolated to Microsoft and emphasizes the need for heightened vigilance and robust security measures across the board.

My review of the RTX 5090 graphics card found that NVIDIA continues to innovate in AI. While the 5090 is a very large and power hungry card compared to the 4090, its performance is also considerably higher in 4K with DLSS 4 and 4x frame generation turned on. I also found that the AMP (AI management processor) is a RISC-V core, which is programmable and shows the allure of RISC-V for such applications.

For the first time, all the subsystems necessary to implement universal and fault-tolerant quantum computation have been combined in a photonic architecture. Xanadu has created a photonic quantum computer named Aurora that is a scale model for universal, fault-tolerant quantum computing. Aurora incorporates 35 photonic chips, 84 squeezers, and 36 photon-number-resolving detectors.

The system achieves 12 physical qubit modes per clock cycle, which means it can handle 12 qubits for each processing step, and it has synthesized a cluster state with 86.4 billion modes — reflecting the vast number of different ways 12 qubits can interact with each other. For error correction, it uses a distance-2 repetition code with real-time decoding. Aurora’s architecture is divided into three stages:

1. Preparing photons to create quantum states
2. Adjustment of quantum states or entangling the qubits
3. The QPU performs the computations

The Aurora operates at room temperature and uses fiber-optic networking, which facilitates scalable quantum computing. Xanadu’s design is focused on fault tolerance and scalability. Compared to other photonic quantum computing efforts from makers such as PsiQuantum and Photonics, Aurora stands out with its comprehensive system design, error correction, and scalability. Shared challenges among photonic platforms remain optical loss and high qubit error rates.

Last week the Trump administration instructed the Department of Homeland Security to disband all advisory committees within the agency, including the Cyber Safety Review Board. The CSRB was created under the Biden administration in 2022 and, interestingly, played a role in investigating China-sponsored cyberattacks against U.S. telecom providers. The clean sweep of all advisory committees may simply be a resetting of the guard and a change in policy direction, but it will be interesting to see whether it impacts cyber defense negatively in the short or long term.

Something subtle that I think has been mostly overlooked: Qualcomm’s Snapdragon 8 Elite for Samsung comes with more than just a frequency bump; it also includes customizations in the Qualcomm DSP for some of the new Samsung imaging features, plus an integrated display controller on the SoC for lower power consumption. This is something probably only Samsung could achieve, but it still clearly grows out of Qualcomm’s understanding that Samsung needs something different and custom.

Technology continues to transform sports in more and more ways. One example is the TGL indoor golf league, a tech-enhanced golf league cofounded by Tiger Woods, Rory McIlroy, and Mike McCarley in partnership with the PGA Tour. Recently launched after a year-long delay due to storm damage at its SoFi Center facility in Palm Beach Gardens, Florida, TGL combines virtual and traditional golf. Matches feature six teams of four players competing in a mix of simulator-based and on-course play, including a morphing 3,800-square-foot green. The league’s unique format includes nine-hole team matches, head-to-head play, and overtime closest-to-the-pin contests, with scoring determining playoff seeding. Matches will air live on ESPN and ESPN+.

This is a pretty interesting way to showcase golf with advanced simulators, mechanically altered greens, and innovative visuals. I believe that TGL does a good job of bringing technology together while creating a unique spectator and player experience.

Meanwhile, other sports continue to try out new tech, such as soccer using semi-automated offside technology (SAOT) to make video assistant reviews (VAR) for offsides clearer and faster. But fans aren’t always on board with these changes; VAR in particular has created significant concerns among fans of the Premier League and other top leagues about transparency and how the technology affects the flow and fairness of the game. As I’ve said before, it will always be important for sports to integrate new tech while keeping important traditions alive.

5G mobile and fixed wireless access could play a pivotal role within Project Stargate, the ambitious AI effort announced in the early days of the new Trump administration. As covered elsewhere in this update, the initiative aims to invest $500 billion in infrastructure to build out AI datacenters in the United States. As gen AI becomes more hybrid from the cloud to network edges, mobility could become instrumental in the processing of smaller language models hosted in smaller edge data nodes. 5G has been searching for its killer application beyond fixed wireless access consumer services, and given 5G’s low latency, fast throughput, and massive device support advantages — it may have found it in the rollout of AI.

I think everyone realizes the impacts AI is having on a wide range of business activities. So it should not be a surprise to anyone that The World Economic Forum’s 2025 Future of Jobs Report projects that almost 90% of companies expect that AI will redefine company operations by 2030.

AI is reshaping workplace dynamics. It is expected to create a net increase of 2 million jobs, resulting in 11 million new jobs while displacing 9 million. It is not surprising that the titles with the greatest job growth will be data specialists and AI/ML technologists.

What about people being let go from companies because they don’t have the necessary AI skills? It’s not as bad as expected because 75% of companies plan to upskill current employees for AI collaboration. That shows a focus on adapting to AI advancement instead of replacing staff. And 70% of companies plan to hire people who already have AI expertise. So, most companies will do a little of both actions. Along with those statistics, 50% of businesses say they will reorganize around AI opportunities, and 40% will use workforce reductions to handle AI expansion.

Just reading the news daily will tell you how quickly AI is being adopted. It’s an instance of the old “early bird gets the worm” saying: many companies believe that those who integrate AI fast and first will have a competitive advantage over those that don’t.

The message is clear: AI is coming, and it’s coming fast. It is expected to cause the largest workplace shift in decades. Management should establish AI priorities and get ready to implement them as necessary. Go AI, and go fast.

I think everyone realizes the impacts AI is having on a wide range of business activities. So it should not be a surprise to anyone that The World Economic Forum’s 2025 Future of Jobs Report projects that almost 90% of companies expect that AI will redefine company operations by 2030.

AI is reshaping workplace dynamics. It is expected to create a net increase of 2 million jobs, resulting in 11 million new jobs while displacing 9 million. It is not surprising that the titles with the greatest job growth will be data specialists and AI/ML technologists.

What about people being let go from companies because they don’t have the necessary AI skills? It’s not as bad as expected because 75% of companies plan to upskill current employees for AI collaboration. That shows a focus on adapting to AI advancement instead of replacing staff. And 70% of companies plan to hire people who already have AI expertise. So, most companies will do a little of both actions. Along with those statistics, 50% of businesses say they will reorganize around AI opportunities, and 40% will use workforce reductions to handle AI expansion.

Just reading the news daily will tell you how quickly AI is being adopted. It’s an instance of the old “early bird gets the worm” saying: many companies believe that those who integrate AI fast and first will have a competitive advantage over those that don’t.

The message is clear: AI is coming, and it’s coming fast. It is expected to cause the largest workplace shift in decades. Management should establish AI priorities and get ready to implement them as necessary. Go AI, and go fast.

Salesforce CEO Marc Benioff recently announced a pause in software engineer hiring for the company, suggesting that AI could automate a growing portion of development tasks. This move, likely motivated by potential cost savings and reported productivity gains via Salesforce’s Agentforce AI tool, raises questions about the evolving role of tech jobs and how companies might manage an AI-augmented workforce.

While some praise Salesforce’s innovative approach, many remain cautious about AI’s ability to completely replace human engineers soon. This decision also highlights a key challenge: Could existing departments like IT or HR oversee this new workforce, or will companies create new roles specifically to manage this digital labor?

Salesforce’s strategy serves as an interesting example of how AI might reshape business operations. It remains to be seen whether other companies will adopt similar strategies and how these trends could impact the tech job market over time. This potential shift in the tech landscape underscores the growing potential of AI to reshape industries and redefine workforce needs, with Salesforce highlighting a key strategy for companies promoting AI adoption: demonstrating its ROI through internal cost savings.

What to make of the outgoing administration’s restrictions on AI chips and models? There are so many different angles to consider. However, the sharing of AI model weights and export controls on semiconductors are the two biggies. While the U.S. government has billed this as diffusing AI innovation, it is at the same time restricting innovation of a couple of players that are on the leading edge of AI development.

Does this stifle AI innovation in the United States? I don’t believe so. Perhaps it reshapes some of our collaborative efforts on a global basis, but the semiconductor, hardware, and software ecosystems are going to continue to accelerate at seemingly exponential rates. I just don’t see that slowing down.

Here’s an interesting take: The primary target of these restrictions — China — has been leveraging open-weight models and is using these to try and gain a global footprint. Models like Alibaba’s Qwen have been showing good performance relative to what we have here in the U.S. — especially in multilingual support. And Qwen has found traction in many countries outside of the U.S. and western Europe. Just as Huawei pivoted after its U.S. blacklisting and gained such a strong global footprint in telecom, Alibaba and others can (with Huawei) deliver their own AI factories.

One of the questions we have to ask is whether we are truly protecting the U.S. and its allies with these protectionist measures. Or are we accelerating investments from adversary governments into AI that perhaps pay out in the longer term? It’s a tough question to answer.

The big news of the week was Lenovo’s announcement that it will acquire high-end storage provider Infinidat. While Lenovo has long been strong in the low end of the storage market, it has struggled to find a foothold in the high end of the market. Adding Infinidat to the portfolio solves for this challenge. However, it will take a bit of rationalization across product, marketing, and sales to find success and compete in the enterprise.

I believe that one of the most important assets to consider in this acquisition are the people who have developed, marketed, and sold Infinidat’s solutions to date. The high-end segment they have sold into — and that Lenovo desires to capture — works differently from the volume/transactional markets (commercial enterprise, SMB) where Lenovo has made its mark in storage. I think those same developers and go-to-market professionals will be essential for the success of this business combination. For more details, check out my full analysis of this deal on Forbes.

Active Directory (AD) is a core piece of enterprise IT, as it handles authentication and access to many important IT assets such as apps, databases, and security systems. Unfortunately, its importance also makes it a prime target for cyberattacks. That makes AD recovery after an attack a high priority, but that’s been a function in need of more innovation. “Recovering Active Directory is foundational to maintaining continuous business after a cyberattack, yet traditional methods are too complex and prone to error,” said Pranay Ahlawat, Commvault’s chief technology and AI officer.

To address this issue, Commvault has recently introduced Cloud Backup & Recovery for Active Directory Enterprise Edition, which aims to make AD forest recovery much simpler and more automated. Read more about this in my latest Forbes article.

Microsoft has introduced Microsoft 365 Copilot Chat, a new AI service for businesses that blends free chat features with consumption-based access to AI agents. This offering leverages AI technology to help users with tasks like document analysis and process automation. Costs vary depending on the complexity of the task, with simple web searches being free and more complex actions involving company data costing more. This flexible approach allows organizations to dip their toes into AI without a hefty upfront investment, scaling their usage as needed.

Hewlett Packard Enterprise Aruba Networking recently announced a portfolio of products tailored to brick-and-mortar retailers. The company’s retail portfolio includes a cellular bridge, a smaller-form-factor switch, and wireless access points that can support more sensors and devices — ultimately providing broader coverage. HPE is also partnering with retail device leaders including Zebra Technologies to ensure an ecosystem approach to its solution delivery. There is a tremendous opportunity in this market to delight customers with automated shelf replenishment and online-like experiences as well as to improve operational efficiency tied to better logistics and reduced shrinkage. From my perspective, the company’s retail portfolio and its AI-infused HPE Aruba Networking Central management console is well positioned to deliver value to retailers and customers alike.

SAP and IBM are continuing their 50-year relationship with a partnership to support the shift of SAP S/4HANA from on-premises to the cloud. This offering looks to facilitate the migration of SAP S/4HANA workloads from on-premises IBM Power Systems. For context, SAP has 10,000-plus customers running SAP on IBM Power servers. The collaboration of SAP and IBM focuses on helping organizations modernize their ERP environments and support AI-powered business processes. The RISE with SAP program provides a structured approach to cloud migration, offering outcome-driven services and platforms to assist organizations in reimagining their operating models.

The longstanding familiarity between SAP and IBM makes the shift less daunting, though adoption will depend on factors such as a given customer’s current SAP setup, budget, and readiness for cloud migration. I’ve talked a lot about modernization and the importance of change and data management, which will be key areas to address during these transitions. Transitioning systems isn’t easy, and any change can add complexity. Still, modernizing is crucial for businesses using ERP systems to stay competitive. This is a good opportunity for companies to make the most of their IBM Power server investments and use this collaboration to bring their ERP systems up to date.

IBM Consulting has announced plans to acquire Applications Software Technology LLC. AST brings expertise in Oracle Cloud applications, specifically with public-sector organizations in government and education and companies in manufacturing, energy, and CPG. AST specializes in implementations of Oracle ERP, HCM, Configure, Price, Quote (CPQ), Oracle Cloud Infrastructure (OCI), JD Edwards ERP, and NetSuite. This move fits with IBM’s strategy and builds on its recent acquisition of Accelalpha, which offers Oracle Cloud consulting services. My thought is that this year is the perfect time for ERP modernizations, especially with the AI craze. In that context, IBM Consulting has set itself up to help businesses transform and succeed.

Epicor Prism is bringing AI agents to the supply chain, making it easier for users to gain relevant insights. Integrated with Epicor Kinetic ERP, Prism uses AI agents to handle tasks such as data analysis, demand prediction, scheduling, inventory optimization, and updates. This should allow supply chain teams to save time and cut down on routine manual tasks so they can spend more time on strategic work. This is part of Epicor’s push to modernize its ERP systems in 2025 and could make life easier for businesses using Epicor in manufacturing, distribution, and retail. Definitely something to keep an eye on.

The Nintendo Switch 2 is precisely the device that I expected Nintendo would launch. It’s a combination of generations-old hardware with significantly improved user experience and UI. I think Nintendo understands clearly that it needs to hit the right balance between a certain price point and a certain game experience, which is what the Switch is all about. I think the people who expected the new model to be like a PC gaming handheld are living in an alternate reality. The handheld gaming market will always have the Switch at the entry level, while PC handhelds are distinctly premium products.

AT&T announced a fiber and wireless guarantee that compensates customers for downtime. I’m watching this strategy from my edge/IoT point of view because of its potential applicability in industrial IoT. Specifically, the business case for private 5G adoption rests on delivering reliable, predictable, scalable connectivity in enterprise and industrial settings. However, unlicensed spectrum alternatives (Wi-Fi et al.) are “good enough” for many use cases — at substantially lower costs. 5G’s advantages must deliver quantifiable ROI to justify the higher cost, and service-level guarantees help make the case for buying more 9s of guaranteed reliability.

The recent Sonos fiasco teaches a valuable lesson about what can go wrong with long-term support for complicated mashups of device firmware, cloud services, and phone apps. In this case, the company released a major app rewrite last May, resulting in usability issues and a cascade of serious bugs. Sonos could not simply revert to the old apps because upgrades to firmware and cloud services broke backward app compatibility. Among other consequences, this fiasco led to the departure of the company’s CEO.

Here’s my take from an edge / IoT perspective: Software-defined products, including vehicles (SDVs), create technical debt that extends throughout the product’s lifetime. Regression tests aren’t sufficient to catch real-world bugs and usability problems. (Last year’s Crowdstrike outage is another example of a catastrophic testing failure.) The lessons are simple:

1. Don’t bet the farm on internal tests. Experiential tests on deployed products with real users must be part of the plan.
2. Avoid forklift updates. If unavoidable, budget for significant testing, roll the update out slowly, and have a rollback strategy ready to go.

In another firmware-related incident affecting a software-defined product, Tesla is recalling more than 239,000 vehicles for a condition where a computer circuit board short circuit causes problems, including loss of the rearview camera image. The fix is a software update that alters the power-up sequence to avoid a potential reverse-voltage situation that causes the short. (Transistors hate reverse voltage.) This is an excellent example of how SDVs can simplify maintenance because the fix is an OTA update that is transparent to the customer.

After 18 months of preparation, the FCC announced the launch of the U.S. Cyber Trust Mark label for IoT consumer devices. The voluntary security and privacy testing program requires eligible products to pass compliance testing by accredited (FCC-recognized) labs. “Voluntary” is the operative word here. Consumers will only look for the mark if it becomes widely used on mainstream products. That might happen, but I’m not holding my breath.

AT&T’s new service guarantee will fundamentally change how carriers operate over time as consumers start to expect actual service-level agreements with their carriers — and compensation when things go wrong. I expect that Verizon and T-Mobile will follow suit if AT&T’s move successfully retains customers, or takes customers away from competitors.

Samsung is teasing its next-generation smartphone — the Galaxy S25 line — this week. It will be really interesting to see how Samsung’s new flagship devices perform in the latest benchmarks against the iPhone as well as other Android phones with Snapdragon 8 Elite processors. I am excited to see what new AI features Samsung introduces to differentiate itself from the other Android OEMs and even Apple.

Microsoft has introduced a new consumption-based pricing model for its 365 Copilot Chat alongside its existing subscription-based option. This model allows organizations to experiment with and scale AI usage according to their needs and budget. The consumption-based pricing facilitates controlled experimentation and proof-of-concept projects. However, potential inconsistencies in functionality and updates across the two models in this tiered system could create user experience disparities.

This disparity may be a strategic move by Microsoft to incentivize customers to upgrade. Still, the consumption model’s flexibility could also attract customers who desire the full feature set of Copilot along with consumption-based pricing. The flexible pricing strategy could potentially drive wider AI adoption, but ensuring a consistent and valuable user experience across both models will be crucial for Microsoft. The offering addresses many of the barriers to AI in the enterprise, including cost and adoption, and it promotes better security for companies by discouraging BYOAI.

Google has announced changes to its Workspace offerings, integrating AI capabilities into its Business and Enterprise plans without requiring paying for additional add-ons. Effective last week, this update includes AI assistance in various Workspace applications such as Gmail, Docs, Sheets, and Meet. The new features encompass Gemini Advanced for complex tasks and NotebookLM Plus for research assistance. By incorporating these AI tools directly into existing plans, Google appears to be lowering barriers to entry for businesses interested in AI. This approach, similar to Microsoft’s recent consumption-based model, could facilitate wider AI adoption and allow Google to demonstrate the value of its AI to customers. The strategy may encourage users to engage more readily with AI features within familiar applications, potentially leading to increased productivity and improved work quality. Google states that it has implemented security measures and compliance certifications for these AI features, addressing potential concerns about data protection and information access control.

CES 2025 has been over for a week now, and it’s quite clear looking back that lots of PC OEMs refreshed their lineups to take advantage of the latest chips from AMD, Intel, and NVIDIA, almost all of which focus on AI performance and experiences. It remains unclear whether AI applications will actually take hold this year, but it’s quite clear that they did not in 2024.

