Welcome to this edition of our Weekly Analyst Insights roundup, which features key insights that our analysts have developed based on the past week’s events.
One of the highlights of my year is our annual Six Five Summit — and I’m thrilled to say that we have confirmed Salesforce CEO Marc Benioff to open The Six Five Summit: AI Unleashed 2026. He’ll be kicking off three days of executive conversations focused on the operational realities of AI, setting the tone with his take on where enterprise AI is heading next and the realities of deploying AI within large organizations.
Salesforce CEO Marc Benioff is confirmed to open the 2026 Six Five Summit in August.
As the AI conversation increasingly shifts from pilots to production, AI Unleashed 2026 will explore what it actually takes to operationalize AI across infrastructure, cloud, edge, data, and enterprise workflows at scale. Because the Six Five Summit is virtual, there’s no travel and no noise for you to deal with — just the inside edge on what’s really driving the evolution of enterprise AI from some of the best in the business.
We hope you’ll plan to join us August 25–27. Registration is free — sign up today!
This week, Matt, Mike, and I are at Dell Technologies World in Las Vegas. Mel is attending Zendesk Relate in Denver, and Anshel will be at Google I/O in Mountain View, California. The spring season of client and vendor events is in full swing, and we’re on the road a lot over the coming months, including at Computex, Cisco Live!, Snowflake Summit, Microsoft Build, and the NetApp Analyst Summit — all by the first week in June! If you’ll be attending any of the same events or see that we’ll be in your city, please reach out.
Last week, Moor Insights & Strategy analysts’ perspectives appeared across multiple business and technology outlets, including Data Center Knowledge, The New Stack, StockTwits, and Investing.com. Media coverage focused on AWS expanding Graviton into its Redshift analytics stack and deploying 50-year-old logic engines for requirements analysis instead of AI; Cisco’s stock surge driven by strong earnings and AI networking momentum; the AI server market’s evolution from silicon to services; and Kopin’s Q1 2026 earnings results, which missed revenue forecasts yet still drove stock gains.
I also joined CNBC’s Money Movers to discuss President Trump’s trip to China, Jensen Huang’s involvement, and what it could all mean for NVIDIA and other AI chip companies.
Our MI&S team also published 8 deliverables — 5 Research Notes, 1 Analyst Insights, and 2 Podcasts.
Check out this week’s Analyst Insights roundup for more from the MI&S team, including what’s top of mind for each analyst, our thoughts on vendor announcements, press quotes, and more.
Have a great week!
Patrick Moorhead
SAP Sapphire 2026
Last week I was excited to attend my first SAP Sapphire event. Once again, I walked away impressed with yet another SaaS player that is fully leaning into its customer value proposition as Salesforce is doing. What set SAP apart was its focus on helping move the customer base forward. It’s a lesson that has been burned into SAP since the days of massive upgrade and migration efforts from past versions of SAP software. And while some of these upgrades remain substantial, the RISE with SAP methodology and “clean core” principles are now augmented with migration assistants and agents. There is now also more incentive to move forward with SAP’s new AI-based platforms and products. While being touted as a “completely new SAP platform,” I’d argue that it’s less a new architecture and more of a radical repackaging and rebranding of SAP’s existing tooling with sensible updates and better integrations with recent acquisitions. Here are some takeaways.
CEO Christian Klein’s argument: Most enterprises are still thinking about AI wrong, and SAP is positioning itself as the partner to help them course-correct. The case he made: What makes AI work in the enterprise is grounding it in your business processes, your governance rules, and your company’s accumulated operational knowledge. That’s the job of the new SAP Business AI Platform — and it reframes the platform itself, not the application layer, as the place where the durable value lives.
Here are two things (and there are more) that stood out as proof points of that philosophy:
SAP is smart to affirm that its platform is a strategic moat much like its applications and services ecosystem. The open question: Can SAP help customers adopt this without recreating the customization debt they’ve spent years unwinding?