Miami University and Cleveland Clinic have created a partnership that will strengthen Ohio’s efforts to become a leader in quantum computing. The partnership will create Ohio’s first college quantum computing degree program. The collaboration will integrate Miami University with the Cleveland Clinic’s on-site IBM Quantum System One, the first quantum computer fully dedicated to healthcare. (Readers with long memories may recall the Forbes article I wrote a couple of years ago about the debut of that computer.)

Miami University will develop bachelor’s, master’s, and doctoral programs in quantum computing. Cleveland Clinic, in turn, will offer internships and research opportunities for Miami students. Aligning a quantum curriculum with actual healthcare applications will open a pipeline that will probably boost Ohio’s economy.

On a personal note, I’m especially glad to see Miami University move into quantum because my three grown daughters all graduated from that institution.

Last week, Microsoft launched its Quantum Ready program to alert business leaders that quantum computing has made significant progress over the past few years — and that they should get ready to take advantage of that progress. In 2024, several significant quantum breakthroughs and important pieces of research moved the technology forward. The field has gone from theoretical mathematical concepts to an emerging technology on the cusp of making major breakthroughs in multiple modalities. These modalities include superconducting, trapped ions, neutral atoms, photonics, and topological quantum computing.

Other factors have also helped improve quantum. One important advance is that quantum processors have improved significantly over the past five years. Current quantum computers have higher-quality qubits, allowing computations that weren’t possible five years ago. Microsoft’s initiative urges business leaders to get ready to harness the transformative potential of quantum computing coupled with AI.

Microsoft’s commitment extends to the global stage. This year, it is partnering with the United Nations, the American Physical Society, and others to celebrate the 2025 International Year of Quantum Science and Technology. This initiative commemorates a century of quantum innovation while fostering awareness of how quantum applications will revolutionize industries. By leading these efforts, Microsoft aims to empower organizations and communities worldwide to embrace the quantum future effectively.

Cisco recently announced its AI Defense platform, which is slated to be generally available in March. One of the challenges associated with securing algorithmic models is that they are not deterministic and can be easily compromised. As modern AI workloads move from the cloud to network edges, attack surfaces will be greatly expanded, making safety and security more difficult. Time will tell if Cisco’s approach is effective, but I believe that AI Defense has the potential to address AI security at scale with automated validation techniques that can dynamically adjust guardrails to an ever-changing threat landscape.

Nokia is making progress towards its goal of becoming an enterprise network services provider. The company has had challenges broadening its reach beyond the cellular market, but its innovation in delivering autonomous networks has great promise. Last week I published my insights on this topic in a Moor Insights & Strategy research paper.

I was quite intrigued by the agentic blueprints that NVIDIA announced last week at CES. But it was not necessarily the use cases—which were pretty commonplace—that were the real story. Much more compelling was the vision of what agentic development could be. The first thing that stuck out was that these are partner-driven solutions. This is in contrast to what we have seen so far, which have been siloed and internally developed agentic solutions. With those, you basically have to use a homogeneous stack of technology to realize the value. And, yes, there is still a need to use the NVIDIA AI Enterprise platform to deploy the blueprints that were announced, but the simple thought of co-development is good to see—and hasn’t been highlighted enough.

Second, I am very interested in how NVIDIA is thinking about agentic AI in the physical world. That is what I consider a second leap from what we are seeing so far. Today’s agents are very much bound to a cloud or a platform. The first leap I am hoping to see is a leap to the on-premise compute world. This could mean collaboration between the AIs on devices such as an AI PC or an iPhone and the cloud (edge AI, so to speak). The second leap is the same idea but to physical devices and robotics. Again, it’s refreshing to see NVIDIA paint a picture of the agentic world that is so visionary.

Over the past month I have been researching AI development platforms, and I have some research coming out on that very soon. But the deeper I have gotten into the topic, the more I’ve realized that each platform regards different user roles with different priorities. It is almost as if each vendor started development from a completely different place, yet they all ended up close enough to each other that we now have a new category of solution. This is a very good thing early in a product lifecycle. By having a broad base of solutions to choose from, the market will have a better opportunity to judge what ends up being the best use of the technologies.

To that point, I want to mention that the newly released Azure AI Foundry from Microsoft is, to my mind, the first of these platforms to really take on the IT management aspects of the problem set. And while it may not have all of the very coolest developer features we see from the competition, it does highlight an under-represented set of requirements that will be essential for enterprise deployment and success.

Last week a member of the media reached out to me to discuss a topic that got me thinking—how value is being redefined in the age of generative AI. Here’s an example: for as long as we can all remember, having lots of data was critical to drive decision making. And if you had good and exclusive data, that was highly valuable. But now that AI can assemble and infer data so quickly and so well, has the value moved away from mere data possession towards reasoning and prediction? Or will people now go to greater lengths to hoard the best data? I am leaning towards reasoning winning the day, but I do think it’s a great topic for reflection. (I’ll let you know when the article comes out.) At the very least, I expect that we will see a more distinct break between data and reasoning in the business world—like we already do between training and inference within AI.

I was quite intrigued by the agentic blueprints that NVIDIA announced last week at CES. But it was not necessarily the use cases—which were pretty commonplace—that were the real story. Much more compelling was the vision of what agentic development could be. The first thing that stuck out was that these are partner-driven solutions. This is in contrast to what we have seen so far, which have been siloed and internally developed agentic solutions. With those, you basically have to use a homogeneous stack of technology to realize the value. And, yes, there is still a need to use the NVIDIA AI Enterprise platform to deploy the blueprints that were announced, but the simple thought of co-development is good to see—and hasn’t been highlighted enough.

Second, I am very interested in how NVIDIA is thinking about agentic AI in the physical world. That is what I consider a second leap from what we are seeing so far. Today’s agents are very much bound to a cloud or a platform. The first leap I am hoping to see is a leap to the on-premise compute world. This could mean collaboration between the AIs on devices such as an AI PC or an iPhone and the cloud (edge AI, so to speak). The second leap is the same idea but to physical devices and robotics. Again, it’s refreshing to see NVIDIA paint a picture of the agentic world that is so visionary.

Over the past month I have been researching AI development platforms, and I have some research coming out on that very soon. But the deeper I have gotten into the topic, the more I’ve realized that each platform regards different user roles with different priorities. It is almost as if each vendor started development from a completely different place, yet they all ended up close enough to each other that we now have a new category of solution. This is a very good thing early in a product lifecycle. By having a broad base of solutions to choose from, the market will have a better opportunity to judge what ends up being the best use of the technologies.

To that point, I want to mention that the newly released Azure AI Foundry from Microsoft is, to my mind, the first of these platforms to really take on the IT management aspects of the problem set. And while it may not have all of the very coolest developer features we see from the competition, it does highlight an under-represented set of requirements that will be essential for enterprise deployment and success.

Last week a member of the media reached out to me to discuss a topic that got me thinking—how value is being redefined in the age of generative AI. Here’s an example: for as long as we can all remember, having lots of data was critical to drive decision making. And if you had good and exclusive data, that was highly valuable. But now that AI can assemble and infer data so quickly and so well, has the value moved away from mere data possession towards reasoning and prediction? Or will people now go to greater lengths to hoard the best data? I am leaning towards reasoning winning the day, but I do think it’s a great topic for reflection. (I’ll let you know when the article comes out.) At the very least, I expect that we will see a more distinct break between data and reasoning in the business world—like we already do between training and inference within AI.

Sam Altman, CEO of OpenAI, made a very interesting post on his personal blog about when he believes OpenAI could achieve artificial general intelligence (AGI), an advanced level of AI that can perform at human levels. It was only two years ago that OpenAI made the historic launch of ChatGPT. Just two months after its release, ChatGPT had 100 million active users, heralding AI’s potential as one of the most powerful technologies ever created. Beyond that, the launch transformed OpenAI from a small research lab into a major AI player. Today, an advanced version of the GPT platform handles more than one billion queries daily.

In the blog post, Altman shared personal anecdotes, including his unexpected firing from the company and the governance issues that followed, offering lessons learned in leadership and company management. The rapid growth of OpenAI required him to build the company culture and infrastructure almost from scratch. That led to both successes and setbacks. Altman admitted to his own failures in governance, particularly around his firing. In another lesson learned, he emphasized the importance of having diverse and experienced board members.

Now, here’s the most interesting part of his post. Altman predicts that AGI will be achieved in 2025 in the form of AI agents that will impact workforce productivity. Altman said, “We are now confident we know how to build AGI as we have traditionally understood it. We believe that, in 2025, we may see the first AI agents ‘join the workforce’ and materially change the output of companies.” Moving beyond AGI, Altman talks about focusing on superintelligence, envisioning a future where AI could dramatically enhance human capabilities and societal prosperity.

From my perspective, the trajectory towards more incremental and advanced AI capabilities looks doable. However, AGI needs a level of human-like reasoning and adaptability that can be applied over a wide range of tasks. That is a very complex goal. We are not there yet for the strict definition of AGI, but AI agents in the workforce with a limited form of AGI might be doable this year.

I believe that superintelligence is not possible at this stage or at any time within several decades, if ever. While AI has made tremendous advancements over the past decade, superintelligence involves numerous unknowns, including abstract reasoning, creativity, conscious thoughts, and problem-solving at the highest level. None of those is on the horizon yet—let alone all of them.

Oracle has launched the Exadata X11M data management platform, with a focus on driving extreme performance across three key workloads—AI (vector search), online transaction processing (OLTP), and analytics. Exadata is a combination of tuned hardware and software that enables organizations to accelerate performance of these key workloads while enabling greater levels of consolidation in the datacenter.

I like what Oracle is doing. For decades, Oracle Database has been the data management platform of the enterprise (97% of Fortune 500 companies run Oracle). It only makes sense that the company would take its IP and better enable core workloads that power the enterprise. The numbers are quite compelling across the board: Oracle claims vector search performance increases of up to 55% on storage servers and 45% on compute servers, along with 25% faster OLTP and analytics performance relative to the X10M platform. And this performance is delivered on-prem, in any major cloud, or in hybrid environments.

It can be very difficult for legacy infrastructure companies to pivot and maintain relevance as the market shifts around them. Oracle is unique in how it has smartly pivoted and taken full advantage of its footprint in enterprise data.

The data protection software market grew in 2024, driven in no small part by advanced cyberthreats and stricter regulations. Unsurprisingly, AI tools became much more important for automating governance, ensuring compliance, and detecting threats. Vendors such as Cohesity, Commvault, Rubrik, and Veeam Software improved their market presence through acquisitions, partnerships, going public, and adding new features to their platforms. Observability tools also progressed, integrating system monitoring with data protection for proactive solutions. Read more in my latest Forbes article about what I see ahead for data protection in 2025.

Zoho Analytics has grown into a full-fledged, AI-driven business intelligence platform. Its September 2024 release included more than 100 updates, with a big emphasis on expanding access to data analysis across different job functions. Considering its advancements in AI and machine learning, Zoho Analytics now competes with established BI solutions, enabling a broad range of users in different industries to make more informed decisions. Check out the recent MI&S Research Brief about Zoho Analytics from Melody Brue and me for more.

Extreme Networks launched its Extreme Platform One in early December. Platform One is positioned to allow IT professionals to manage and secure networks faster and more efficiently. The company claims that the offering has been developed based on customer feedback and aims to unify connectivity experiences with a single composable workspace and high degrees of AI-powered automation, and to deliver a simplified licensing structure. I believe that when Platform One becomes available in the second half of 2025, it will allow Extreme to compete with the likes of Cisco, HPE, and others more effectively given its historic focus on providing commodity connectivity infrastructure.

At the National Retail Federation’s show, SAP rolled out some new features for the retail industry. These include the SAP S/4HANA Cloud Public Edition, designed for retail, fashion, and related businesses, as well as an AI-powered shopping assistant. The company also shared plans for a loyalty-management solution for retailers and consumer goods companies, which is set to launch in late 2025. The updates are geared toward helping retailers work more efficiently and better connect with their customers.

In 2025, AI agents are expected to change the game in retail by enabling personalized customer experiences, flexible shopping options, and sustainability initiatives. At NRF, Microsoft highlighted tools such as Copilot and Dynamics 365 ERP agents that can handle routine tasks, improve operations, and make real-time decisions. This gives employees more time to focus on what matters most while improving efficiency, reducing costs, and helping build relationships with customers and suppliers.

Key statistics from Adobe’s 2024 Holiday Shopping Report reveal significant trends in online retail during the holiday season. Online retail spending reached a record $241 billion, representing an 8.4% increase compared to 2023. Additionally, spending on buy now, pay later (BNPL) options grew substantially, exceeding $18 billion and peaking at $993 million on Cyber Monday, which set a new single-day record. Mobile revenue accounted for 53.2% of online shopping, totaling $128 billion. This shift towards mobile spending and the increasing popularity of BNPL options highlight changing consumer preferences in digital payments and financing.

Indeed has released its 2025 U.S. Jobs & Hiring Trends Report, which includes interesting data points and trends for the workplace and workforce in the new year. Two significant trends stood out to me as poised to reshape the workforce in 2025, presenting challenges and opportunities for businesses and workers alike.

1. Demographic shifts and labor shortages: The U.S. is experiencing a decline in its prime working-age population, a trend with profound implications for labor supply. This demographic shift suggests that future workforce growth could hinge on immigration, potentially leading to persistent labor shortages across various sectors. Companies may need to reevaluate their talent acquisition strategies to focus on upskilling existing employees, embrace remote work to access wider talent pools, and implement aggressive retention initiatives.

2. The dual nature of AI: Artificial intelligence is rapidly transforming the workplace, potentially automating existing jobs while creating new roles. While estimates suggest that AI could automate millions of jobs, it’s also projected to generate millions of new positions requiring a blend of technical expertise and uniquely human skills such as empathy, creativity, and critical thinking. This duality underscores the growing importance of adaptability and continuous learning for workers at all levels.

The convergence of these trends presents a complex landscape. A shrinking workforce may accelerate AI adoption to address labor shortages, potentially increasing productivity but also raising concerns about job displacement. To thrive in this evolving environment, businesses and individuals must proactively adapt, embrace learning, and cultivate a workforce equipped for the demands of the future.

After last year’s CES, I predicted that Matter would reach its tipping point in 2025, becoming the preferred connectivity standard for new smart home product designs. I’m doubling down on that prediction this year because Matter ecosystems (platforms) are maturing, and consumer adoption is finally taking off.

1. Matter ecosystems: Certifying products and developing product-specific apps is becoming much easier.

• Easy product certification — “Works with” compatibility programs from Apple, Google, and Samsung agreed to accept Matter interoperability testing. Apple is already accepting these lab results, and Google and Samsung plan to do the same later this year. What about Amazon? Stay tuned. My take: This announcement validates Matter’s “universal interoperability” brand promise and encourages more device makers to get on board. Meanwhile, the economics are compelling—one interoperability test replaces three or four.
• Easy app development — As promised earlier this year, Google is opening up Google Home as a developer platform. The company just announced a new set of Matter Home APIs for Android developers, with iOS support coming in a few months. These APIs link partner apps with Google Home hubs to control devices and automation experiences. The apps connect directly to the Google Home runtime package on local, on-premises hubs. The runtime controls Matter devices without a round-trip to the cloud, reducing latency while improving reliability and privacy. Google Home’s installed base is over 40 million hubs, including Nest, Chromecast, Google TVs, and some LG TVs. My take: This is a big deal. Today, Matter standardizes connectivity, but CE manufacturers often require product-specific features at the ecosystem level. Creating new ecosystems is complicated and costly, and consumers don’t want a separate ecosystem for each product, so Google is on the right architectural path here. Google Home hubs connect local devices without round trips to the cloud, and APIs let partners extend the ecosystem with product-specific features and experiences. Other ecosystems (Apple, Amazon, and Samsung) already have comparable APIs, and could add on-premises control logic to their hubs. I hope ecosystem companies consider standardizing APIs or at least using similar design patterns.

2. Matter products: The tech news outlets will review all the new Matter products that debuted at CES, but here are my short takes on a few that caught my eye.

• Resideo (Honeywell Home) announced the Honeywell Home X2S Matter-enabled smart thermostat ($79.99 MSRP). My take: The low price point proves that adding Matter is cost-effective.
• GE unveiled two new wall-mounted Matter-based “Cync” dimmer switches with several interesting innovations, including single-device three-way circuits ($44.99 and $25.99). My take: It’s great to see major consumer brands support Matter with innovative, mainstream, reasonably priced products.
• LG’s over-the-range microwave oven with a 27-inch touchscreen and full Matter support created considerable interest at CES. It’s a smart TV, Matter hub, Thread border router, and home control panel. Oh yeah, it also microwaves food and has three cameras to show it cooking. This product is part of an industry trend to use touchscreens as the UI for appliances, from light switches to washing machines. My take: Some analysts dismiss this trend as silly, but we should take it seriously. LCD panels are inexpensive peripherals for smart appliances, so the question isn’t whether to use them, but how to use them. For instance, I see a rough road ahead for CE companies that envision these screens as advertising billboards or sales tools.
• Aqara is going all-in with dozens of Matter products and variants. Examples include control panel hubs, dial-based touchscreen controllers, touchscreen switches, light switches, dimmer switches, presence sensors, climate sensors, a doorbell camera, and a Matter hub. My take: Aqara is beating established brand names to the punch with a broad Matter product portfolio.
• Locks — Several companies introduced innovative Matter-enabled smart locks. Schlage’s first Matter product is the Sense Pro Smart Deadbolt. It uses UWB for hands-free unlocking. ULTRALOQ’s Bolt Fingerprint and Bolt Mission locks have Matter support, and the latter has UWB spatial awareness. My take: I’m pleased to see house locks get some of the great features we’ve had in car locks for years. I’m also bullish on UWB.

3. Industrial automation: There were hundreds of industrial announcements at CES. Here are three examples.

• NVIDIA — For IIoT and edge tech, the quote of the week was from Jensen Huang: “The ChatGPT moment for general robotics is right around the corner.” He defined three kinds of robots that require no special accommodations to put them into service—agentic (because they’re information workers), self-driving vehicles (because roads are already in place), and humanoid (because they fit directly into our world). I think enterprise and industrial operations technology is a fourth AI embodiment. Industrial IoT systems are increasingly autonomous and adaptive but lack the uniform connectivity and interoperability needed to act “robotic.” This is the new definition of industrial IoT—enabling robotic physical infrastructure.
• NXP — The company has agreed to acquire TTTech Auto in an all-cash transaction valued at $625 million. NXP plans to upscale TTTech MotionWise in its CoreRide software, accelerating the shift from hardware-based designs to software-defined vehicles (SDV). TTTech stands for time-triggered technology, a set of techniques for synchronizing and scheduling events across distributed systems. My take: This savvy acquisition ensures the CoreRide platform can use standard networks for tightly timed, safety-related distributed automotive applications. CoreRide and TTTech technologies could also apply to manufacturing and other industrial applications, but NXP hasn’t confirmed that.
• Ceva-MediaTek collaboration — Imagine wearing VR headgear, turning your head, and the audio space remains fixed relative to the 3-D video space. Ceva’s RealSpace immersive spatial audio integrates this and other advanced audio processing techniques into MediaTek’s Dimensity 9400 mobile chipset. My take: Locking the audio space to the virtual visual world is very cool, and not just for gaming. For instance, spatial audio adds realism to Industrial digital twins.

IonQ recently completed its acquisition of Qubitekk, a quantum networking firm. The acquisition provides IonQ with advanced networking technology and a large number of new patents, bringing IonQ’s portfolio to over 600 patents. It also acquired an important networking asset in Qubitekk’s EPB Quantum Network, the first commercial quantum network in the U.S. That will enhance IonQ’s quantum networking capabilities and remote ion-ion entanglement. The integration of Qubitekk’s technology will likely provide IonQ with faster quantum network deployment, which will enhance its secure communications and distributed computing capabilities. This should push IonQ into a leadership position in quantum networking, which could result in new partnerships and/or contracts. Combining IonQ’s quantum expertise with Qubitekk’s networking experience could result in significant advancements in security and computational power. This acquisition is strategic for IonQ, given its dependence on networking for how it plans future scaling of qubits.

Recent negative news related to the Palo Alto Networks Expedition firewall migration tool may be overblown. The tool was offered as a free utility to migrate configurations from third-party firewalls to Palo Alto Networks’ next-generation firewall platform, but it was never intended for production deployments. There is no evidence of active exploitation, but even though the company retired the tool last year, patches have been issued and production migration tools have been provided to its customers.

In 2025, sports tech is sure to keep evolving as part of the ongoing transformation of how fans experience games and connect with their teams. For example, the platform Cosm and Meta’s Xtadium app are bringing sports into virtual reality. Meanwhile, streaming services—as we saw in 2024 with Peacock during the Summer Olympics, Netflix with boxing and the NFL, and AWS with the NFL—are expected to expand with AI features that include personalized highlights and real-time stats. This tech is also branching into other areas of entertainment and music, with AI shaping everything from songwriting to virtual concerts and even influencing events like the Grammys. We can expect platforms like TikTok and YouTube to continue blending sports, music, and entertainment, giving creators and fans new ways to connect and engage.

Last week, Dell announced substantial upgrades to its AI PC portfolio, highlighting enhancements in performance and sustainability. This initiative reflects the increasing importance of sustainability in business, a trend expected to continue influencing industry strategies through 2025. Dell’s approach includes implementing circular design principles, such as modular components and greater use of recycled materials, to extend product lifecycles and minimize e-waste. The company’s initiatives to improve energy efficiency, battery life, and repairability likewise underscore its commitment to addressing environmental concerns while catering to the performance demands of the AI PC market.

AT&T recently announced a customer guarantee for consumers and small businesses that use its wireless and fiber networks. Any customer who experiences a fiber outage of 20 minutes or more or a wireless outage of 60 minutes or more will receive compensation in the form of a billing credit. Additionally, the company is setting a goal for its customers to reach a call-center technical expert within five minutes or receive a callback at a chosen time, as well as a commitment to send a field technician the same day or next day for unresolved issues. AT&T Guarantee is a significant move for the operator, given that it’s the first of its kind for consumers, and I expect many of AT&T’s competitors will respond with similar commitments.

My biggest surprise of 2024 was learning how developers have embraced AI assistance. Developers are a smart and often skeptical group of people. But time and again, I heard stories about devs paying out-of-pocket for assistant technologies to speed up their work. I expected more cynicism about AI’s ability to help with coding — which tells me that the technology must be pretty good.

I have two predictions for 2025:

• Agentic development will continue to be big in the first half of the year, especially since we now have some highly viable agentic development platforms including Bedrock (AWS), AI Foundry (Azure), and Agentspace (Google). I also expect to see non-cloud competitors to these platforms this year. (Red Hat, can you hear me??)
• AI governance and controls will be a massive challenge. We are already seeing technologists grapple with the implications of AI usage and apps. But once line-of-business professionals get comfortable with pervasive AI use, we will see IT and legal departments flex their muscles in a meaningful way.

My biggest surprise of 2024 was learning how developers have embraced AI assistance. Developers are a smart and often skeptical group of people. But time and again, I heard stories about devs paying out-of-pocket for assistant technologies to speed up their work. I expected more cynicism about AI’s ability to help with coding — which tells me that the technology must be pretty good.

I have two predictions for 2025:

• Agentic development will continue to be big in the first half of the year, especially since we now have some highly viable agentic development platforms including Bedrock (AWS), AI Foundry (Azure), and Agentspace (Google). I also expect to see non-cloud competitors to these platforms this year. (Red Hat, can you hear me??)
• AI governance and controls will be a massive challenge. We are already seeing technologists grapple with the implications of AI usage and apps. But once line-of-business professionals get comfortable with pervasive AI use, we will see IT and legal departments flex their muscles in a meaningful way.

I believe that in 2025, 5G will become an accelerator for AI and gain more prominence as a key component for enabling AI. While many vendors talk about edge AI and running models on devices, the reality is that many models simply cannot run on the device and that hybrid AI will remain in the future for a long time. The only way for hybrid AI to work effectively is with an always-on connection; this is really easy for smartphones but more challenging for PCs, and we might actually see 5G PCs grow as a result of that this year. Additionally, XR is an excellent interface for AI, and—conversely—AI is an accelerant for XR capabilities and growth. I believe we will see the new Android XR spatial OS as a proof point for that interconnection in both MR and AR products and solutions.

AI is a snowball that gathers larger amounts of material and grows bigger and more capable every day. In fact, it is accelerating in functionality and scope. Every morning when I open my inbox, it is filled with more new information about larger models, new features, larger funding, new funding, new startups, better reasoning, and so on.

I was curious about how much information is distributed about AI on a daily basis. I thought Google’s Gemini search might give me a general idea, but after spending a few paragraphs explaining why it couldn’t offer a hard number of publications about AI, Gemini said, “However, I can offer some informed speculation: Considering the immense volume of information Google indexes, the widespread interest in AI, and the constant stream of new content, it’s safe to say the number of publications is extremely large. We’re likely talking about millions, perhaps even tens of millions, of pages.”

So without using any specific numbers, here’s how I see AI’s growth. Most everyone has seen videos of the earth in comparison to the size of other objects in the universe. It starts like this: A basketball-sized Earth is initially shown next to a stadium-sized Sun. Then, the giant star Betelgeuse appears on screen, dwarfing the sun and the earth. Betelgeuse is as big as a city block. But Really Big is yet to come. When the massive star called VY Canis Majoris appears, Betelgeuse shrinks in comparison. What was once a giant star is now an insignificant sandbox compared to an entire beach. Finally, a supermassive black hole covers the screen. Relative to it, the Earth and Sun are nearly invisible specks.

Today, AI is like Earth in the video, but it will grow to the size of the bigger stellar objects over time. At least that’s how I envision its long term growth—AI of today is a speck compared to what it will likely become in 25 or 50 or 100 years. Let’s hope humanity has the wisdom and ability to use it wisely.

In 2024, despite the rise of AI, customer service saw a surprising trend: a renewed emphasis on human interaction. 77% of customers said they preferred an immediate connection with a person, and 81% would rather wait for a live agent than interact with a bot. While businesses strategically blend AI with human agents to enhance efficiency, customers overwhelmingly prefer connecting with real people for a more nuanced and practical experience. In 2025, AI-powered voice data analysis will become crucial, enabling hyper-personalized experiences by detecting emotions and predicting needs in real time. While omnichannel remains necessary, companies must prioritize voice interactions and leverage AI to extract valuable insights from this channel.

Meanwhile, CRM trends in 2024 revealed a shift towards user-friendly, self-service solutions, empowering businesses of all sizes. This trend will continue into 2025, with “CRM à la carte” and low-code/no-code platforms allowing for easier customization and simplified data entry. To combat data silos, companies are increasingly unifying teams under a single CRM system, streamlining communication, reducing errors, and enhancing data-driven decision-making.

2024 saw a dynamic in the compute silicon space that somewhat parallels the storage market: a bifurcation of silicon along AI versus non-AI lines. Bespoke silicon for bespoke workloads and functions has existed since semiconductors have been in existence. However, AI is different. The needs of AI have led to a renewed focus on semiconductors and startups such as Cerebras, Tenstorrent, Untether AI, and so many others. Further, the market has accelerated growth in the custom silicon space as companies like Broadcom and Marvell have benefited greatly from the needs of hyperscalers, which have very specific computational and power requirements around training, inferencing, moving, and securing data. So while NVIDIA has commanded the AI silicon market overall, it has been somewhat surprising to see the amount of VC funding that has gone into the silicon startup space.

I believe that 2025 will see this trend continue. AI inference will take center stage alongside AI training, with increasing focus on the many startups serving this market. Additionally, smaller functions along the AI data journey that currently add significant latency will spawn a new wave of silicon innovation to drive better performance and security. As in recent years, I expect to see significant VC funding going to seed startups that help in the collection, storage, preparation, and movement of data in the AI pipeline.

As in the silicon market, 2024 saw somewhat of a bifurcation of the storage market as high-performance storage vendors such as VAST Data, Weka, and DDN pivoted to address the AI data pipeline and data management. While storage is a critical element of the AI equation, gathering, cataloguing, and readying enterprise data is where the real complexity of AI becomes real for business and IT leaders alike as projects move from conceptual to operational. The early-mover status achieved by VAST and other high-performance computing storage players is logical, as these companies have been focused on the more advanced functions of storage systems for the sake of accelerating workload performance. This is why we saw VAST’s valuation skyrocket through 2024, at the same time the profiles of Weka and DDN rose considerably.

I believe 2025 will see the storage market shift in both technology and messaging as these upstarts continue to increase awareness, share, and valuation. We have already seen NetApp begin its evolution, and both Dell and HPE have quietly made moves that better position their respective portfolios. While AI-washing in terms of messaging is no surprise (because every vendor tries to exploit market trends), the investment in technology being made by these companies is the real tell; it signals that they see AI as fundamentally shifting enterprise IT organizations, in terms of both operations and technology consumption.

One partial outlier in this equation is Pure Storage. While the company continues to broaden its support for enterprise AI through its portfolio, it has not lost sight of the enterprise storage needs that exist outside of this one significant workload. However, the company seems to be taking a more measured approach in terms of allowing the market to come to it and meeting customers where they are. I believe it is this approach that has led to the company to regularly recognize roughly 10% year-over-year growth in its quarterly financial reports.

The enterprise application market experienced significant growth from 2023 to 2024, with the market size increasing from $335 billion to as much as $363 billion, depending on the source. This represents an approximate growth rate of 8.4% year over year. However, despite this growth, customer dissatisfaction with enterprise software vendors rose in 2024. This dissatisfaction primarily arose from perceived unfair pricing strategies and a lack of clear value delivered by vendors. This indicates a market shift in which customers are demanding greater transparency and a better ROI from their software purchases.

In 2025, I expect deeper integration of AI within application ecosystems. At the same time, customer companies will prioritize trust and demonstrable value, seeking clearer ROI and more flexible pricing models from vendors. Unlike the 2024 emphasis on feature expansion, in 2025 we will see more focus on efficiency, interoperability, and user-centric design. This shift reflects a maturing market where vendors must adapt to needs of discerning customers that have multiple buying personas and significant budget constraints.

ERP systems got a shake-up in 2024 with AI and vendor modernization efforts. But what really stood out was the shift in mindset—enterprises realized adopting ERP isn’t just about new tech. It’s also about getting their data organized and making sure their teams are ready for change. Functionality matters more than features. If anything, 2024 made it clear that ERPs aren’t just about keeping the lights on—they’re a critical tool for businesses to grow, adapt, and stay competitive.

The payoff for those who got it right was obvious. Modern ERPs centralize data across departments, automate routine tasks, and deliver sharper insights with improved analytics. Cloud technology—pushed hard by vendors—has made these systems more flexible, mobile, and user-friendly, while also being easier for vendors to support. Even so, with an estimated two-thirds of enterprises still using on-premises setups, there’s an emphasis on moving to hybrid or fully cloud-based systems.

Looking ahead, I see data strategies and ERP systems shaping up to be even more important in 2025. Cloud adoption will keep growing because it’s flexible, cost-effective, and lets enterprises keep their locations connected while enabling their people to work from anywhere. Managing data will still be a big deal, with businesses focusing on keeping the data clean, secure, and well-governed, with better tools for protecting sensitive info.

In many industries, supply chain management will also stay front and center. With IoT increasing in functionality and especially getting better at providing data, ERPs will get better at real-time tracking and analytics, making it easier to handle inventory, logistics, and demand planning. (See Bill Curtis’s “IoT and Edge” entry in this newsletter for more on how this part of the data landscape is changing.) I expect pricing to move toward consumption-based models (versus user-based) to make it easier to bring more employees onto each system.

We’ll also continue to see ERP systems designed specifically for different industries. But here’s the thing: all this technology only works if businesses manage change well. Teams need support to adapt to new workflows, or it won’t stick. And finally, sustainability will be a bigger part of the picture, with ERPs helping businesses track environmental goals and ethical sourcing.

In 2024, operational data was the unsung hero of digital transformation. While LLMs, generative AI, and agentic AI captured headlines, operational technologies (OT) quietly emerged as critical enablers of enterprise digital transformation. Enterprises with significant physical assets discovered that fusing OT and IT data into a company-wide, multimodal, real-time data estate transforms AI-enhanced ERP, SCM, and BI applications from reactive to proactive. (Robert Kramer’s entry on ERP and SCM elsewhere in this newsletter gives more perspective on trends affecting those software vendors and their customers.) This profound change upgrades decision-making, enhances process efficiencies, and provides a holistic context for advanced industrial automation. The rapidly growing ROI for OT-IT integration projects creates insatiable demands for OT data.

However, despite compelling integration business cases, most OT data remains inaccessible due to the complexity, cost, and security risks of connecting OT systems with mainstream enterprise applications. This is the OT-IT gap—the chasm between the uniform, managed world of IT and the heterogeneous, chaotic world of industrial IoT (IIoT).

Motivated by AI-driven demand for operations data, enterprise software suppliers that are scrambling to find more efficient ways to bridge the OT-IT gap are adopting a straightforward “data first” approach. Instead of trying to manage devices from end to end, just grab the data. Replace complicated, costly, hard-coded, application-specific device-to-cloud connectivity and device management solutions with simple cloud interfaces for data, events, and status. This approach provides immediate access to IIoT machine data and enables OT software to evolve independently from cloud-native IT systems. Multimodal AI applications can use many types of unstructured IIoT data as is, further reducing device-side software complexity.

Recent announcements from AWS, Google, Honeywell, Microsoft, Qualcomm, and other major cloud frameworks and ERP suppliers confirm this trend. The goal is clear: feed the rapidly growing market for AI-enhanced business transformation with massive amounts of OT data via standard protocols and simple APIs. In other words, simplify getting OT data from IIoT devices.

For 2025, I’m watching three enterprise edge trends and one consumer trend.

1. CSP and ERM frameworks simplify and accelerate OT data collection, processing, and correlation for AI-powered enterprise applications. AI is now IIoT’s “killer app.”
2. IIoT devices transition from customized, end-to-end mashups to scalable platforms supporting multiple enterprise frameworks via simple interfaces.
3. Middleware companies fill the gaps, providing industry-specific connectivity, data, edge analytics, and device management services.
4. For “smart home” consumer applications, 2025 is the year Matter reaches its tipping point, with significant design wins and increased adoption. Other vertical industries are carefully watching Matter’s standardization efforts, learning from its successes—and mistakes.

In 2024, platforms such as Zoom, Microsoft Teams, and Webex evolved into essential all-in-one communication and collaboration business tools, integrating features including whiteboarding, collaborative documents, and project management functions. This trend towards unified business platforms will accelerate in 2025, combining previously separate tools. Expect deeper integrations, such as what we’ve seen this year with Adobe Express within Box and the ability to create Jira tickets in the Grammarly extension.

Inclusivity will also be a significant focus in 2025, with accessibility features such as real-time translation and closed captioning becoming standard. Companies such as Ava are leading the way with tools specifically designed for individuals who are deaf or hard of hearing, while companies such as Google continue to prioritize accessibility.

As hybrid work persists and collaboration becomes more complex, security concerns remain paramount. Organizations will demand collaboration tools with robust security features, including end-to-end encryption and compliance with evolving data protection regulations.

With Qualcomm’s introduction into the PC market as a chipset vendor, we’ve seen new levels of competition in the space—something that I don’t think we’ve seen in probably the last 25 years. While the introduction of Copilot+ PCs with Qualcomm’s Oryon-based Snapdragon X Elite processors wasn’t necessarily the smoothest (lots of Arm app compatibility needed to get worked out), it did present an alternative offering that pushed the incumbents to accelerate their roadmaps and improve their execution; as a result, the PC market is now far more competitive and faster paced. I expect that this trend will continue to accelerate in 2025 as PC OEMs continue to negotiate with the chip vendors for better products and pricing, which I believe will ultimately benefit the consumer and accelerate the uptake of the AI PC.

Over the past five years, quantum computing has made significant progress—with 2024 as a big contributor to that progress. IonQ has become a public company. IBM has created a roadmap with corresponding technologies to push superconducting qubits past the 1,000 mark. Atom Computing has firmed up its neutral atom technology and has begun pushing aside barriers with its own 1,000-qubit machine. Quantinuum’s H-2 quantum processor has an unbelievably high quantum volume. Microsoft and Quantinuum are both advancing topological computing. Finally, the ecosystem has made several breakthroughs in quantum error correction. In fact, Google’s latest Willow chip actually reduces the error rate as more qubits are added.

In 2025, the trajectory of quantum computing will continue to be shaped by technological breakthroughs, increased investment, and the integration of quantum into broader technological ecosystems. We’ll also see IonQ begin to network its quantum processors together for increased power and scaling. IBM will continue to move forward with advancements in post-quantum cryptography. (More on that in this article.)

I also expect to see some early movement in using quantum for financial applications, such as applying QAOA (the Quantum Approximate Optimization Algorithm) for portfolio optimization and possibly some real-time analysis. JPMorgan Chase has a large portfolio of financial operations where quantum computing could replace parts of classical systems.