Salesforce’s MuleSoft announced a new product called Omni Gateway. And while MuleSoft has had agentic governance capabilities for some time now, Omni Gateway is a means to federate governance capabilities across multiple platforms (i.e., agents can live outside the Salesforce platforms). This enables a more consistent means to enact and manage agents and the policies to keep them secure and effective. I like this approach since agents are springing up in just about every platform and ecosystem, and it will likely be impossible for IT to be successful in managing across a wide range of tools, all with different capabilities. I also like its ability to automatically convert APIs into MCP servers through the gateway, which should help better support agent builders throughout the enterprise. If you are starting to feel the pains of agents everywhere, this is worth a look.
AWS introduced a new method to review coding requirements in the Kiro IDE, and this is important in a couple of ways. Now that we are seeing tooling get smarter to support planning application development projects with planning modes or spec-based development, we need to also ensure that those plans make sense. For example, two product owners may issue conflicting requirements that could confuse the agents writing and testing the code. So, it’s nice to see quality assurance methods moving out of the coding phases. But specific to Kiro, there was a desire to do more than having an evaluation LLM looking at LLM-generated content. AWS has been making strides with new types of models to prove the correctness of LLM outputs. In this case, a neurosemantic model is employed. It’s an interesting concept, and I’m interested in hearing more about it next month at the AWS Summit in NYC. If you’d like to learn more you can check out this article from Darryl Taft at The New Stack where he and I spoke about it.
SAP Sapphire 2026
Last week I was excited to attend my first SAP Sapphire event. Once again, I walked away impressed with yet another SaaS player that is fully leaning into its customer value proposition as Salesforce is doing. What set SAP apart was its focus on helping move the customer base forward. It’s a lesson that has been burned into SAP since the days of massive upgrade and migration efforts from past versions of SAP software. And while some of these upgrades remain substantial, the RISE with SAP methodology and “clean core” principles are now augmented with migration assistants and agents. There is now also more incentive to move forward with SAP’s new AI-based platforms and products. While being touted as a “completely new SAP platform,” I’d argue that it’s less a new architecture and more of a radical repackaging and rebranding of SAP’s existing tooling with sensible updates and better integrations with recent acquisitions. Here are some takeaways.
CEO Christian Klein’s argument: Most enterprises are still thinking about AI wrong, and SAP is positioning itself as the partner to help them course-correct. The case he made: What makes AI work in the enterprise is grounding it in your business processes, your governance rules, and your company’s accumulated operational knowledge. That’s the job of the new SAP Business AI Platform — and it reframes the platform itself, not the application layer, as the place where the durable value lives.
Here are two things (and there are more) that stood out as proof points of that philosophy:
SAP is smart to affirm that its platform is a strategic moat much like its applications and services ecosystem. The open question: Can SAP help customers adopt this without recreating the customization debt they’ve spent years unwinding?
Salesforce’s MuleSoft announced a new product called Omni Gateway. And while MuleSoft has had agentic governance capabilities for some time now, Omni Gateway is a means to federate governance capabilities across multiple platforms (i.e., agents can live outside the Salesforce platforms). This enables a more consistent means to enact and manage agents and the policies to keep them secure and effective. I like this approach since agents are springing up in just about every platform and ecosystem, and it will likely be impossible for IT to be successful in managing across a wide range of tools, all with different capabilities. I also like its ability to automatically convert APIs into MCP servers through the gateway, which should help better support agent builders throughout the enterprise. If you are starting to feel the pains of agents everywhere, this is worth a look.
AWS introduced a new method to review coding requirements in the Kiro IDE, and this is important in a couple of ways. Now that we are seeing tooling get smarter to support planning application development projects with planning modes or spec-based development, we need to also ensure that those plans make sense. For example, two product owners may issue conflicting requirements that could confuse the agents writing and testing the code. So, it’s nice to see quality assurance methods moving out of the coding phases. But specific to Kiro, there was a desire to do more than having an evaluation LLM looking at LLM-generated content. AWS has been making strides with new types of models to prove the correctness of LLM outputs. In this case, a neurosemantic model is employed. It’s an interesting concept, and I’m interested in hearing more about it next month at the AWS Summit in NYC. If you’d like to learn more you can check out this article from Darryl Taft at The New Stack where he and I spoke about it.