Meanwhile, PsiQuantum and Photonic are well on their way to creating photonic quantum computers. We will also see the beginning of real supercomputers that integrate AI, Quantum, and HPC.

Overall, I expect 2025 to be a year of early proofs-of-concepts.

The CrowdStrike global IT outage in 2024 was a seminal moment not only for the cybersecurity industry but also for developer operations in general. The million-dollar question—more likely a billion-dollar question, given the collateral damage—is what could have prevented such a devastating occurrence. Modern continuous integration and continuous delivery/deployment pipelines coupled with test environments are designed to provide a failsafe mechanism that catches bad code and allows rollback before catastrophe strikes. Integrations will continue among software platforms to provide the highest levels of endpoint security. I believe the CrowdStrike incident will serve as a learning experience for other IT solution providers.

Cybersecurity in 2025 will be defined by its ability to adapt to a rapidly evolving threat landscape. Identity access solutions will embrace zero trust architectures, automation, and seamless integration. Endpoint security will rely on AI-powered analytics and lightweight architectures. Cisco’s ongoing momentum with its security cloud, plus recent innovations from Microsoft, Okta, Palo Alto Networks, and others, demonstrate an industry move toward unified, scalable, and AI-enhanced platforms. Organizations must stay ahead of bad actors by investing in modern cybersecurity infrastructure, employing a culture of security awareness, and adopting an integrated approach to cyber defense that facilitates improved security outcomes.

2024 saw both progress and contradictions in tech sustainability. While green datacenters and energy-efficient AI emerged, the industry’s footprint remained significant. “Greenhushing” highlighted the need for transparency as companies became more cautious about publicizing their environmental efforts. In 2025, sustainability will shift from an optional good deed to a core business imperative driven less by a sense of virtue and much more by the energy demands of advanced technologies, regulatory pressures, and investor scrutiny. Companies must integrate sustainability into all operations, as it will become a key differentiator, separating leaders from laggards.

Google has just announced Agentspace, which is a no-code-ish environment geared towards personal work productivity. Many of Google’s competitors are also playing in this market, so it’s no surprise Google is getting into it. Each of the major cloud vendors has both an AI development platform (in Google’s case Vertex AI) and associated tools for different personas. Google’s foray into power users is quite interesting in that it leverages both Vertex AI and the viral NotebookLM project. That said, it’s different from many of the other agentic approaches out there. In some ways it may be ahead of its time. Stay tuned for more on this topic soon.

Last week I got to spend some time with Matt Gierhart, who leads the custom app dev practice at IBM Consulting. While we spoke a bit about tools and AI assistants we both like, we then shifted focus to IBM Garage, which is a collaboration space for delivering projects with IBM clients. What stood out most was how IBM can quickly present multiple scenarios for a customer decision-making process—“Why should we do one feature versus another?,” for example. Developing these scenarios often takes time and data, but using gen AI is a way to accelerate the gathering and preparation process.

Finally for now, I think we have hit a point in the maturity of AI platforms where we can start to define and compare them in a meaningful way. What’s interesting is how much the diversity of tools has driven both the awareness of the need for a platform as well as the functionality. In 2025, I predict that competition at the AI platform level will overtake the LLM wars. More to come in this area, particularly looking at Amazon Bedrock, Microsoft Azure AI Foundry, and Google Vertex AI.

Google has just announced Agentspace, which is a no-code-ish environment geared towards personal work productivity. Many of Google’s competitors are also playing in this market, so it’s no surprise Google is getting into it. Each of the major cloud vendors has both an AI development platform (in Google’s case Vertex AI) and associated tools for different personas. Google’s foray into power users is quite interesting in that it leverages both Vertex AI and the viral NotebookLM project. That said, it’s different from many of the other agentic approaches out there. In some ways it may be ahead of its time. Stay tuned for more on this topic soon.

Last week I got to spend some time with Matt Gierhart, who leads the custom app dev practice at IBM Consulting. While we spoke a bit about tools and AI assistants we both like, we then shifted focus to IBM Garage, which is a collaboration space for delivering projects with IBM clients. What stood out most was how IBM can quickly present multiple scenarios for a customer decision-making process—“Why should we do one feature versus another?,” for example. Developing these scenarios often takes time and data, but using gen AI is a way to accelerate the gathering and preparation process.

Finally for now, I think we have hit a point in the maturity of AI platforms where we can start to define and compare them in a meaningful way. What’s interesting is how much the diversity of tools has driven both the awareness of the need for a platform as well as the functionality. In 2025, I predict that competition at the AI platform level will overtake the LLM wars. More to come in this area, particularly looking at Amazon Bedrock, Microsoft Azure AI Foundry, and Google Vertex AI.

Synopsys has become the first silicon company to introduce IP for UALink, the new scale-up specification that can connect up to 1,024 accelerators in support of LLM training, HPC, and other workloads. This is a significant announcement as it gives considerable weight to the recent launch of the consortium’s version 1.0 spec, which we covered in detail last month.

I would expect that this could mean that UALink-ready solutions might hit the market by mid-2026. In this connection, I will be tracking companies like AMD, Intel, Arm, and Astera Labs—along with any developments in NVIDIA’s NVLink connectivity spec.

Is storage cool again? Along with the rush of AI adoption comes an extreme focus on data. And of course, data management is highly dependent on storage. Because of this, the market has seen the arrival of a number of storage companies that index heavily on data management. And we also see a lot of traditional storage players evolving their products and messaging to orient around data management and data protection.

I’ve been in several engagements in this second half of 2024 to discuss and advise storage vendors on everything from product strategy to positioning and messaging. I say this to highlight how much companies are gearing up for the data wars of 2025 and beyond.

Each engagement ends with a similar set of takeaways: remove complexity, drive toward an autonomous state, ensure scale, and consider (and speak to) the full range of enterprise requirements—not just AI. Some companies do this better than others, and we see the results as they continue to grab market share.

Congratulations to Cohesity and Veritas Technologies on the successful completion of their business combination. It represents a significant shift in the data security and management industry—something I covered in detail in my research paper on the combination a couple of months ago.

Cohesity’s president and CEO, Sanjay Poonen, notes, “This deal combines Cohesity’s speed and innovation with Veritas’ global presence and installed base.” The combined entity will serve over 12,000 customers, including 85 of the Fortune 100, with projected revenues of around $2 billion for the 2025 fiscal year. You can read more in the announcement about the deal’s completion.

Adobe’s 2025 Creative Trends Forecast predicts four major design trends for the upcoming year. “Fantastic Frontiers” emphasizes surreal and imaginative visuals influenced by AI and gaming. “Levity and Laughter” underscores the growing importance of humor in engaging audiences. “Time Warp” blends futuristic and historical elements to create a nostalgic yet modern aesthetic. And “Immersive Appeal” focuses on multisensory experiences that combat screen fatigue and prioritize deeper brand engagement.

These trends reflect consumer desires for both escapism and authentic connection. The predictions are informed by a notable rise in experiential spending, illustrating how these trends resonate with the longing for adventure and genuine experiences. Adobe’s data insights provide a solid foundation for these predictions, and I look forward to seeing how creative trends play out in the new year.

The introduction of CameoX, a new onboarding policy by the fan-connection app Cameo, aims to make it easier for content creators including YouTubers to join the platform. By simplifying the enrollment process to a basic form and identity verification, Cameo hopes to attract a broader range of talent, potentially offering an alternative revenue stream for creators who may not be able to sustain themselves solely on platforms like YouTube, Instagram, or Twitch. However, it remains to be seen whether this will be enough to lure YouTubers away from that platform’s established audiences and revenue streams. The success of CameoX will depend on its ability to provide significant financial incentives and unique engagement opportunities that differentiate it from other popular creator platforms. With over 31,000 new creators joining through CameoX and contributing to millions in earnings, the platform is taking a promising step. Still, its long-term impact on attracting and retaining top talent is yet to be seen.

At Microsoft Ignite 2024, significant updates to Microsoft Fabric stood out for me. These updates improve data management for faster AI development, but that’s just the tip of the iceberg. During Ignite, I had the chance to sit down with Arun Ulag, corporate vice president for Microsoft Azure Data, about how tools such as OneLake and Fabric Databases unify workflows and simplify data access and support AI solutions. Read more of my analysis on Microsoft Fabric in my latest Forbes article.

HPE’s recent Q4 earnings rang in a record for top-line quarterly revenue, and the company’s guidance for total revenue, earnings per share, and free cash flow for the fiscal year are all above guidance. Networking remains an area with opportunities for improvement, but I expect that the completion of the Juniper Networks acquisition—expected in early 2025—will bring material synergies thanks to a fortified engineering effort and combined IP portfolio. If the company can crack the code on delivering more sustainable AI infrastructure across the board, especially by leveraging its applied research with HP Labs and Juniper’s Beyond Labs, it could provide significant tailwinds for future top-line revenue and margin improvement.

I attended IBM’s Strategic Analyst Forum in Boston last week. One of the highlights was how IBM is partnering with competitors such as Oracle, SAP, Microsoft, AWS, and others to help customers achieve IT transformation success. I was particularly impressed with IBM’s Garage methodology, an approach to digital transformation designed to develop solutions that address real business needs. It stresses building systems step by step. By working closely with IBM and its partners, clients can create solutions that effectively address challenges and are ready for real-world implementation. One area I felt could benefit from more focus is effective change management—a topic you can read more about in an article I wrote earlier this year.

One of the most significant 2025 IoT tech trends is the maturation of embedded application development. Traditional embedded developers constructed custom platform software stacks, including board support, OS, I/O, network, security, connectivity, and device management. However, the days of building complete software stacks for IoT devices are ending as silicon suppliers and independent software vendors offer complete platforms that support application development right out of the box. Developers can now begin writing application code immediately after unboxing the development kit. And building those applications using standards-based open-source components further reduces undifferentiated overhead. My advice: “If it’s not differentiated, don’t build it—buy it.”

I see more evidence every week that this “platform-based IoT” trend is accelerating. Here are three examples. First, Synaptics posted a demo of a contextual AI voice assistant application operating entirely on-device with no cloud dependencies. The demo uses an off-the-shelf Synaptics Astra Machina SL1680 development board. Developers access all the software required for this demo via GitHub, including the Yocto Linux OS and all necessary support software. According to the company, a developer can get the demo up and running “in a day.” This is a great example of a silicon supplier providing a complete software stack that lets developers focus on applications immediately and avoid writing undifferentiated code.

Second, Nordic Semiconductor just launched an impressive prototyping platform with a catchy name—the “Thingy:91 X.” This board features the nRF9151 system-in-package cellular module (LTE-M, NB-IoT, DECT NR+, GNSS positioning) with an Arm Cortex M33 system processor. The nRF002 companion chip adds SSID-based Wi-Fi location tracking. Expansion options from Qwiic, STEMMA QT, and Grove plug right in. The battery-powered board comes bundled with preloaded SIM cards from Onomondo and Wireless Logic, so it’ll connect to the nRF cloud right out of the box. Nordic supports developers with a comprehensive SDK and courses from its Nordic Developer Academy. Getting started with IoT cellular development is a rough road, but this “thingy” promises to make it smoother.

Finally, Matthias Bösl, head of hardware engineering at Tado (a home energy management company), posted this insightful comment: “In the past, a solid 30 percent of our development team was occupied with connectivity and the platform alone. The open source concept of Matter and Thread plus standardization ensures that we can concentrate better on things that offer our customers real added value.” Off-the-shelf IoT platforms also reduce the technical debt associated with long-term support, so the total cost savings are probably much higher in the long run.

For the past several years, the MITRE ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge) framework has provided an analysis of cybersecurity threat actor tactics, techniques, and procedures. In the process, it has measured how well endpoint security solutions detect and prevent cyber threats. This year’s Round 6 focuses on ransomware emulation and MacOS infiltration by a North Korean threat actor profile, including adversary behaviors and defensive capabilities.

Palo Alto Networks performed exceptionally well compared to other participants this year. The company’s Cortex XDR set a record as the first participant to achieve 100% detection with technique-level detail and no configuration changes for a second year in a row. Additionally, it prevented eight out of 10 attack steps while maintaining zero false positives with that newly introduced metric. Those are impressive results, and they reinforce my positive impressions of the company’s cybersecurity platform strategy, execution, and Unit 42 Threat Research Center capabilities after my meetings with the executive leadership team in November.

As I think back about the best uses of technology in sports this year, my mind keeps returning to Intel’s involvement in this summer’s Olympic Games. Intel’s AI technologies played a significant role at the 2024 Paris Olympics, enhancing the event in multiple ways. The Athlete365 platform employed AI to provide real-time, multilingual support for athletes, facilitating communication and knowledge sharing. AI-powered systems also automatically generated personalized highlights for fans, creating a more engaging viewing experience. Furthermore, Intel’s technology enabled the creation of 3-D videos and AR clips, offering interactive media experiences. The company’s processors facilitated 8K live streaming with low latency, ensuring high-quality broadcasting. These innovations showcase the growing potential of AI not only to enhance sporting events but also to transform various aspects of daily life by optimizing workflows, accelerating innovation, and improving performance across diverse fields.

I appreciate Intel’s transparency in showcasing the technology behind these advancements. As Robert Kramer and I often discuss on the Game Time Tech podcast, understanding how technology shapes significant events like the Olympics is essential. With these sports technology partnerships, it’s refreshing to see the story behind the tech portrayed in an accessible and relatable way. This level of transparency also helps educate the public about the increasing impact of AI on our lives.

What are the top sports technology trends to look for in 2025? Throughout this year on our Game Time Tech podcast, Melody Brue and I have explored how the sports industry is adopting new technologies to improve athlete performance, reduce injuries, engage fans, and modernize team management. Looking ahead to 2025, some key developments include AI coaching apps and computer vision for real-time movement analysis and injury prevention, wearable devices that monitor performance data, and AR-enhanced broadcasts that provide real-time stats and multiple viewing angles. Other advancements include improvements to the use of video assistant referees (VARs) for fairer in-game decisions, AI systems such the NFL’s Digital Athlete for injury prevention, data-driven tools for scouting talent, and VR simulations that offer realistic training environments. In 2025, Mel and I will continue discussing how these technologies are shaping both professional and amateur sports. Read more about 2025’s top trends, and be sure to check out our latest GTT podcasts.

AMD has shared updates on its progress toward achieving its 30×25 energy-efficiency goal for AI and HPC processors by 2025. The company reports significant advancements in chip architecture, such as 3.5D CoWoS packaging and high-bandwidth memory, bringing it close to reaching its target. The report emphasizes the critical role of software optimizations, particularly through AMD’s ROCm open software stack, in enhancing both performance and energy efficiency.

AMD has adopted a comprehensive energy-efficiency approach that optimizes both hardware and software to advance AI development. Key hardware innovations improve performance and facilitate the use of larger AI models. On the software side, the ROCm open software stack continually optimizes performance and energy efficiency by supporting lower-precision math formats, leading to substantial performance gains. This combined approach results in higher performance, greater accessibility to AI, more efficient training and inference, and a reduced environmental impact. AMD says it is confident that it will surpass its ambitious 30×25 energy efficiency goal—and that it is actively seeking additional improvements at the system level.

Huawei used the Ultra Broadband Forum held in Istanbul this fall to announce its autonomous mobile network platform. The embattled infrastructure provider is positioning it as a level-four offering, analogous to the stage of autonomous driving in which a vehicle can navigate without the intervention of a human driver. It is another example of the company making bold claims with little substantiation. To no one’s surprise, AI factors heavily into Huawei’s claims of a latency-aware topology, but removing human operators from the loop is not a realistic scenario for any mobile network operator deployment.

Last week was all about the Amazon Web Services re:Invent conference, for which 60,000 people descended on Las Vegas. It was quite an event, and AWS came out swinging with announcements up and down its stack. Here were the biggest news items on the developer side:

My personal favorite announcement of re:Invent was the new transformation capabilities in Q Developer. For context, Chapter 1 (out of 4) in my career was in systems integration and consulting; the big takeaway from that chapter was that migrations are hard—and maybe I should consider moving on to Chapter 2. One key lesson learned was that anything that can alleviate the pain of migrations and upgrades will make enterprises more secure and more efficient, and give them more time and money to innovate. AWS’s solid use case shows the enterprise potential of agentic apps and gen AI. Check out the piece that I published in Forbes about AWS agents.

For a Six Five on the Road videocast, I got to interview Sherry Marcus, Ph.D. about gen AI and AWS Bedrock. I have met Sherry before and was so happy that this time we got it on video. Bedrock has significantly expanded its capabilities in the newest release to support agentic applications. Bedrock is looking a lot like a great enterprise solution, but it is not alone, given the release of Microsoft’s AI Foundry at the Ignite conference a couple weeks ago. What we are starting to see is the formation of a new type of middleware category that I am calling agentic development frameworks. This is the type of technology that will get us past productivity agents operating within application platforms (which still do have their place) and get us into integrated high-scale agentic solutions. Want to know more? Check out the Forbes piece I wrote about these agentic frameworks.

While AWS generated a lot of focus, re:Invent is also something of an ecosystem show for the cloud. So, partners and partner announcements were everywhere as well. I got to speak to product leaders from IBM, which made multiple announcements of their own products running upon and in some cases integrating with AWS cloud. It’s an interesting combination, as IBM is a major champion for hybrid cloud, and its tools could help create a bridge for management, governance, and observability anywhere that applications are deployed.

There was also a lot of talk about AWS SageMaker’s new unified toolset. At first glance, it may seem like it’s simply a means to unify the data-scientist experience, but after doing some digging I was able to find out that what was announced this week was only step one. I think we will be seeing a great deal of work integrating SageMaker with Amazon Bedrock in 2025.

Last week was all about the Amazon Web Services re:Invent conference, for which 60,000 people descended on Las Vegas. It was quite an event, and AWS came out swinging with announcements up and down its stack. Here were the biggest news items on the developer side:

My personal favorite announcement of re:Invent was the new transformation capabilities in Q Developer. For context, Chapter 1 (out of 4) in my career was in systems integration and consulting; the big takeaway from that chapter was that migrations are hard—and maybe I should consider moving on to Chapter 2. One key lesson learned was that anything that can alleviate the pain of migrations and upgrades will make enterprises more secure and more efficient, and give them more time and money to innovate. AWS’s solid use case shows the enterprise potential of agentic apps and gen AI. Check out the piece that I published in Forbes about AWS agents.