Three governance storylines stood out for me last week. One focused on where AI assets live, one on how agents are supposed to run safely, and one on where sovereignty enforcement sits. The vendors differ, the markets differ, and each is solving a different piece of the governance problem.
First up, Alation launched its AI Governance offering. Enterprises standing up agents right now are all hitting the same wall, and they can’t answer where a given model’s training data came from, who approved its deployment, or which regulation it’s supposed to satisfy. That’s a sourcing problem before it’s a policy problem, and the catalog vendors are the only ones with the underlying plumbing to actually answer it. Alation’s pitch is that the system of record for AI assets should sit on top of the system of record for data, because lineage from an agent back to the source column is where the answer lives. Policy-first competitors can claim governance, but they can’t draw that line the same way. The open question is whether buyers see AI asset governance as a new buy or a feature their existing catalog should just have.
SAP and NVIDIA used SAP’s Sapphire conference to co-define secure agent execution for the enterprise. Agents are running against enterprise data at machine speed, but the guardrails most enterprises have today were designed for humans clicking buttons. The math doesn’t work. Something has to govern what an agent is allowed to see, do, and write back, and it has to govern at agent speed, not human speed. SAP and NVIDIA are putting a reference architecture on the table for what that layer should look like, with NVIDIA’s hardware-level trust capabilities underneath it. For data leaders standing up agent programs against SAP-resident data this year, this is the bar you measure your own setup against.
Equinix Fabric Geo Zones also GA’d last week. Sovereignty has lived in contract clauses and PowerPoint diagrams for two years, but Fabric Geo Zones turns it into a network-layer product, with policy enforcement for where data is allowed to traverse and reside across 77 metros. This launch moves Equinix from being the colocation underneath someone else’s sovereign story to being the layer that actually enforces it. For customers, this stands to fill in the missing piece in every sovereign architecture deck; residency claims have to hold at the network, because compute and storage commitments don’t mean much if traffic crosses a jurisdiction it shouldn’t.
AMD Enterprise AI Focus Comes into View with the Instinct 350P
AMD launched a more enterprise-friendly version of its MI350 GPU last week. The MI350P is a PCIe-compatible GPU that enables an enterprise IT organization to deploy without requiring more specialized “AI infrastructure” everywhere in the datacenter. These customers want accelerators that can fit into the servers they’ve been racking and deploying for years. Existing servers. Existing operating models. Existing power envelopes. And existing procurement motions.
I am not going to focus on the specs for the MI350P, because they detract from the bigger story. That story is simple: While hyperscaler adoption of AI captures headlines and supply today, the enterprise wave is coming. And those enterprises are going to still have a large on-prem presence for AI, from smaller, specialized models to inference at scale. However, this term “at scale” means something different for down-segment organizations. And AMD is lining up its portfolio to support these organizations that will be tuning, customizing, and orchestrating smaller specialized models tied directly to business workflows and operational data.
Here’s where things get interesting. AMD is aligned for this phase of enterprise AI unlike any of its competitors. Over the past several years, AMD has established meaningful trust with enterprise IT organizations through deployments of its EPYC processors across virtualization, databases, analytics, and cloud infrastructure. That matters a lot. Why? Because AI infrastructure adoption in enterprises won’t be influenced by benchmarks that most folks can’t translate into real-world benefit. Adoption will be influenced by operational familiarity and ecosystem maturity. And in the enterprise, AMD is a well-known commodity.
Maybe put more plainly, the winner in enterprise AI is going to be the company that can best enable practical, cost-effective inference and model deployment at enterprise scale. And for this space, AMD is in the hunt.
Cerebras’s Crazy IPO Signals That AI Hype Is Peaking — Now What?
Cerebras had a near-record-breaking IPO last week, achieving the largest U.S. semiconductor IPO ever. The company went out at a $5.5 billion valuation, quickly skyrocketing above $50 billion. All of this on $550 million in reported revenue.
I think these crazy numbers are a real indicator that the market is increasingly convinced that there is both room and necessity for traditional GPU-centric AI scaling models. Along with a little bit of hype.