For a Six Five on the Road videocast, I got to interview Sherry Marcus, Ph.D. about gen AI and AWS Bedrock. I have met Sherry before and was so happy that this time we got it on video. Bedrock has significantly expanded its capabilities in the newest release to support agentic applications. Bedrock is looking a lot like a great enterprise solution, but it is not alone, given the release of Microsoft’s AI Foundry at the Ignite conference a couple weeks ago. What we are starting to see is the formation of a new type of middleware category that I am calling agentic development frameworks. This is the type of technology that will get us past productivity agents operating within application platforms (which still do have their place) and get us into integrated high-scale agentic solutions. Want to know more? Check out the Forbes piece I wrote about these agentic frameworks.

While AWS generated a lot of focus, re:Invent is also something of an ecosystem show for the cloud. So, partners and partner announcements were everywhere as well. I got to speak to product leaders from IBM, which made multiple announcements of their own products running upon and in some cases integrating with AWS cloud. It’s an interesting combination, as IBM is a major champion for hybrid cloud, and its tools could help create a bridge for management, governance, and observability anywhere that applications are deployed.

There was also a lot of talk about AWS SageMaker’s new unified toolset. At first glance, it may seem like it’s simply a means to unify the data-scientist experience, but after doing some digging I was able to find out that what was announced this week was only step one. I think we will be seeing a great deal of work integrating SageMaker with Amazon Bedrock in 2025.

Amazon’s newly announced Amazon Nova is a line of three new foundation models: Nova Pro, Nova Lite, and Nova Micro. These models have frontier intelligence and can handle difficult language tasks, as proven by benchmarks like MMLU and VATEX. Their key advantages are speed, agentic workflows, and the ability to be customized. With these models, Amazon has focused on price-performance, which could disrupt the AI market with a new standard and the ability to democratize advanced AI capabilities for small models.

Similar to Dell’s AI Factory, Microsoft announced the Azure AI Foundry to facilitate AI implementation. The foundry includes an SDK that integrates Azure AI capabilities with GitHub and Visual Studio. Azure AI Foundry’s objective is to simplify the implementation of AI through a unified platform that streamlines AI development. Azure AI Foundry includes tools to help determine AI’s effectiveness and monitor ROI, with the goal of determining AI’s impact and ensuring it is focused on the proper business objectives.

Verint has introduced a new automated Scoring Bot tool to give organizations a more accurate and efficient method to assess the quality of their customer and employee experiences. This bot likely employs advanced analytics and AI technologies to analyze data from various interactions, such as customer feedback, service calls, and employee engagement metrics. By automating the scoring process, the bot can reduce manual effort and increase the speed at which insights are generated. This should enable businesses to make more informed decisions and swiftly implement improvements.

Integrating customer and employee experience scoring into a single tool reflects the growing recognition of the interdependence between the two. Engaged and satisfied employees are often more likely to deliver better customer service, enhancing overall customer satisfaction.

I want to talk about a key announcement out of re:Invent—Q Developer for modernization. This agent does exactly as one would think: modernize environments for IT. It focuses on three environments—.NET, mainframe, and VMware. While all are compelling, the VMware agent is incredibly disruptive, and maybe a little provocative.

Here’s the gist: through Q Developer, organizations can take their on-prem VMware environments and workloads and migrate them to cloud-native environments—on AWS, of course—all with a “few clicks.” This includes everything from re-mapping networks to workload migration.

Do I think this tool is going to be as simple as entering a few bits of data and clicking the mouse? No. But if this gets an organization, say, 75% of the way to successful migration, we are talking many months of saved time. And many, many dollars.

I’m curious to see how this will take off with customers; I can’t wait to see those first success stories and understand what was really involved in getting across the finish line.

One of the stars of AWS re:Invent was silicon—specifically Trainium2. This AI training silicon, boasting 20.8 petaflops of peak performance, is the company’s answer for meeting the needs of cost-effective, performant instances. In fact, the company claims a 30% to 40% price-performance advantage over its own GPU-based training instances running NVIDIA’s H200. The Trainium2, now in general availability under the trn2 instance, is also built into an AWS UltraServer (four chips) and UltraCluster (hundreds of thousands of chips).

So what is AWS doing? Is it taking on NVIDIA and other GPU players with Trainium (and Inferentia)? People at the company will tell you no. They will tell you that they are simply providing customers with choice, just as they have with Graviton. I understand this position, and the logic behind it. Further, I have no doubt the company is simply providing choice across the AI journey.

That said, I also believe that AWS is going to see success with Trainium like it has with Graviton. This means a first-generation part that played more as a proving ground than anything else. Then a second generation that delivered significant price-performance improvements, and a third (and fourth) generation that continued to build adoption. And one day we will all take note that about half of the workloads run on AWS silicon.

Trainium will be a little harder to grow as aggressively as Graviton, however, because migrating a workload to a new virtualized CPU is considerably easier. However, I believe that success will come. With that said, there is no loser—and NVIDIA’s astronomical growth in the training space will not be slowed.

Salesforce’s AgentForce platform has made a strong initial market entry, as shown by contracts with prominent companies such as FedEx and IBM within its first quarter since launch. CEO Marc Benioff projects significant growth, estimating that Salesforce customers will deploy one billion AI agents within the next year. This expected expansion in AI integration has led to increased revenue forecasts for Salesforce.

The platform’s success will likely depend on Salesforce’s ability to provide reliable model training and address potential issues such as hallucinations or memory inconsistencies, which it seems to be managing effectively. However, the widespread adoption of AgentForce involves more than just technological reliability. Organizations face the complex challenge of integrating a new digital workforce into their existing structures and workflows. Successfully implementing a digital workforce requires a considerable change-management effort. This entails a shift in mindset, organizational culture, and operational processes. Organizations must be ready to invest the necessary time, resources, and leadership commitment to navigate this transition and unlock the full potential of AI agents.

I look forward to seeing more progress from Salesforce and AgentForce during the AgentForce 2.0 event in mid-December.

Data has always been essential for businesses, but it’s not enough to just collect it—you need to act on it. At re:Invent, AWS CEO Matt Garman said, “The next big leap in value is not just about getting great data, but about taking actions and doing something with it.” That idea stuck with me because it gets to the heart of what businesses need today.

AWS just introduced the next generation of Amazon SageMaker, which brings data, analytics, and AI into one platform. It includes tools such as SageMaker Unified Studio for accessing and working with data, SageMaker Catalog for managing and finding data, and SageMaker Lakehouse for combining analytics and AI. The existing SageMaker service has been renamed SageMaker AI, focusing on building, training, and deploying AI and ML models. It can still be used on its own, but it’s also part of the larger integrated platform for those who need everything in one system.

These updates show that AWS is innovating to simplify things for businesses. By unifying these tools, AWS looks to guide enterprises in turning data into results.

As we all know, data is essential for innovation and business transformation. This fall, I attended events hosted by Teradata, AWS, Infor, and LogicMonitor, each offering different approaches to data management. Although all four had different methods, they shared a consistent perspective on the importance of data management—which is often overlooked in discussions dominated by AI. Read my latest Forbes article, where I highlight these companies’ views on how data management supports IT integration, analytics, real-time monitoring, and AI-based processes.

Broadcom’s acquisition of VMware and subsequent price increases have introduced significant challenges for enterprises reliant on VMware’s virtualization solutions. The financial and technical concerns associated with these changes dictate a reassessment of IT strategies, especially to avoid vendor lock-in. Deep data and network observability can serve a critical function in managing hybrid workloads, optimizing resources and utilization, and ensuring seamless operations across public and private clouds.

Over time, Broadcom’s pricing strategy for VMware may spur innovative approaches as its current customers adopt competitive offerings and diversify their virtualization solutions. As businesses adjust to a new normal, I believe this evolution will shine a light on the importance of network observability solutions within a migration journey for those customers that wish to explore alternatives. For more insights on this, take a look at my recent Forbes contribution on the role of deep observability in VMware migrations.

At AWS re:Invent 2024, SAP introduced GROW with SAP on AWS, designed to make it easier for enterprises to adopt SAP S/4HANA Cloud ERP. As you may know, ERP transformations can be challenging. The goal of GROW with SAP is to reduce the upfront costs and complexity of adopting cloud ERP, enabling businesses to complete deployments in months instead of years. This solution integrates SAP data with AWS’s generative AI tools—especially Amazon Bedrock—to improve operations. It also uses SAP’s Joule AI copilot to work across SAP applications to make processes more efficient.

I’m interested to see how GROW with SAP on AWS plays out when it is released soon. I understand the promise of easier access to resources, but knowing how challenging ERP transformations can be, especially in terms of both data management and change management, I’m cautious about how well it will deliver.

PayPal and Venmo are updating their platforms to remain competitive in the digital payment market. Paypal is introducing features such as money pooling to enhance user experience and attract a broader audience. This strategy also indirectly promotes the PayPal brand to younger Venmo users, potentially fostering future loyalty to the larger PayPal ecosystem.

At the same time, PayPal is facilitating online holiday shopping with its new “Fastlane by PayPal” feature. Launched in August 2024 and now available throughout the U.S., Fastlane aims to simplify guest checkout by using e-mail recognition and one-time passcodes to fill in shipping and payment information automatically. This reduction in manual entry could lead to faster checkouts and increased sales. Currently, Fastlane is available to U.S. merchants and shoppers using U.S. dollars and integrates with various e-commerce platforms, including Adobe Commerce, BigCommerce, and Salesforce Commerce Cloud. These developments indicate a two-pronged approach by PayPal, improving the user experience across different platforms and transaction types to boost engagement and expand market share.

SoFi Invest has expanded its offerings through a partnership with Templum, providing accredited investors with access to private-market investments. This move includes new funds such as those focused on SpaceX, Pomona Investment Fund, and StepStone Private Markets Fund. The partnership leverages Templum’s technology to facilitate these alternative investments, aligning with SoFi’s strategy to enhance its investment options and cater to the growing demand from retail investors interested in privately held companies.

There are rumors that Sony is working with Apple to bring games from PlayStation VR to the Vision Pro. This would be a huge win for both companies, since Sony ultimately doesn’t care about the hardware as much as it does selling games and Apple desperately needs more-immersive and better-quality games for its headset. A big part of this rumored partnership is that Apple would ensure that Sony’s PSVR Controllers would work with the Apple Vision Pro. This would also mean that developers could develop for the Vision Pro and PSVR using the same control scheme. I believe that this is partially an admission from Apple that shipping a headset without controllers and expecting people to game on it is a fool’s errand.

It appears that Valve is not only building a VR headset codenamed Deckard, but that it is also creating new VR controllers and a living room console to accompany the Steam Deck as part of its upcoming VR hardware offerings powered by SteamOS. I talked about Valve’s efforts in the XR space in my recent State of XR Part 2 report, but it seems that Valve’s hardware ambitions are both deeper and broader than originally anticipated. That said, “Valve time” is a very real thing, and these products could launch in a few months or a few years—it’s anyone’s guess.

AWS re:invent attendees had to look behind the session titles to find IoT news. That’s because CSPs (including AWS) and enterprise ERP providers are following the money—using AI to extract significant business value from the operations data collected by IoT systems. Here are two examples from re:invent: using AI to monitor and analyze IoT data and using Greengrass to deploy machine learning on edge devices.

AWS announced the general availability of IoT SiteWise Assistant in November and demonstrated its capabilities at re:invent. IoT SiteWise Assistant adds a layer of intelligence on top of the SiteWise Monitor Dashboard to simplify industrial data collection, organization, and monitoring. The new Assistant enables enterprises to gain actionable insights into complex operational situations involving disparate data sources and types. Operators can ask simple, natural-language questions to identify problems, troubleshoot root causes, and take corrective actions.

Digging deeper, here’s why this is a game-changer for IoT: Using AI to look for patterns across heterogeneous data lowers one of the most significant industrial IoT solution barriers—data ingestion and transformation. The trend is to use data pretty much as-is rather than remodeling it into a unified, centralized, managed data fabric. This approach fits with AWS’s “data as a product” strategy, combining the benefits of mesh and fabric data architectures.

The re:Invent workshop “Unleash edge computing with AWS IoT Greengrass on NVIDIA Jetson” demonstrated deploying ML models directly on edge devices, facilitating on-device intelligence with data paths connected to AWS services. The workshop used the NVIDIA Jetson Orin platform to accelerate complex AI workloads such as image recognition, Edge Impulse to streamline cloud-to-edge AI workflows, and Greengrass to connect the data and orchestrate software deployment on fleets of devices. I’m doubling down on my prediction that AI at the edge is a 2025 “megatrend.”

Apple’s 5G modem ambitions are no secret to anyone; the company wants to ramp its 5G modem capabilities with low-cost and low-volume products. This is meant to protect the company from any potential defects or delays that could arise. I also believe it gives Apple time to refine its modem design and add capabilities while shrinking die space and power consumption. We could even see a 5G RedCap modem from Apple for the Apple Watch or other wearables like an AR headset. I would expect a more powerful modem inside new MacBooks or iPads that’s more in-line with what Apple will use in the iPhone.

A comprehensive global study, the 2024 HP Work Relationship Index, reveals that only 28% of knowledge workers have a healthy relationship with their work. This represents a mere one-point increase from 2023. However, the study highlights two promising solutions to improve this dynamic: integrating artificial intelligence and offering personalized work experiences.

The study’s key findings include:

• AI usage among knowledge workers has surged to 66% in 2024, up from 38% last year, with AI users reporting 11 points higher satisfaction with their relationship to work.
• Approximately two-thirds of workers desire personalized work experiences, with 87% willing to forgo part of their salary to achieve this.
• AI is crucial for making jobs easier, improving work-life balance, and opening new opportunities for enjoyment and career advancement.

For the study, HP surveyed 15,600 respondents across 12 countries. The results underscore the evolving expectations of employers and employees and the potential of smart technology, particularly AI, to drive better work relationships and overall job satisfaction. Learn more about how these trends are shaping the future of work in my recent Forbes article.

Microsoft finally pushed its Recall feature to x86 Copilot+ PCs with chips from Intel and AMD that have capable enough NPUs. This update came through the Windows Insider Dev channel, so it’s not quite a broad release, but it is good to see Microsoft deliver on its promise of bringing Copilot+ to x86 PCs. I did think it was a bit odd that Recall didn’t come as part of the November update that brought Copilot+ to x86 PCs, but at least it wasn’t a long wait. I believe that we’ll continue to see a feature delta between Snapdragon-based systems and x86 systems, given Qualcomm’s six-month head start.

IonQ recently announced IonQ Quantum OS, a quantum operating system to increase the efficiency and scalability of quantum computing. It reduces classical and cloud overheads, provides improved qubit calibration, and increases security for enterprise-level applications. IonQ is currently using the OS in the IonQ Forte system and plans to use it in the IonQ Forte Enterprise in Switzerland.

IonQ also announced the IonQ Hybrid Services suite, which is designed to blend quantum and classical computing. It contains a new tool called the Workload Management & Solver Service, which makes cloud integration of quantum tasks easier. It also announced “Sessions” for optimized QPU time management.

Nile Secure’s latest Trust Service leverages its core architecture to simplify zero trust access and policy management with continuous updates as well as monitoring and enforcement capabilities. I also like its SSE integrations with Microsoft Security, Palo Alto Networks, and Zscaler for extended cloud workload protection. The company’s approach to delivering secure networking as a service with SLA guarantees is somewhat unconventional, and this latest announcement has the potential to provide additional value for its customers.

At this year’s AWS re:Invent, there was of course a lot of great technology to discuss. But one thing that stands out to me is how these tools and solutions aren’t just geared toward enterprises—they’re also making an impact in sports. I attended an AWS Sports session that highlighted work being done with the National Football League, National Hockey League, PGA Tour, and Deutsche Fußball Liga.

This session gave context to our Moor Insights & Strategy Game Time Tech podcast. Melody Brue and I had a chance to catch up with Julie Neenan Souza, head of global sports strategy at AWS, to discuss how AWS technologies are impacting organizations, players, and fans. Check out this great conversation.

Renewable energy company RWE has chosen Hewlett Packard Enterprise’s Private Cloud AI to enhance its weather modeling and energy resource management. This collaboration is part of HPE’s NVIDIA AI Computing portfolio and will enable RWE to leverage advanced AI to improve forecast accuracy and optimize its global renewable energy operations.

RWE’s AI Research Laboratory will use the AI-optimized private cloud—which HPE says can be deployed in just a few clicks—to evaluate, fine-tune, and deploy weather models. The solution’s ability to handle large datasets and automate processes should streamline this work. It will integrate with the HPE GreenLake cloud, which should allow RWE’s researchers to concentrate on model development and accelerate their time-to-market.

This initiative aligns with RWE’s growth strategy, “Growing Green,” which aims to expand its renewable energy portfolio and achieve net zero emissions by 2040. The advanced AI capabilities should also give RWE a competitive advantage in the renewable energy market.

I had the opportunity to attend AT&T’s Analyst & Investor Day in Dallas. The company’s continued significant fiber investment serves as the bedrock for it to provide converged network services that include highly performant broadband and mobility at scale. In the process, it is bridging the digital divide and providing digital literacy through its network of connected learning centers. It’s a model to follow for other companies that have the capital and operational resources to marshal. One statistic that was shared at the event stood out for me: Converged services are lifting the operator’s lifetime subscriber value by 15%. That flies in the face of convention that subscribers bundle services purely to get discounts. This positions AT&T to offer additional adjacent solutions that have great potential for lifting ARPU over time.

This week Microsoft hosted its Ignite event in Chicago and, as expected, it was very heavy on AI agents in terms of announcements. This makes a lot of sense, and I intend to do a deeper research piece on all of the announcements very soon. What stands out so far is the breadth of the announcements. Microsoft is a massive technology company providing value to many different stakeholders. So, it was not a surprise to see agentic aspects throughout its developer tools such as Copilot Studio, as well as embedded into standard products such as Office 365. What I wonder is how quickly we will see the agent category start to fracture into sub-categories. This will likely be a healthy thing, since my conversations with sales and marketing leaders tells me that education around agents and AI in general is seriously lacking. One click down into how this stuff works—and the implied business value of different types of agents—might do wonders for everyone.

This week I got the chance to meet the founding team at Zavvis Technologies to discuss their approach to agentic applications. The discussion provided a cool perspective on startups in the age of AI. The access to LLMs via hyperscalers and horizontal tooling for data science and application development enables new approaches to innovating. What Zavvis is doing is digging into how agents can improve the CFO function in a company. To be specific, it’s not really about automating financial operations and processes, but more a set of agents to help the CFO understand options and see opportunities in a mass of data. To me, it’s a very interesting approach for a couple of reasons. First is simply the fact that a company can now use technology to take AI deep into a very specific functional area. Second, the agents should help blend together structured and unstructured data, which may in fact enhance the CFO’s role. That said, this approach is a bit tricky, given that the CFO role in particular is known for risk aversion—which suggests a different sort of go-to-market strategy for Zavvis. I am excited to hear what the company learns as it gets moving.