Cerebras is all about the differentiated architecture of its Wafer-Scale Engine (WSE), purpose-built for large-scale inference and model execution. The company has spent years positioning its wafer-scale architecture as an answer to some of the inefficiencies associated with distributed GPU clusters, particularly around memory movement, interconnect overhead, and model scaling complexity. And in many ways, its recent partnership with AWS validates its approach. (As does NVIDIA’s recent unveiling of its own integrated Groq LPU).
Cerebras carved out and occupies a different position in the market than many traditional accelerator vendors. It doesn’t seem to be trying to compete broadly across every enterprise workload category. Instead, it appears increasingly focused on high-performance AI services, large-model inference, sovereign AI deployments, and environments where simplified scaling and extremely large model support matter.
The question now is what comes next.
One area worth watching is whether Cerebras can extend meaningfully into enterprise AI deployment models. Today, much of the company’s visibility remains tied to cloud-scale and service-provider environments. But over time, enterprise AI adoption may create opportunities for more specialized architectures. This is particularly true for organizations looking to deploy large inference environments without hyperscaler-scale infrastructure complexity.
With this said, the enterprise path is far from automatic. Enterprise buyers value ecosystem maturity, software compatibility, operational tooling, procurement simplicity, and long-term platform confidence as much as raw performance.
Above all, enterprise buyers value familiarity. Cerebras still has work to do there.
Still, the IPO reinforces a broader industry reality: AI infrastructure is entering a diversification phase. The market is no longer asking if there are alternatives to conventional GPU at scale. It is asking which alternatives to bet on.
HPE, NetApp, and Everpure (formerly Pure Storage) all made infrastructure moves last week, and none of them led with capacity, performance, or speeds and feeds. The conversation has moved past where storage and private cloud spent the last decade competing.
HPE’s next-gen Private Cloud landed last week. Enterprises modernizing private cloud for AI are stuck stitching together virtualization, containers, backup, migration, and data protection across five different vendors and operating models. That complexity is what’s keeping AI workloads from getting deployed. HPE’s pitch is that all of it comes from one vendor in one platform, so the operating model gets simpler and the path to running AI on private infrastructure gets shorter. For enterprises planning private cloud upgrades while the AI conversation passes them by, simpler is what actually unblocks the project. Whether the integration runs as cleanly in practice as it does in the announcement is the next thing to watch.
The NetApp and Red Hat OpenShift integration that landed last week stands out for its breadth. NetApp is making ONTAP available as the storage layer across an expanding set of virtualization runtimes, and Red Hat’s KubeVirt-based OpenShift Virtualization is the latest. Together with the Nutanix alliance and first-party services on AWS, Azure, and Google Cloud, NetApp shows up underneath whichever runtime customers choose to standardize on. For enterprises picking a virtualization strategy for the AI era, ONTAP being available regardless of runtime takes one variable off the table. NetApp’s architectural argument is that the storage layer doesn’t have to be re-decided every time the runtime conversation evolves.
Everpure’s acquisition of 1touch closed last week. Storage vendors have spent two years figuring out how to matter in the AI era as capacity and performance get commoditized. 1touch gives Everpure a data discovery and classification layer that makes the data on its arrays semantically understandable to agents and models, which is what AI workloads actually need. Everpure’s argument is that storage should know what the data is, which is a different job than knowing where it sits. For enterprises trying to put their unstructured data to work for AI without an army of data engineers cataloging it first, that capability matters. Whether Everpure can integrate 1touch deeply enough to differentiate is the open question.
Two more versions of the stack pitch landed last week, and the pattern is familiar by now. Vendors keep adding layers to what they sell so customers have fewer pieces to assemble, though some pitches are more complete than others.
Boomi World closed with three moves that tighten Boomi’s stack pitch. The Lunar.dev acquisition adds governed agent connectivity, the Couchbase partnership covers the data side, and the newly expanded ServiceNow tie-up deepens integration with ServiceNow’s AI Control Tower. The message is that Boomi wants to be the control plane for governed agent execution. That’s a more specific bet than competing on connector counts, which is where the integration market has been heading anyway. The vendors that survive the agent era will need to do more than move data between systems. Boomi is making the case that orchestration, identity, and execution belong in one platform. The harder question is where Boomi’s pitch overlaps with ServiceNow’s own ambitions for the same buyer.
Quest’s announcement last week is a smaller version of the same pitch. The new Data Modeler and Data Intelligence components extend Quest’s existing data management platform, with modeling on one side and a data intelligence layer on the other. The stack pitch here is more modest than Boomi’s, but it speaks to the same underlying read on the market. Customers don’t want to assemble data modeling, data quality, lineage, and governance from four vendors anymore. For enterprises that already have Quest in their data management footprint, the extension matters because it’s one less seam in the operating model. The question is whether the broader buyer audience sees Quest as belonging in the same conversation as the bigger stack pitches, or whether this keeps the story focused on the existing base.
The unprecedented joint venture by AT&T, T-Mobile, and Verizon is a clear approach to improving the overall satellite connectivity experience for American cell-phone users and unifying the spectrum that the carriers have, potentially improving performance as well. Many, including SpaceX, see this as a hedge against Starlink direct-to-device (D2D) service becoming a fully vertical solution without carrier involvement. But right now, the carriers have a lot of the IP, the spectrum, and the customers. I also believe that the operators can make D2D much more accessible for any satellite provider, whether Amazon, ViaSat, or AST SpaceMobile, with a unified set of standards across all three carriers and harmonized spectrum. While I don’t think T-Mobile needs this partnership as much as the other two carriers because it’s already aligned with Starlink for its satellite service, I get the sense that maybe T-Mobile doesn’t fully trust SpaceX to not become a competitor. While the JV will be owned equally by all three carriers, it’s still unclear whether it will require FCC approval and whether the U.S. Department of Justice will challenge its existence based on the comments of some SpaceX executives.
Comcast’s CEO says that its wireless business is now its top priority. Comcast currently has 30 million cable broadband subscribers and 12 million video and cable TV subscribers. Its current wireless or mobile business, which blends Wi-Fi, CBRS, and a Verizon mobile virtual network operator (MVNO), has almost 10 million customers. The company has found success with wireless bundling to keep people on cable, even though fiber from Comcast’s competitors has proven to be much better in virtually every way. I believe that, long-term, Comcast and Charter will both need to move towards fully fiber networks if they want to compete with the likes of Verizon and AT&T, which are aggressively expanding their fiber networks and offering a superior product for the same or lower price. Note, though, that because of this focus on wireless customers, we are seeing the traditional big three carriers struggle to gain new subscribers as Comcast and Charter now have 20 million combined wireless customers. It will be interesting to see how Comcast continues to grow this business and how it affects the traditional mobile carriers, which by now have 15 million fixed wireless access (FWA) customers that they gained almost entirely from the likes of Comcast’s and Charter’s cable broadband customers.
Trump Mobile continues to struggle to deliver an actual Trump T1 phone, which has changed its shape and appearance many times. First, it was a mockup based on the Galaxy S25 Ultra; then it became a Chinese, white-labeled gold phone that failed to deliver on its Made-in-America claims. Now it looks to be a new Chinese-made phone, which the company claims will start shipping after almost a year of delays. While it is unclear which phone the Trump T1 is based on, there are rumors that its either a Wingtech model that was previously made for T-Mobile as the REVVL 7 Pro 5G, or an HTC U24 — but neither the camera configuration nor the ports match either of those phones. The company now claims to be “shaped by American innovation.” Besides the various launch problems and miscommunications, the MVNO service also offers nothing particularly special as far as coverage or cost, so this really comes down to the Trump brand more than anything.
HRL Laboratories isn’t widely recognized for its significant architectural contributions to fault-tolerant quantum computing (FTQC). Formerly known as Hughes Research Laboratories, HRL is a vertically integrated research institution jointly owned by Boeing and General Motors. It has deep roots in advanced semiconductor fabrication, and has evolved into one of the most consequential organizations in quantum computing, recognized by those in the know as an architectural leader in FTQC.
HRL has special expertise in exchange-only silicon spin-qubit arrays, silicon carbide (SiC) photonics, and deployable quantum optical systems. These important technologies put it at the forefront of next-generation computing infrastructure.