In October, IBM announced the availability of its Granite 3.0 models; since then it has been engaging many different ecosystems talking about the open source value proposition for LLMs. While IBM is not the only player here, it does have an evolved take on how transparent vendors can be about LLMs. But one item that got lost in the shuffle was IBM introducing one Granite variant specifically to implement guardrails on speech and bias. IBM has a notion of this LLM sitting in front of other LLMs. For an analogy, think of when someone in a TV studio “bleeps” out a word they are not allowed to broadcast. I was talking to another client about this today and I started to think about the utility of purpose-built LLMs being used as a front-end. It’s an interesting notion, and whether it’s going to win out against other rules-based guardrails and security measures is unknown. But it also suggests that we are about to enter into some really serious architectural discussions for AI in 2025.

This week Microsoft hosted its Ignite event in Chicago and, as expected, it was very heavy on AI agents in terms of announcements. This makes a lot of sense, and I intend to do a deeper research piece on all of the announcements very soon. What stands out so far is the breadth of the announcements. Microsoft is a massive technology company providing value to many different stakeholders. So, it was not a surprise to see agentic aspects throughout its developer tools such as Copilot Studio, as well as embedded into standard products such as Office 365. What I wonder is how quickly we will see the agent category start to fracture into sub-categories. This will likely be a healthy thing, since my conversations with sales and marketing leaders tells me that education around agents and AI in general is seriously lacking. One click down into how this stuff works—and the implied business value of different types of agents—might do wonders for everyone.

This week I got the chance to meet the founding team at Zavvis Technologies to discuss their approach to agentic applications. The discussion provided a cool perspective on startups in the age of AI. The access to LLMs via hyperscalers and horizontal tooling for data science and application development enables new approaches to innovating. What Zavvis is doing is digging into how agents can improve the CFO function in a company. To be specific, it’s not really about automating financial operations and processes, but more a set of agents to help the CFO understand options and see opportunities in a mass of data. To me, it’s a very interesting approach for a couple of reasons. First is simply the fact that a company can now use technology to take AI deep into a very specific functional area. Second, the agents should help blend together structured and unstructured data, which may in fact enhance the CFO’s role. That said, this approach is a bit tricky, given that the CFO role in particular is known for risk aversion—which suggests a different sort of go-to-market strategy for Zavvis. I am excited to hear what the company learns as it gets moving.

In October, IBM announced the availability of its Granite 3.0 models; since then it has been engaging many different ecosystems talking about the open source value proposition for LLMs. While IBM is not the only player here, it does have an evolved take on how transparent vendors can be about LLMs. But one item that got lost in the shuffle was IBM introducing one Granite variant specifically to implement guardrails on speech and bias. IBM has a notion of this LLM sitting in front of other LLMs. For an analogy, think of when someone in a TV studio “bleeps” out a word they are not allowed to broadcast. I was talking to another client about this today and I started to think about the utility of purpose-built LLMs being used as a front-end. It’s an interesting notion, and whether it’s going to win out against other rules-based guardrails and security measures is unknown. But it also suggests that we are about to enter into some really serious architectural discussions for AI in 2025.

Microsoft just released Magentic-One, a multi-agent AI system that can handle open-ended tasks common to daily life. The system’s multiple agents each have specialized functions. These agents are controlled by an orchestrator agent that acts as a monitor and supervisor. The open source system is new from the standpoint that it is active instead of passive and can provide recommendations and execute tasks. According to Microsoft, Magentic-One excels at software development, data analysis, and navigating the internet.

My biggest takeaway from the Microsoft Ignite conference is how much the company has invested in its infrastructure. It has added an HSM for crypto management and a DPU for networking and storage acceleration to complement its Cobalt CPU, Maia AI accelerator, and existing security platform. Effectively, Microsoft has joined AWS and Google Cloud in developing custom silicon to deliver a full compute experience.

In addition to this, the company worked with AMD to develop a custom chip—the EPYC 9V64H—to support virtualized HPC workloads. This chip will be outfitted with HBM3 memory and double the infinity fabric. While this is an incredibly powerful compute platform, what is perhaps more interesting is to see the dominant position AMD has taken in the cloud space. Custom chip work for the CSPs was once the domain of Intel and Intel only.

Finally, Microsoft has expanded its partnership with Oracle by activating an additional 12 regions for Oracle Database@Azure and making the environment managed and governed through Azure Resource Manager and Purview, respectively. Effectively, Oracle Database@Azure is now fully integrated as a native service.

Part 1: SC24 came and went, and boy was it a ride! There are so many storylines to trace from the big supercomputing conference, so let me just share a few.

My biggest takeaway is a little bit esoteric. When I attended SC15 in Austin in 2015, it felt like I was walking through a science fiction magazine because the technology was so disconnected from what was happening in the enterprise datacenter at that moment. In particular, it was very focused on the big national and academic labs. At that event, I saw and talked about topics that would eventually become common in the enterprise—but not for years.

By contrast, walking through the show floor this week in Atlanta, I could immediately tie all of the innovations I saw to uses by the average enterprise trying to operationalize AI. Put more succinctly, over the past decade the pace of innovation has drastically accelerated from the innovators to the deployers.

Here’s another big takeaway: holy liquid cooling, Batman! I have been watching the liquid cooling market for some time. Some of the earlier players such as Vertiv, LiquidStack, CoolIT, GRC, and Motivair are now part of a much larger market peppered with logos—both the familiar and the new. I saw a total of 50 companies listed on the exhibitor list, including 22 for liquid cooling. And this doesn’t count the likes of Delta, Schneider Electric, and some of the power and infrastructure companies that have either already joined the market or are looking to enter it. The (smart) acquisition of JetCool by Flex is a good example of this.

Overall, I’m quite impressed with the amount of liquid cooling I’ve seen from cooling vendors and OEMs alike. Lenovo has been pushing Neptune for a long time, and we saw HPE start aggressively telling its liquid cooling story this summer at its Discover conference. Now Dell is really starting to jump in the game (for instance, the XE9712 racks they are shipping to CoreWeave are liquid-cooled).

With this said, I think we are still very early in this cooling game, and what we are seeing in today’s market is kind of like the days of discovering fire and inventing the wheel. As warm-water cooling is starting to find a place in the market, look for two-phase direct-to-chip (D2C) cooling to play a bigger role, as it is so much more able to address the heat density that we see on chips. Longer term, I think immersive technologies will be niche in application and will eventually bridge to cooling technologies we aren’t even covering today.

The last big takeaway is about the silicon innovation going into this market across the entirety of the data journey. I have been in the tech industry for over 30 years and I’ve never seen so much innovation in the silicon space. Most of us see NVIDIA’s biggest threat coming from AMD, Intel, and Arm—or maybe even a Qualcomm or Marvell. However, don’t overlook the many, many innovation engines in the chip industry like NeuReality, Tenstorrent, Cerebras, Untether, or others.

Part 2: The line from supercomputing to enterprise computing has become short and straight.

We’ve seen HPC-like requirements creeping into the enterprise for some time. First it was the larger enterprise organizations with workloads like crash simulation and high frequency trading. Big data, EDA, and data analytics really pushed this requirement for accelerated compute and more bespoke storage and networking to populate the enterprise datacenter. But AI has totally disrupted the game and, yes, it has brought supercomputing into the enterprise in a major way. And to the edge—and wherever else there’s data. This is why we see such big market sizing and CAGRs associated with AI. It’s not just about the chips, servers, storage, and networking; it’s about the cost of deploying, tuning, and managing these environments. And because of its nascency, there is so little knowledge to share—certainly no institutional knowledge or “muscle memory.” Because of this, I see the consulting companies playing a big role in the AI journey.

Like I said when I was talking about the SC15 conference earlier, the line from supercomputing to mainstream enterprise used to be long and crooked. That line is now very short and very straight. No longer is technology being developed and then kind of iterated on for eventual broader consumption. The ability for technology to be broadly adopted (and used) in a commercial way is now a primary concern for any startup playing in this space.

Canva recently appointed Kelly Steckelberg, former Zoom CFO, to the same position within its organization. Steckelberg brings a wealth of experience, having successfully steered Zoom through its IPO and a period of rapid growth. Canva is currently valued at approximately $32 billion, with more than $2 billion in annual recurring revenue. It has seen significant success in expanding into the enterprise market, with 95% of Fortune 500 companies as users. Although Canva states there are no immediate plans for an IPO, Steckelberg’s appointment and the company’s strong financial performance suggest a public offering could be on the horizon. Steckelberg says she sees tremendous opportunity at Canva, and I believe the company is very fortunate to have her. At a recent Zoom event, she expressed confidence in her successor, Michelle Chang. Chang joins Zoom from Microsoft, where she served as corporate vice president and CFO of the Commercial Sales and Partner Organization. Chang will be front and center next week as Zoom presents its earnings. Analysts project Zoom to deliver year-over-year earnings growth driven by higher revenues when it releases its Q3 2025 financial results for the quarter ending October 2024. Chang will be a critical part of Zoom’s next growth phase as the company moves from a video conferencing company to an AI-driven collaboration and productivity platform.

Microsoft Fabric announced significant updates to its unified data platform at Ignite 2024. Fabric Databases, initially including SQL Database, now include transactional capabilities with built-in security, vector search, RAG support, and Azure AI integration, enabling the development of AI-optimized applications. OneLake, the platform’s multi-cloud data lake, now has enhanced multi-cloud and on-premises data integration with Azure SQL DB Mirroring. Several workload-specific updates were also announced, including sustainability data solutions, AI functions for text analysis in notebooks, and a GraphQL API for simplified data access. AI capabilities expanded with conversational AI tools, Azure Event Hubs KQL database support, and integration with Azure AI Foundry.

These updates strengthen Microsoft’s position against competitors, enhancing Fabric’s appeal as a unified platform for data management and AI development. By addressing enterprise requirements, Fabric reinforces Microsoft’s ability to compete with other major players in data management and AI such as Google Cloud and AWS.

IBM has modernized Db2 with a new AI-powered database assistant. As data demands grow, database systems must evolve to keep pace, and last week IBM released Db2 12.1, incorporating a slew of AI features. The new release addresses key challenges faced by database administrators and introduces the Database Assistant, developed using IBM watsonx. This assistant delivers instant answers, real-time monitoring, and intelligent troubleshooting. Explore these innovations further in my latest Forbes article, which features Miran Badzak, IBM’s program director for databases.

LogicMonitor is advancing in the hybrid observability industry with an $800 million investment to integrate AI into datacenter operations. Led by CEO Christina Kosmowski, the company focuses on helping businesses reduce costs and scale AI while improving efficiency and meeting sustainability goals. This funding looks to strengthen LogicMonitor’s role in supporting modern datacenter management and AI-driven operations.

Cloudera announces the acquisition of Octapai’s data lineage and catalog platform, expanding its data catalog and metadata management capabilities. With this move, Cloudera will be able to provide customers with visibility across data solutions, allowing them to use trusted data for AI, predictive analytics, and decision-making tools. Key benefits include improved data discoverability, quality, governance, and migration support.

Change is in the air as we approach the new year: Amazon Web Services has brought in Julia White as CMO. White was recently the Chief Marketing and Solutions Officer at SAP. Before that, she was at Microsoft for two decades, including as corporate vice president for Azure product marketing. Her expertise spans cloud services, AI, and product messaging, making her well-suited to AWS’s strategic needs. With this move, AWS looks to strengthen its position in the competitive cloud market against Microsoft Azure and Google Cloud. With AWS quarterly profits surpassing $10 billion for the first time, White’s leadership is expected to enhance AWS’s focus on cloud computing and AI innovation. This leadership change follows recent executive departures at AWS, including former CEO Adam Selipsky and VP of AI Matt Wood. Wood has since joined PwC as the firm’s first Commercial Technology and Innovation Officer.

Microsoft launched Flight Simulator 2024 with significantly enhanced features and new flying jobs that enable you to pilot virtually any kind of flying craft, from hot air balloons to helicopters and dirigibles. Unfortunately, Microsoft didn’t adequately anticipate the demand for the game, and servers crashed; the company apologized to gamers for not being prepared for the launch to be such a hit. I believe part of the success came from the game being included with Game Pass. Flight Simulator is the second big title this month to launch on Game Pass after Call of Duty launched earlier in November.

Qualcomm Investor Day — Nakul Duggal (Qualcomm’s group GM for automotive, industrial, and cloud) presented Qualcomm’s IIoT deployment model for AI at the edge. The company foresees a $50 billion market opportunity for edge intelligence by 2029 and has defined a path to achieve that goal. The path includes new edge computing chips (the Qualcomm IQ series) designed to support a comprehensive edge deployment architecture. The architecture aligns with industry-wide trends that make IIoT much more scalable.

Here’s how it works. Qualcomm customers develop AI-powered applications in the cloud for deployment on both cloud and local platforms. AI-accelerated on-premises “AI edge boxes” run cloud-native computing software environments on appropriately scaled compute platforms. The development model is “build in the cloud, deploy on the edge” using the same software infrastructure. However, IT-managed, cloud-native platforms do not extend all the way down to the chaotic world of OT (operational technology) devices. These small embedded platforms are often highly customized and optimized for specific tasks. OT devices require unique software stacks, device management services, and connectivity schemes. The result is a line of demarcation that separates small OT platforms from large IT systems running distributed cloud environments. Mr. Duggal explained the Qualcomm IQ Series processors and subsystems in terms of this model, enabling a new generation of on-premises compute platforms with the power and scale to address a wide range of vertical industries.

MY TAKE: AI at the edge is the new IIoT north star. I’ve advocated this three-tier architecture (cloud, distributed cloud, device) for years, and it’s great to see Qualcomm and other big suppliers follow the same pattern.

Microsoft Ignite — Not surprisingly, Ignite focused on AI in the cloud and at the edge. From an IIoT standpoint, Azure’s Adaptive Cloud approach was the star of the show, enabling AI to work across sites, clouds, distributed computing, and devices. The Azure IIoT model is consistent with my “fractal” view of IIoT intelligence, with cloud-native environments scaling from global clouds to local on-premises servers. Microsoft’s AI enablement products, data fabric, event grid, event hubs, storage, Power BI interfaces, and other services run on the whole range of platforms, while Azure IoT Operations (enabled by Azure Arc) implements device data interfaces and manages the OT data at the edge. The IIoT devices are, essentially, peripherals communicating via standards-based protocols.

MY TAKE: Azure IoT Operations is emerging as the interface between the chaotic world of OT devices and the structured world of Microsoft’s AI-enhanced IT systems. Other hyperscalers and platform suppliers are moving in this same direction, allowing enterprise applications (i.e., ERP) to immediately scale up OT-enabled AI-powered solutions with minimal dependencies on IIoT device systems. Even though Ignite didn’t have many IoT-specific sessions, the industrywide trend to separate chaotic device development from high-growth, AI-driven business transformation came across loud and clear.

Shure, a company known for high-quality audio equipment, has partnered with Microsoft by integrating its products with the Microsoft Device Ecosystem Platform (MDEP). This collaboration allows Shure to develop new audio solutions for Android devices for Microsoft Teams Rooms. What this means for Shure is enhanced security measures that meet Microsoft’s high standards, improved compatibility with Microsoft Teams Rooms, and the opportunity to tap into new markets. For example, government agencies that rely on Microsoft Teams for secure communication could now outfit their conference rooms with Shure microphones and audio processors that integrate with their existing systems. This partnership paves the way for Shure to deliver enhanced audio experiences to a broader range of users who depend on Microsoft Teams for collaboration.

Box held its annual BoxWorks event in person for the first time in several years and announced a suite of new AI-powered tools focused on helping businesses unlock the value of their content. These include AI Studio for building custom AI agents, Box Apps for automating workflows, and enhanced security features. Box also supports nonprofits using AI for social good through its Impact Fund. I provide more detail about BoxWorks and the company’s strategy for addressing organizations’ growing need to manage and extract value from increasing content volumes in this Forbes contribution.

Amazon announced the new Echo Show 21, its biggest and most capable smart home display with a built-in smart home hub. This new smart home hub includes support for both Matter and Thread wireless standards, making it an anchor for your smart home and putting Amazon at the center of that experience. While I’m not sure I have the need or space for this, there is also a 15-inch version which may be easier to fit into smaller kitchens. Amazon has designed the Echo Show 21 as a wall-mounted display; I may have to test it myself to understand the utility of having such a large display in the kitchen.

At Microsoft Ignite, the company announced a few updates to Copilot and Window 365, including a thin client called the Link. The Link appears to be Microsoft’s way of satisfying the market need for low-cost products while driving Windows 365 virtual PC usage. While many of Microsoft’s partners like HP, Dell, and Lenovo already offer thin clients, this appears to be Microsoft’s own approach focused around Windows 365 only. Microsoft also announced the new Copilot Actions, which gives prompt templates to help automate repetitive tasks. While this isn’t quite a way to enable scripting, I do think this should help to improve Copilot usage.

Microsoft and Atom Computing successfully entangled 24 logical qubits using neutral atoms under control of Atom Computing lasers. This is the record for the highest number of entangled logical qubits. Logical qubits are constructed from multiple physical qubits, and they allow complex quantum algorithms to be run. The system demonstrated high gate fidelities: 99.963% for single-qubit gates and 99.56% for two-qubit gates, making this the highest neutral-atom two-qubit gate fidelity in a commercial system.

A characteristic of neutral atom quantum computers is the tendency of atoms to disappear during operations. The team developed a method to detect and replace lost atoms without disrupting computations. As a benchmark, the researchers ran the Bernstein-Vazirani algorithm, which identifies a hidden binary string. The 20 logical qubits (created from 80 physical qubits) found the secret code in a single attempt, outperforming classical counterparts that must run the search many times to find all the bits.

This is good news for quantum computing. Great progress is being made with logical qubits, in fact doubling the number that the Microsoft-Atom Computing team has accomplished in just a few short weeks.

Qualcomm held its investor day—the first in three years—and updated analysts and investors on the progress it has made over the last three years in its product diversity strategy. The company also stated that it expects its smartphone business to be just 50% of revenue by 2030. This is a significant goal because the company currently generates 75% of its revenue from its handset business. In addition to that lofty goal, the company also revealed that it is planning an even more affordable PC processor soon, which will enable $600 CoPilot+ PCs. Additionally, it said that the next generation of its X Elite processors will be powered by the third generation of its Oryon CPU cores, which have done exceptionally well in benchmarks against Apple.