Scalable Vision for Quantum Hardware
One important element of HRL’s research strategy is its development of two-dimensional silicon spin-qubit arrays with advanced multilevel interconnect structures. That makes it compatible with standard CMOS-like wafer manufacturing — the same technology we have used to scale classical semiconductor technology for decades. Aligning quantum hardware fabrication with proven, high-volume classical manufacturing techniques provides HRL with a practical path to scale physical qubits to fault tolerance.
This is so important because FTQC is the threshold at which quantum systems become useful for real-world problems. That threshold will require huge numbers of high-performing physical qubits that can be manufactured, integrated, and controlled at scale.
EO Qubits and the QPU Architecture
HRL’s quantum processing units (QPUs) integrate a custom-designed cryogenic CMOS controller, a novel high-density superconducting ribbon cable, and a low-noise exchange-only (EO) qubit device. The quantum chip has a three-rail array of 54 exchange-coupled quantum dots. These can be configured to host up to 18 EO qubits.
Silicon EO qubits are good for two significant reasons: (1) The control signaling is relatively simple, and (2) they are architecturally compatible with semiconductor manufacturing.
HRL’s integrated system demonstrates qubit performance for both single-qubit and entangling operations. According to the team’s recently published research, this advances the EO state of the art by an order of magnitude — a substantial leap that validates the team’s architectural choices.
Quantum Error Correction and the Future
HRL has moved beyond raw qubit performance and into the important domain of quantum error correction. HRL has implemented a distance-5 repetition code and a quantum error-detecting code, with results that have been validated using detailed comparisons and simulations. It is expected that these demonstrations will lead to full error correction, separating fragile experimental qubits from the logical qubits needed for utility-scale computation.
There is a strong practical side to this work. HRL’s approach is designed to facilitate a utility-scale quantum computer with manageable operational and capital requirements — a goal that is as much about economic viability as scientific achievement. The combination of CMOS-compatible manufacturing, advanced qubit control, and error correction should enable HRL Laboratories not only to build better qubits but also to establish a credible industrial pathway to FTQC.
Amazon launched Alexa for Shopping last week, merging its Rufus product assistant with Alexa+ and extending those capabilities across Amazon’s search bar, shopping app, website, and Echo Show devices for U.S. customers. The service combines product knowledge with shopping history, preferences, and cross-device context to automate routine purchases, build carts conversationally, track price history, and surface targeted recommendations. Its most consequential feature is “Buy for Me,” which Amazon says can complete purchases from Amazon and third-party retailers using saved payment and delivery information.
My own test of the assistant exposed both the promise and the problem. It recommended rhinestone glue. Immediate fail — although I also immediately understood why. I recently bought craft supplies to decorate my two high school seniors’ graduation caps. The system detected a crafting signal and moved toward an adjacent category. The logic was obvious to me, but the recommendation was still wrong because it lacked contextual understanding of what I had actually been making: grad caps for boys. That distinction matters, and in a retail setting, a bad recommendation is annoying. In a future where agents are expected to take more action on a shopper’s behalf, weak contextual reasoning can quickly undermine trust.
The assistant was much better when it shifted to a different pattern. I have been cooking a lot lately and am currently experimenting with Asian flavors. I often use Alexa for recipes or inspiration, and I rely heavily on Amazon Grocery and Whole Foods delivery because I travel, work full-time, and live 15 minutes from the closest grocery store. Alexa for Shopping recommended a KitchenAid attachment that can shred carrots, grind ginger, and thinly slice cucumbers, among other things. That one landed. I really did not know I needed it, but it was useful enough to add to cart immediately. This kind of hit-or-miss behavior is exactly why retail’s agentic moment is interesting. The technology can feel highly relevant when it correctly connects multiple signals, but it can also expose how easily recommendation engines confuse adjacency with intent.
That is where the enterprise relevance comes in. Enterprise software companies are already deploying agents that can take action inside business systems, whether that means procurement, service workflows, IT operations, or software management. The lesson I’m taking here from Amazon is that, regardless of the differences between consumer shopping and enterprise automation, trust in agents depends on more than task completion. Users need to understand why a recommendation or action was made, what data informed it, and where the limits are. When an AI system gets something wrong in a way that still seems superficially logical, it highlights a broader governance problem that enterprise buyers are now facing as well.
Amazon also has work to do on the control side. The company has not clearly defined the guardrails for autonomous purchasing, including spending thresholds, approval flows, dispute handling, and the level of user confirmation required before a transaction is completed. Those details will shape whether consumers see this as genuinely helpful or as one more layer of friction wrapped in AI. In the enterprise, the key takeaway is that agentic AI becomes more valuable when control, visibility, and context improve together. Capability alone will not be enough.
Unless otherwise noted, our analysts will be attending the following events in person.
August events coming soon.
September events coming soon.
December events coming soon.
Founder, CEO and Chief Analyst | + posts
Patrick Moorhead is the founder, CEO, and chief analyst of Moor Insights & Strategy. His big-picture view of technology is grounded in more than 20 years as an executive leading strategy, product management, product marketing, and corporate marketing functions at NCR, AT&T, Compaq, and AMD. He has shared his expertise in areas from silicon to infrastructure to enterprise SaaS and everything in-between in thousands of national broadcast appearances (CNBC, Yahoo Finance), articles (Forbes, CIO), research-based analyses, and podcast episodes. Today, he has 100+ CXO-level advisory clients and is often ranked the #1 technology industry analyst by ARInsights.
Paul Smith-Goodson is the Moor Insights & Strategy Vice President and Principal Analyst for quantum computing and artificial intelligence. His early interest in quantum began while working on a joint AT&T and Bell Labs project and, during 360 overviews of Murray Hill advanced projects, Peter Shor provided an overview of his ground-breaking research in quantum error correction.
Jason Andersen is vice president and principal analyst covering application development platforms, technologies, and services. Jason brings over 25 years of experience in product management, product marketing, corporate strategy, sales, and business development at Red Hat, IBM, and Stratus to his work for MI&S and its advisory clients. Working both in the field and in the headquarters of some of the most innovative technology companies, Jason has a wealth of experience in building great products and driving their adoption across a broad spectrum of industries and use cases.
Senior Analyst-in-Residence | + posts
Bill Curtis is the Moor Insights & Strategy Analyst in Residence for large-scale Internet of Things systems. Bill helps enterprises design distributed solutions that integrate the full end-to-end IoT stack from real-world devices to analytics.
Matt Kimball is a Moor Insights & Strategy senior datacenter analyst covering servers and storage. Matt’s 25 plus years of real-world experience in high tech spans from hardware to software as a product manager, product marketer, engineer and enterprise IT practitioner. This experience has led to a firm conviction that the success of an offering lies, of course, in a profitable, unique and targeted offering, but most importantly in the ability to position and communicate it effectively to the target audience.
Mel Brue is vice president and principal analyst covering modern work and financial services. Mel has more than 25 years of real tech industry experience in marketing, business development, and communications across various disciplines, both in-house and at agencies, with companies ranging from start-ups to global brands. She has built a unique specialty working in technology and highly regulated spaces, such as mobile payments and finance, gaming, automotive, wine and spirits, and mobile content, ensuring initiatives address the needs of customers, employees, lobbyists and legislators, as well as shareholders.
Mike Leone is a principal analyst at Moor Insights & Strategy covering data platforms and analytics, data infrastructure and storage, and data governance and enterprise data strategy. He brings 15 years of analyst experience from his work at Enterprise Strategy Group, where he rose to practice director for data management, analytics, and AI. Mike's work is grounded in a strong technical and strategic foundation, including early roles in software and hardware engineering.
Anshel Sag is Moor Insights & Strategy’s in-house millennial with over 18 years of experience in the IT industry. Anshel has had extensive experience working with consumers and enterprises while interfacing with both B2B and B2C relationships, gaining empathy and understanding of what users really want. Some of his earliest experience goes back as far as his childhood when he started PC gaming at the ripe of old age of 5, building his first PC at 11, and learning his first programming languages at 13.
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。