The whole industry eagerly awaited NVIDIA’s earnings, and the company beat on revenue and profit and guided slightly above expectations . . . yet still got punished in after-hours trading. The reality is that people’s expectations of NVIDIA are simply unrealistic and heavily tainted by retail hype, even though the company is now generating $35 billion in revenue per quarter and almost $20 billion in profit per quarter. NVIDIA is basically printing money compared to almost everyone else in the industry and dwarfs many of its nearest competitors. I believe that we are still very much in the early phases of AI, and while some of the AI model builders may be hitting a bit of a wall with training, NVIDIA says that demand for training chips remains high — and it is convinced that it is also well-positioned with chips for inference.

IBM has partnered with the Ultimate Fighting Championship to become UFC’s Official Global AI Partner. The UFC Insights Engine, built with IBM watsonx, utilizes data and AI to analyze live bouts, fighter tendencies, projected match outcomes, and methods of victory. This provides fans with detailed information to deepen their interest in the sport. It is another great example of sports technology enriching a sport and driving fan engagement.

Solidigm has launched a new 122TB SSD designed to reduce energy consumption in data centers, which are facing increasing demand and costs due to the rise of AI. This new drive offers significantly higher storage density and efficiency, which should lead to lower energy bills and a smaller physical footprint. This is crucial for sustainability and allows companies to invest more in AI development. Read more about Solidigm’s 122TB drive in my recent Forbes article.

Last week I attended the annual TBM Council Conference in San Diego. TBM stands for Technology Business Management, which is a movement to demystify and optimize IT spending—leading to improved business results. While that may sound like it’s strictly FinOps, in fact it goes well beyond FinOps and covers the business and resource metrics to facilitate a transparent and predictable digital supply chain. What makes it so great is that TBMC is a user conference that keeps the focus on the present state and the solution space—it is not a product and technology extravaganza. What stood out was the common origin story of how companies adopted TBM solutions. It seems to commonly start with a surprise budget-busting bill that nobody can quite account for. However, once the right level of discovery happens, not only are financial surprises minimized, but other areas also emerge such as cross-project resource conflicts or smarter product architecture. The takeaway was twofold: For users, TBM as a framework is worth checking out. For vendors like Apptio, by establishing a very clear view and driving engagement on a solution space, they are able to more effectively position their solutions in the TBM space.

Anthropic is having its moment in the sun with developers. Last week I mentioned that both GitHub and AWS made announcements around their respective AI-powered IDEs. But within those announcements, it was also revealed that Anthropic’s Claude 3.5 Sonnet was being used as either the LLM or a major new LLM option. Sonnet is a deeper-thinking model without a degradation in performance from the previous generation. It’s another example of LLMs being packaged to meet specific situations; for developers, it should be a welcome offer since devs are prioritizing accuracy over speed. But for those who need speed, Antropic happens to have a different model available.

JFrog has been a key partner of GitHub for a while. The combination of curating source code and binaries tees up what is a very complementary partnership. But at GitHub Universe last week, JFrog and GitHub announced they are working together on a more security-driven GitHub Copilot IDE. JFrog has launched integration with GitHub Autofix so devs can scan their code pulls as part of the development workflow using JFrogs own SAST security capabilities. This is a great way to enable devs to intercept problems even earlier in the process, reducing the cost and complexity of future remediation. For more on this, you can read JFrog’s blog post about the integration here.

Last week I attended the annual TBM Council Conference in San Diego. TBM stands for Technology Business Management, which is a movement to demystify and optimize IT spending—leading to improved business results. While that may sound like it’s strictly FinOps, in fact it goes well beyond FinOps and covers the business and resource metrics to facilitate a transparent and predictable digital supply chain. What makes it so great is that TBMC is a user conference that keeps the focus on the present state and the solution space—it is not a product and technology extravaganza. What stood out was the common origin story of how companies adopted TBM solutions. It seems to commonly start with a surprise budget-busting bill that nobody can quite account for. However, once the right level of discovery happens, not only are financial surprises minimized, but other areas also emerge such as cross-project resource conflicts or smarter product architecture. The takeaway was twofold: For users, TBM as a framework is worth checking out. For vendors like Apptio, by establishing a very clear view and driving engagement on a solution space, they are able to more effectively position their solutions in the TBM space.

Anthropic is having its moment in the sun with developers. Last week I mentioned that both GitHub and AWS made announcements around their respective AI-powered IDEs. But within those announcements, it was also revealed that Anthropic’s Claude 3.5 Sonnet was being used as either the LLM or a major new LLM option. Sonnet is a deeper-thinking model without a degradation in performance from the previous generation. It’s another example of LLMs being packaged to meet specific situations; for developers, it should be a welcome offer since devs are prioritizing accuracy over speed. But for those who need speed, Antropic happens to have a different model available.

JFrog has been a key partner of GitHub for a while. The combination of curating source code and binaries tees up what is a very complementary partnership. But at GitHub Universe last week, JFrog and GitHub announced they are working together on a more security-driven GitHub Copilot IDE. JFrog has launched integration with GitHub Autofix so devs can scan their code pulls as part of the development workflow using JFrogs own SAST security capabilities. This is a great way to enable devs to intercept problems even earlier in the process, reducing the cost and complexity of future remediation. For more on this, you can read JFrog’s blog post about the integration here.

Konecta and Google Cloud have established a three-year strategic partnership to integrate Google Cloud’s AI and cloud technologies into Konecta’s customer experience solutions. This collaboration seeks to enhance Konecta’s Digital Unit with AI-powered tools and services, including implementing Google Cloud’s Customer Engagement Suite and generative AI solutions. As part of the partnership, Konecta will transition its workforce to Google Workspace and certify up to 500 engineers in Google Cloud technologies. These initiatives are designed to improve operational efficiency, enable the development of advanced CX offerings for clients, and strengthen Konecta’s position as a provider of AI-driven customer service solutions. Konecta and Google Cloud expect this alliance to facilitate more personalized and efficient customer interactions for businesses and contribute to their digital transformation objectives.

Twilio has partnered with Google Cloud to integrate generative AI into its customer engagement platform. This enables Twilio users to deploy AI-powered solutions such as virtual agents and interactive chatbots to enhance their customer service capabilities. By leveraging Google Cloud’s AI tools, including Dialogflow, businesses can automate responses to common inquiries, provide 24/7 support, and efficiently escalate complex issues to human agents. Early results from this collaboration are promising. Google says that a major automotive manufacturer using its “Destination Assist” feature has reported an 18% to 20% reduction in agent call times. While Google did not include resolution rates for this particular use case, this type of reduction in call times and always-on support will likely lead to higher resolution and happier customers.

Freshworks has announced a significant restructuring that will result in layoffs affecting approximately 13% of its global workforce, equating to about 660 employees. This move surprised many, particularly since the company recently reported strong fourth-quarter financials for 2024, demonstrating a 22% year-on-year increase in revenue. Most of the positions affected by these layoffs are based in India and the United States. CEO Dennis Woodside described this decision as a difficult but necessary step to streamline operations and focus on the company’s key strategic areas: employee experience, artificial intelligence, and customer experience. Following the announcement, the company’s share price surged by 15%, and Freshworks unveiled a substantial $400 million stock buyback program, indicating a strong financial position despite the layoffs.

While Freshworks is framing this as a strategic realignment, the increasing role of AI could be a contributing factor to these layoffs. Overall, this decision reflects a broader trend in the tech industry, where companies are optimizing their workforces in response to changing business objectives and the rise of AI. It will be interesting to observe how this restructuring impacts Freshworks’ growth trajectory in its key strategic areas moving forward.

I write this as I am finishing up the final day of Dell’s analyst event—Dell Tech Summit. While this was an NDA event, so the details are secret, there are a few general takeaways I want to share:

1. If you thought AI hype had peaked—think again. However, unlike some other hype cycles, AI is driving a lot of revenue and completely disrupting enterprise IT organizations.
2. Dell’s AI journey is an amazing story. I’m convinced this experience puts the company in a stronger position for helping its customers evolve.
3. Partners are critical to successfully delivering AI transformation projects to the market
4. We are still very early in the AI game. Very, very early.
5. There is opportunity for every company in the AI supply chain. Dell partners. Dell suppliers. Frankly, Dell competitors.

2025 is going to be a fun year in tech and AI. If you are an IT solutions vendor: better understand where you fit in the enterprise AI journey and start aligning portfolios and GTM efforts. If you are an enterprise organization: don’t start this AI journey on your own. Learn from those that have gone through this before.

Most of us in tech are familiar with software-defined storage, software-defined networking—even software-defined infrastructure. How about software-defined silicon? NextSilicon, a semiconductor company based in Tel Aviv and Minnesota, has launched its Maverick-2 intelligent compute accelerator (ICA). This silicon, which in earlier versions has been powering HPC clusters for a number of years, has been tuned to also support AI and vector database operations, delivering a performance-per-watt advantage of 4x over GPUs while cutting operational costs in half.

Isn’t this just a fancy name for an FPGA, you say? Not really. With Maverick-2, real-time application telemetry feeds the silicon, tuning its characteristics for optimal performance. If you are familiar with Kalman filtering for voltage/current regulation, a very crude comparison can be drawn.

My thoughts: There is a really interesting play here with Maverick-2. As datacenters struggle with power and space constraints, such a piece of silicon enables a bit of platform flexibility that can optimize itself in real time for a variety of workloads. NextSilicon is not going to use Maverick-2 (or -3, -4, etc.) to displace the likes of NVIDIA or AMD for AI training. Nor is it going to replace those companies or Qualcomm, Untether, or others for AI inference. However, NextSilicon has found a niche for itself. Further, the way the company is approaching what I would call autonomous programmability will have a longer-term impact on the silicon market in general.

The Ultra Accelerator Link (UALink) Consortium was officially incorporated last week. This group, which includes some of the biggest tech companies in the world, is focused on establishing standards and specifications for GPU-to-GPU connectivity. What problem is this solving? In larger AI and HPC environments where GPUs are used to perform complex computations, the design of legacy infrastructure can force GPUs to rely on CPUs that can become bottlenecks. As you can imagine, this introduces a lot of latency into the equation.

To solve this, NVIDIA developed a fabric called NVLink to enable its GPUs to bypass the CPU as a controller or head node. This allows NVIDIA-based environments to be more performant. However, NVLink was developed by NVIDIA for NVIDIA. UALink aims to take this concept and make it open for all: a universal set of standards and specifications that enable accelerator-to-accelerator connectivity at scale.

My thoughts: The broad support for UALink (from AMD, Intel, Meta, HPE, Astera Labs, AWS, Microsoft, and others) tells me that this consortium has legs and will find adoption among the customers that have immediate needs. I expect to see other big players such as Dell and Broadcom to join in as contributors at some point. See my full analysis on Forbes.

Should enterprises be concerned about cyberattacks from AI agents? AI’s advanced capabilities can enhance the sophistication and scale of malicious attacks, making them more difficult to detect and defend against. This is especially concerning given AI’s ability to automate and personalize attacks on a large scale. Potential threats include AI-powered phishing, deepfakes, and malware that can bypass traditional security measures.

Here are some key reasons why AI poses a cyberthreat to enterprises:

1. Advanced Attack Vectors — AI can analyze large datasets to exploit vulnerabilities and deliver highly targeted attacks.
2. Automated Attacks — AI enables rapid, large-scale attacks that are difficult to contain due to automation.
3. Evasion of Detection Systems — AI can create and modify malware to bypass traditional security measures, complicating detection.
4. Data Manipulation and Poisoning — Attackers can use AI to corrupt training data, impacting security models and resulting in missed threats or false positives.
5. Accessibility to Malicious Actors — The availability of AI tools allows even attackers who are less skilled to deploy AI-powered attacks.

In response, here are some mitigation strategies for enterprises:

1. Develop AI-powered defenses to detect and respond to sophisticated threats in real time.
2. Monitor AI systems closely, particularly the behavior of AI agents, to detect potential malicious activity.
3. Implement employee awareness training to help staff recognize and report AI-driven threats.
4. Strengthen data protection and privacy measures to safeguard sensitive information.

It’s important to stay updated on this topic, as the integration of AI agents into various applications will continue to expand, bringing with it new challenges.

By all measures, Juniper Networks is executing well financially, beating expectations for both top-line revenue and profitability with its recent 3Q 2024 earnings announcement. Chief executive Rami Rahim credits the company’s support of front-end and back-end AI networking for its recent success. Juniper’s reinvigorated focus on enterprise networking and its depth in AI help explain why HPE is acquiring the company, with an expected close of the deal anticipated soon. The combination of both companies’ engineering resources and complementary portfolio coverage of campus, branch, and data center is powerful, which should allow the combined entity to compete more effectively with Cisco and others.

NetSuite has examined emerging trends and made some predictions about ERP as we head into the new year. Enterprises are increasingly prioritizing ERP modernization, with the cloud ERP market projected to expand from $72.2 billion in 2023 to $130.5 billion by 2028. Because ERP systems are so essential for so many enterprise processes, modernizing them can reshape how enterprises operate.

Many enterprises face the challenge of modernizing their operations by transitioning to cloud ERP solutions. Software provider IFS saw strong performance in Q3 2024, with a 30% increase in ARR, driven by a 71% rise in IFS Cloud usage and a 46% growth in cloud revenue. IFS added 90 new clients, introduced AI features in IFS Cloud 24R2, and launched a new module for sustainability management.

Matter, the smart home automation standard from the Connectivity Standards Alliance, just released version 1.4. The new version has some much-anticipated enhancements, including support for many new device types. When the CSA released Matter 1.0 in October 2022, the organization’s leadership promised a regular six-month release cadence, and that’s exactly what they have done. The team has shipped four releases in two years, each with significant improvements in device coverage, ease of use, and functionality. The cumulative expansion of device types is impressive, with version 1.4 adding support for energy management products including solar power, energy storage, heat pumps, water heaters, electric car charging, and time-based orchestration. The CSA was smart to focus device development on a single high level use case—energy—rather than adding a hodgepodge of random devices.

But the most significant version 1.4 enhancement is automating “multi-admin” configurations. Here’s why. Matter’s value proposition rests upon interoperability, and that means enabling devices from any manufacturer to work with any combination of home automation ecosystems—i.e., HomeKit, Google Home, Alexa, or SmartThings. Although previous Matter versions support this feature, the configuration process has been complicated and often confusing. Version 1.4 automates multi-admin configuration by allowing the user to grant permission just once, then Matter automatically adds a new ecosystem. Thus, “Alexa” and “Hey Google” can both turn down your thermostat. This fulfills a fundamental brand promise for Matter. Between now and CES, I’ll write more about Matter’s progress. I’m also doubling down on my prediction that Matter will hit its tipping point in early 2026, becoming the leading smart home connectivity standard for new designs.

A new security feature in iOS 18.1 has made it more difficult for police and other agencies to snoop inside iPhones they’ve confiscated for investigations. This feature is an inactivity timer that reboots iPhones into a more secure state when they haven’t been unlocked for a while. This is a welcome feature for many privacy and security advocates because so many warrantless searches have been conducted on people’s phones. And Apple isn’t the first to implement this, as GrapheneOS for Google Pixel phones already offers this capability.

Workvivo, an employee experience platform acquired by Zoom in 2023, has launched a new suite of tools called “Employee Insights.” This suite is designed to measure and improve employee engagement. Integrated directly into the Workvivo platform, Employee Insights enables organizations to deploy pulse surveys, monitor engagement across 12 key drivers, and analyze results using real-time dashboards.

This solution provides a centralized way to gather employee feedback, particularly from frontline workers who may have limited access to e-mail. Workvivo emphasizes that the platform facilitates actionable insights based on the “listening” data collected, aiming to foster a cycle of continuous improvement. Future updates are expected to include integration with Zoom AI Companion for enhanced industry data analysis and benchmarking capabilities. This launch comes at a significant time for Workvivo, as it was recently named Meta’s preferred migration partner for the discontinued Workplace platform.

I served as a judge for UC Today’s inaugural UC Leaders Awards 2024, which aims to highlight the top professionals in the unified communications (UC) and collaboration space. The judging process was enjoyable, and I learned a great deal about the individual and team contributions to the companies and industries I cover. There were many outstanding applicants this year.

UC Today has announced the UC Leaders finalists, including Craig Walker from Dialpad and Eric Yuan from Zoom for the Innovator of the Year award. Eric Yuan is also nominated alongside Vlad Shmunis from RingCentral for the UC Leader of the Year award. The finalists for the Women in UC Leadership Award include Smita Hashim, chief product officer at Zoom; Christina Hyde, VP of revenue at SkySwitch; Kira Makagon, chief innovation officer at RingCentral; and Aruna Ravichandran, SVP and chief marketing and customer officer at Cisco Webex—quite a powerhouse lineup!

Additional award categories include Industry Influencer of the Year, Rising Star of the Year, The Editor’s Choice Award, and the UC Team of the Year. The award ceremony will be held on November 21, 2024, and will be streamed live on the UC Today website, LinkedIn, and X at 4:00 p.m. GMT / 11:00 a.m. EST. I will be presenting the awards in an Oscars-style format, and I hope you will join us!

People are really loving Apple’s new M4 Mac Mini thanks to its extremely compact size and reasonable price. With an M4 chip, it may be one of the most inexpensive and powerful computers on the market. One of its biggest downfalls, however, is that memory upgrades are extremely expensive, so much so that in some cases it’s cheaper to buy a second Mac Mini than it is to upgrade the RAM. The one upside of this new design is that there is user-upgradable memory; however, there are no known third-party memory products for the device yet, although I expect OWC will offer one fairly soon.

North Korean threat actors have successfully used a combination of phishing emails and social engineering schemes to target cryptocurrency-related business. Cybersecurity solution provider SentinelOne has named the campaign “Hidden Risk”; this complex attack also employs a seemingly benign PDF with fake cryptocurrency news headlines and offers of employment to infect Apple macOS users with a malicious payload. It is a highly sophisticated campaign, one that is difficult to defend against, especially given the extensive grooming of targets over social media. The silver lining in this latest cyberattack is that security operators have access to an incredible amount of real-time threat intelligence shared by Infoblox, Microsoft, Palo Alto Networks, SentinelOne, Zscaler, and others to enable stronger security postures.

AMD’s new 9800X3D CPU is officially on the market and has already sold out. This new CPU is the first to use AMD’s second-generation 3-D V-Cache, which has faster CPU clock speeds, better memory bandwidth, and significantly improved thermals because now the CPU cores sit atop the memory instead of the other way around. While I haven’t had a chance to test this chip fully, the consensus is that its gaming performance is industry-leading, and its handling of multitasking and productivity workloads isn’t far behind—which wasn’t necessarily the case with past processors in this family.

Globant has opened a new office within Intuit Dome, home of the Los Angeles Clippers. This move is part of the two organizations’ ongoing partnership to enhance the fan experience through digital transformation initiatives. The new office includes a “Digital Playground” technology showcase, which the company says it hopes will foster creativity and attract talent while strengthening Globant’s presence in Southern California. This strategy aligns with a growing trend of businesses creating experience centers to demonstrate the potential of their products and services in an engaging environment. These centers provide tangible, interactive experiences beyond traditional marketing, allowing consumers to interact directly with technology and see various real-life use cases.

IBM is partnering with Ferrari’s racing team starting in 2025. This partnership will leverage IBM’s advanced data analytics to improve engagement for fans of Scuderia Ferrari through personalized content and insights that “bring racing enthusiasts closer than ever to the racing team.” IBM will also support Ferrari by using cutting-edge data analysis to enhance performance on and off the track. This newly announced partnership shares several similarities with IBM’s long-standing collaborations with the US Open, Wimbledon, and The Masters. All of these partnerships focus on enhancing fan engagement with premium sporting events—now including Formula 1—through digital platforms, leveraging advanced AI and data analytics to process vast amounts of data for real-time insights. These partnerships also serve as high-profile demonstrations of IBM’s capabilities on a global stage.

The National Football League’s data-driven approach to reducing injuries is showing progress. By analyzing player data with Amazon Web Services, the NFL provides insights to help teams improve training and safety. The league and its teams have also used computer vision and sensors to track head impacts, helping coaches implement injury-prevention strategies.

“When you can integrate and aggregate data across all 32 [teams] for all 53 [players], you have more power in the data that you are generating to model,” said Jennifer Langton, NFL Player Health & Safety Innovation Advisor. With AWS, the NFL can track injuries in real time, automatically linking incidents to specific plays. The league is also working on new tech for full-body tracking to better prevent injuries in the future.

As one example, the redesigned kickoff rule, created in collaboration with AWS, has reduced injury risk. The rule change has led to 32% fewer injuries overall on kickoffs, including no ACL or MCL tears, as player speeds have decreased.

US Cellular informed the FCC earlier this fall that T-Mobile’s acquisition of its wireless operations was essential in ensuring uninterrupted service for its dwindling subscribership. A sticking point is regulatory approval of the transfer of 30% of US Cellular’s spectrum assets to T-Mobile. However, that may be less of an issue now, given the breaking news that AT&T has agreed to purchase over $1 billion worth of US Cellular’s spectrum licenses.

It is an unfortunate situation for US Cellular, signaling the company’s inability to keep pace with necessary infrastructure deployments to support its primarily rural customer base. It is extremely challenging to provide mobility services in remote areas, given lower population densities and longer timelines to recoup investment in radio access network infrastructure. T-Mobile’s acquisition makes sense in many ways, especially given its success in connecting rural America with 5G access that leverages its lower-band spectrum assets.

Last week AWS announced inline editing for Amazon Q Developer, among a series of other new capabilities. Inline editing is very much in line (ha!) with AWS’s commitment to an efficient pro developer experience. Instead of the AI working in a window next to the coding workspace, you can now inject AI recommendations right into your work. What’s nice is that it needs to be invoked via a command key instead of just looming there all the time. Initial findings suggest that developers like using inline editing when they are truly stuck on something where they may lack familiarity. Think of something like a whole routine versus just something at the line level. The intended benefit is that devs will get richer and more verbose assistance, leading to even greater productivity. It will be very interesting to see how this approach moves the needle in metrics such as accepted changes, which is something that AWS is tracking and publishing regularly.

GitHub hosted its Universe event last week and made two major announcements. The first is that GitHub Copilot will now support the use of multiple LLMs. While this is not a new concept, given that many other developer tools do this, it’s interesting since Gitub has been all-in on using parent company Microsoft’s Copilot LLM until now. Additionally, GitHub announced a new no-code tooling and runtime offering called Spark, which is geared towards citizen and business developers. My initial thought is that this tool is geared towards business users who want to build simple forms-based applications, as opposed to power users who want to incorporate workflows and processes. Again, this is a capability that many other companies already have. And to be honest, it’s interesting to see GitHub branching out to attract new users. Especially since a shared set of repositories and services could be of value to enterprises. What will be more interesting is to see how this introduction shapes Microsoft’s low-code and no-code strategy, which currently spans multiple projects.

Red Hat hosted an analyst event in which its leadership walked through its AI strategy and other product updates. However, just like at IBM’s Analyst Summit a couple weeks ago, Red Hat could not resist talking about its major push to move customers from VMware to its OpenShift Virtualization offering. To be candid, it’s not cool, sexy, or even new since Red Hat has been in the virtualization game for decades now. But, frankly, it’s a sneaky-brilliant monetization play. Red Hat has a very strong history in executing pricing disruptions in commoditized and semi-commoditized markets (Unix to RHEL, BEA to JBoss). And Red Hat typically needs a lot of time to spin up in new markets (like AI). This is very typical of low-cost players in any marketplace. So while Red Hat talks the AI talk, my sense is that continued financial results will come from ripping-and-replacing hypervisors for a while. Red Hat will then attempt to parlay that customer savings into consideration for more modern solutions From Red Hat or parent IBM down the road.

Last week AWS announced inline editing for Amazon Q Developer, among a series of other new capabilities. Inline editing is very much in line (ha!) with AWS’s commitment to an efficient pro developer experience. Instead of the AI working in a window next to the coding workspace, you can now inject AI recommendations right into your work. What’s nice is that it needs to be invoked via a command key instead of just looming there all the time. Initial findings suggest that developers like using inline editing when they are truly stuck on something where they may lack familiarity. Think of something like a whole routine versus just something at the line level. The intended benefit is that devs will get richer and more verbose assistance, leading to even greater productivity. It will be very interesting to see how this approach moves the needle in metrics such as accepted changes, which is something that AWS is tracking and publishing regularly.

GitHub hosted its Universe event last week and made two major announcements. The first is that GitHub Copilot will now support the use of multiple LLMs. While this is not a new concept, given that many other developer tools do this, it’s interesting since Gitub has been all-in on using parent company Microsoft’s Copilot LLM until now. Additionally, GitHub announced a new no-code tooling and runtime offering called Spark, which is geared towards citizen and business developers. My initial thought is that this tool is geared towards business users who want to build simple forms-based applications, as opposed to power users who want to incorporate workflows and processes. Again, this is a capability that many other companies already have. And to be honest, it’s interesting to see GitHub branching out to attract new users. Especially since a shared set of repositories and services could be of value to enterprises. What will be more interesting is to see how this introduction shapes Microsoft’s low-code and no-code strategy, which currently spans multiple projects.

Red Hat hosted an analyst event in which its leadership walked through its AI strategy and other product updates. However, just like at IBM’s Analyst Summit a couple weeks ago, Red Hat could not resist talking about its major push to move customers from VMware to its OpenShift Virtualization offering. To be candid, it’s not cool, sexy, or even new since Red Hat has been in the virtualization game for decades now. But, frankly, it’s a sneaky-brilliant monetization play. Red Hat has a very strong history in executing pricing disruptions in commoditized and semi-commoditized markets (Unix to RHEL, BEA to JBoss). And Red Hat typically needs a lot of time to spin up in new markets (like AI). This is very typical of low-cost players in any marketplace. So while Red Hat talks the AI talk, my sense is that continued financial results will come from ripping-and-replacing hypervisors for a while. Red Hat will then attempt to parlay that customer savings into consideration for more modern solutions From Red Hat or parent IBM down the road.

Osmo.ai is able to capture and digitally recreate a scent. That means a scent can be teleported from one location to another. The company uses gas chromatography-mass spectrometry (GCMS) to analyze a given scent so it can be uploaded to Osmo’s cloud-based Primary Odor Map, which uses AI to predict the molecular composition. Next, a formulation robot creates the scent by mixing different molecules. Osmo produces high-fidelity scent replication, and it can capture even the subtlest nuances of a scent. The system is almost fully automated, and human assistance is needed only at the input and output stages. As a result of its experimentation, Osmo has accumulated the largest AI-compatible scent databank, which is now used for training the AI.

Osmo has the opportunity to create a number of new use cases. Perfume retailers could digitally transmit a perfume’s scent to potential customers. Because scent has an important role in memory and emotion, Osmo believes the technology could be used for PTSD or dementia therapy by recreating comforting or significant scents. All in all, the technology could blend the physical and digital worlds and offer a new way to experience and share sensory information.

I’ve been thinking through the recent announcement regarding the partnership between NTT Data and Oracle Cloud Infrastructure. In essence, NTT will be using (or OEM-ing) the Oracle Alloy cloud infrastructure platform to deliver sovereign cloud services to its customers—initially in Japan, with plans to expand over time.

Can we stop and take a moment to look at the novel ways Oracle is building OCI’s relevance? Through Alloy, Database@[CSP], HeatWave, Cloud@[customer], and a number of other avenues, OCI is becoming an indispensable part of the cloud marketplace. Rather than solely focusing on delivering better services at a lower price point than the competition (which it also does), Oracle is looking at embracing traditional competitors to better meet the needs of enterprise customers.

What a unique and smart approach.

I attended Red Hat’s analyst summit in Boston last week, and there were two themes front and center: AI and VMware. While focusing on these may sound somewhat boring to analysts, this is a smart move as these are the top two infrastructure focus areas for any IT organization of size across the industry.

On the VMware front, Red Hat has a solid competitive story with OpenShift. In fact, right now the post-VMware reality for many IT organizations comes down to a two-horse race—Nutanix or Red Hat. I think Red Hat’s depth in cloud-native/containerization combined with its virtualization makes for a compelling story. Further, its long reach into the enterprise datacenter perhaps gives it a little bit of an advantage in pursuing the larger opportunities. With that said, I don’t believe that Red Hat has been aggressive enough in establishing and amplifying its dominant position. My expectation is that we will see the volume turned up in 2025.

Looking at AI, Red Hat seems to be in a little bit of a muddled place in the market as it tries to focus on RHEL AI and OpenShift as foundational elements of the enterprise AI equation. While I can see the story after spending a day with the company, I believe this is another marketing opportunity for Red Hat to educate and engage with enterprise IT. It is critical to be a part of the early AI discussion and project planning if Red Hat hopes to be successful.

It was earnings week for AMD and Intel. Both companies demonstrated growth and progress. However, the markets certainly treated each of them differently.

In the case of AMD, the company showed strong datacenter performance for its fiscal third quarter with revenue of $3.5 billion, representing a 122% year-over-year increase, and operating income of $1.04 billion, a whopping 240% YoY increase. Overall, AMD beat expectations for revenue and met them for EPS. Interestingly, the market was down on AMD (about 15% since earnings) due to the company’s conservative guidance and its weakness in other contributions to revenue (e.g., gaming). Realistically, I don’t believe that investors are viewing AMD negatively; rather, they are normalizing after a hype period that followed the company’s very aggressive take on the AI market.

As an ex-AMD employee, I don’t think there is a single person in Austin or Santa Clara (or Markham) who is disappointed with a $141 share price—save Dr. Lisa Su and the CFO. However, I do believe we will continue to see a little less stability in the stock price as the AI chip market continues to evolve, even though I believe that AMD is making all the right long-term bets for the AI future.

Intel also showed growth in its datacenter segment on revenues of $3.3 billion, representing YoY and sequential growth. Its $300 million in operating income represents margins of 10.4%—also up sequentially. Following the company’s earnings, the market showed favorable coverage as the stock moved up about 2%. Further, a number of institutional investors have raised their target buy price for Intel’s stock.

Intel’s financials were a lot more nuanced than what we saw from AMD. The company is going through a major restructuring, with many of the restructuring costs impacting bottom-line numbers. It seems investors are accounting for this and showing confidence in the work that CEO Pat Gelsinger and team are doing to right the ship.

Stock prices are about expectations: meeting, beating, or failing to meet. Intel has done a good job of managing the investor community as it continues its multi-year turnaround.

Cisco recently held its partner summit, using it to announce a revamped compensation and training initiative. What I really like about the Cisco 360 Partner Program is the long-lead-time deployment, which should ensure a smooth introduction in 2026. The company is also making a substantial $80 million investment in training to facilitate meaningful opportunities for the company’s channel sellers to expand their capabilities for providing expertise across AI, networking, security, observability, and more.

As regulatory demands for environmental accountability grow, enterprises need to track and manage carbon footprints across supply chains. SAP’s new Sustainability Data Exchange (SDX) offers a platform for standardized data sharing and improved emissions accounting. Ahead of the COP29 meeting in November 2024, SDX provides enterprises with tools to support international climate goals, particularly in sharing Scope 3 emissions data across the value chain. Integrated with SAP S/4HANA Cloud ERP, SDX enables precise data exchange of carbon emissions among businesses and their suppliers and customers. It helps address the issues created by outdated tools, data inaccuracies, and inconsistent calculations, helping enterprises move from estimations to actual emissions data provided by suppliers. Read more in my latest Forbes article on SAP’s Sustainability Data Exchange.

AI agents are becoming integral to ERP systems, creating a balance between automation and human oversight. Leading ERP providers are embedding these agents as a core component of their recent innovations. AI agents offer real benefits by supporting task management and improving operational efficiency in areas such as forecasting, customer satisfaction in sales and marketing, and collaboration across the supply chain. By managing routine tasks, these agents simplify ERP interactions, making the systems easier for users to navigate and apply across various business functions. As I’ve suggested in the past, strong data management strategies are necessary for these benefits to be attainable—AI agents rely on accurate, up-to-date data to be effective.

Blue Yonder’s Q3 2024 results show that the company added 31 new customers, including BJ’s Wholesale Club, PepsiCo’s Latin American unit, and Sainsbury’s. Its recent acquisition of One Network Enterprises expands Blue Yonder’s capabilities with real-time collaboration and data sharing across supply chains. Blue Yonder’s latest features include the Intelligent Rebalancer for real-time order adjustments, the Fulfillment Sourcing Simulator for optimizing fulfillment, and tools for automating warehouse and yard tasks. Updates to Cognitive Demand Planning improve forecasting and planning, providing clients with more accurate, flexible supply chain management solutions.

Blue Yonder has identified some key SCM industry trends this quarter, including rising food costs pushing grocery retailers toward value-focused inventory strategies, increasing demand for traceability due to U.S. and EU regulations, and labor and logistics challenges leading companies to cross-train staff and relocate distribution centers closer to U.S. markets. Additionally, manufacturers are adopting generative AI tools to enhance supply chain efficiency, customer service, and cost management. I see these trends as critical real-world challenges that Blue Yonder addresses to support its clients’ complexities and to align with today’s operational and regulatory demands.

As announced last week at the Money 20/20 conference, NVIDIA has introduced an AI-powered workflow for fraud detection running on AWS. With financial losses from credit card fraud projected to reach $43 billion by 2026, this solution is critical. The workflow leverages advanced algorithms and accelerated data processing to identify fraudulent transactions more accurately than traditional methods, potentially improving detection by up to 40%. It takes advantage of NVIDIA’s AI Enterprise software platform, GPUs, and tools including RAPIDS AI libraries and the Morpheus application framework to enhance fraud detection models and streamline their deployment.

For context, financial institutions are increasingly adopting AI and accelerated computing to combat fraud. This new workflow aims to provide a comprehensive solution for fraud use cases beyond credit card transactions, including new account fraud, account takeover, and money laundering. While fraudsters can also exploit AI to develop new and sophisticated schemes, AI equips the good guys with powerful tools to analyze vast amounts of data, detect subtle patterns, and adapt to evolving threats to fight back.

Across three days last week I attended multiple SAP SuccessConnect virtual sessions, during which several key trends in human capital management surfaced. I noted a strong emphasis on strategic workforce planning, particularly the need for proactive approaches to address skills gaps, enhance employee competencies, and support career transitions. Speakers frequently highlighted the importance of integrating HR and financial data to drive growth. Many sessions showcased and acknowledged the potential of AI in HCM, but also emphasized the importance of responsible AI development and deployment to prevent bias. I observed a recurring theme around the critical role of data analytics in understanding workforce trends and informing effective HCM strategies. SAP’s acquisition of WalkMe appears to generate positive results within SuccessFactors, with reports of improved user experience, increased task completion rates, and higher overall satisfaction.

Amal Clooney’s keynote address was a highlight of the event for me. She offered a compelling look at real-world use cases of AI in human rights and the workforce, providing valuable insights into the broader societal impact of these technologies.

I often write about the increasing practicality of using AI and machine learning techniques in small, low-power edge devices. NXP’s new AI-enhanced chips and high-productivity edge AI software development tools align with this trend.

NXP i.MX RT700 — NXP is doubling down on intelligence for small devices with AI-enhanced SoCs, new AI development tools, and eIQ Neutron, an internally developed family of NPUs (neural processors). The company recently introduced the i.MX RT700 “crossover” SoC, designed for ultra-low-power smart devices. (In NXP parlance, crossover MCUs are simple, low-power processors with MPU-like performance.) I don’t usually dive into the technical details of SoC designs in these weekly summaries, but this chip is noteworthy because of its heterogeneous design. There are two Cortex-M33 compute subsystems, each with a DSP (Tensilica HiFi). One M33 is the main processor for the chip, and the other is a low-power subsystem for always-on applications such as keyword recognition. The chip also has a modest graphics subsystem, a dedicated I/O processor (RISC-V), and an advanced memory architecture optimized for multiprocessor partitioning. The chip’s most disruptive compute subsystem is its eIQ Neutron N3-64 NPU. NXP claims the NPU provides a 172x performance boost and 119x per-inference power decrease compared with “general purpose processors.”

NXP eIQ tools — This week, NXP announced two new software enablement tools for the eIQ NPU family: eIQ Time Series Studio (TSS) and eIQ GenAI Flow. TSS automates machine learning workflows, streamlining time-series-based machine learning model development and deployment across MCU-class devices such as the i.MX RT700. Applications include anomaly detection, classification, and regression for many types of sensor data. The development model is BYOD (bring your own data), and the TSS development flow simplifies model tuning, optimization, and deployment. GenAI Flow provides building blocks for large and small language models (LLMs, SLMs) that power applications for generation and reasoning. These large models run on NXP’s i.MX MPU processor families. GenAI Flow makes generative AI applications accessible on these devices and supports retrieval-ugmented generation (RAG), a technique for securely fine-tuning models on domain-specific knowledge and private data.