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.
Each year, Microsoft draws on a unique combination of data sources, including extensive surveys and a huge quantity of anonymized signals from within its productivity apps, to produce the Work Trend Index report. And each year, our own Melody Brue digs deep into the findings to produce an analysis published on Forbes.
Her biggest takeaway this year is that, while enterprises implementing AI are certainly seeing productivity improvements, too many of those organizations are not doing enough of “the harder work of organizational redesign” that would allow them to use AI as a genuinely transformational force. Wherever your own company stands in the AI-implementation continuum, I encourage you to read Mel’s Forbes piece on the Work Trend Index 2026 for more of her insights on what is happening — and what is possible.
This week, Anshel and Matt are attending Computex in Taipei. 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 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 Invezz, TechStock, TechTarget, Security Brief Australia, TechEDT, CIO, Fierce Network, Data Center Knowledge, Computerworld, and CIO.com, with coverage spanning Astera Labs’ AI inference opportunity, Dell’s AI-ready datacenter, storage, server and cloud launches, EnterpriseClaw’s approach to AI agent governance, Google’s $5 billion neocloud plans with Blackstone, U.S. government–backed IBM and D-Wave CHIPS quantum deals, Microsoft and EY’s $1 billion commitment to help customers adopt agentic AI, and Salesforce’s deepening data strategy with Informatica. Meanwhile, Dell also highlighted Moor Insights & Strategy research in its own announcement on reimagining the modern data center for the AI era.
I again joined CNBC’s Closing Bell Overtime to react to NVIDIA’s Q1 earnings beat.
Our MI&S team also published 11 deliverables — 1 Forbes Article, 2 Research Notes, 2 Analyst Insights, and 6 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
The Enterprise OpenClaw Movement — Tech journalist Taryn Plumb reached out last week, and we discussed the idea that open source AI vendors are continuing to take a page from previous enterprise open source strategies. This is now gaining traction with agentic clients like OpenClaw. In this case, Plumb was asking about a company called Automation Anywhere. My take is that someone will eventually succeed with packaging, supporting, and hardening an open source AI stack for enterprise consumption the way Red Hat did for Linux and Java. In fact, that could be Red Hat itself, or possibly even a hardware company such as Dell that is trying to gain traction with private AI. While I am a bit more skeptical about Automation Anywhere, I do still believe that the distribution and trust layer for open source AI remains a wide-open opportunity. Here is a link to Plumb’s article.
Google Antigravity at Google I/O — After I came home from Google Next a month ago with little to say about Antigravity, I was happy that Google I/O filled in the picture. Antigravity is consistent with Google’s strategy of building a coherent, well-categorized AI stack. It’s now a desktop tool (joining Claude Cowork, Codex, Joule 2, and peers), with a CLI and SDK for flexibility. The shared SDK with Agent Builder is an interesting opportunity for creating a clean prototype-to-production handoff between line-of-business pros or product owners on one hand and developers on the other. We are also now seeing increasing use of slash commands, and /Grill-me stands out for stress-testing requirements and architecture. Bottom line: Google is supporting previously released AI dev tools for now, but with this new release I think the migration clock towards Antigravity has started.
IBM Concert: Agentic Observability Arrives — IBM launched Concert two years ago as a promising but perhaps premature standalone capability. Seeing it repositioned as an agentic observability platform makes me think the technology has finally caught up to the team’s ambitions. Digging deeper on Concert, I was able to have a great talk during IBM Think 2026 with Jennifer Fitzgerald, the director of product management for IBM Observability, as part of Six Five on the Road. Here’s a link so you can check it out.
Requirements Quality in AI Dev Tools — Here’s a short, pointed question to close this week’s entry: How do we ensure we’re pushing good requirements into our AI development tools? While the previously mentioned /Grill-Me is a good start to that effort, there are also more robust model-driven approaches out there. The garbage-in-garbage-out problem doesn’t disappear just because the IDE is agentic. To help improve requirements quality, AWS just announced a new requirements evaluation capability in its Kiro agentic IDE. While this is not the first time we have seen model-driven evaluations in an IDE, it is the first time with models that algorithmically evaluate an input. These new models have already been used in other AWS services like IAM and are showing promise in improving more consistent accuracy. I was able to discuss this with tech journalist Darryl K. Taft in this article.
The Enterprise OpenClaw Movement — Tech journalist Taryn Plumb reached out last week, and we discussed the idea that open source AI vendors are continuing to take a page from previous enterprise open source strategies. This is now gaining traction with agentic clients like OpenClaw. In this case, Plumb was asking about a company called Automation Anywhere. My take is that someone will eventually succeed with packaging, supporting, and hardening an open source AI stack for enterprise consumption the way Red Hat did for Linux and Java. In fact, that could be Red Hat itself, or possibly even a hardware company such as Dell that is trying to gain traction with private AI. While I am a bit more skeptical about Automation Anywhere, I do still believe that the distribution and trust layer for open source AI remains a wide-open opportunity. Here is a link to Plumb’s article.
Google Antigravity at Google I/O — After I came home from Google Next a month ago with little to say about Antigravity, I was happy that Google I/O filled in the picture. Antigravity is consistent with Google’s strategy of building a coherent, well-categorized AI stack. It’s now a desktop tool (joining Claude Cowork, Codex, Joule 2, and peers), with a CLI and SDK for flexibility. The shared SDK with Agent Builder is an interesting opportunity for creating a clean prototype-to-production handoff between line-of-business pros or product owners on one hand and developers on the other. We are also now seeing increasing use of slash commands, and /Grill-me stands out for stress-testing requirements and architecture. Bottom line: Google is supporting previously released AI dev tools for now, but with this new release I think the migration clock towards Antigravity has started.
IBM Concert: Agentic Observability Arrives — IBM launched Concert two years ago as a promising but perhaps premature standalone capability. Seeing it repositioned as an agentic observability platform makes me think the technology has finally caught up to the team’s ambitions. Digging deeper on Concert, I was able to have a great talk during IBM Think 2026 with Jennifer Fitzgerald, the director of product management for IBM Observability, as part of Six Five on the Road. Here’s a link so you can check it out.
Requirements Quality in AI Dev Tools — Here’s a short, pointed question to close this week’s entry: How do we ensure we’re pushing good requirements into our AI development tools? While the previously mentioned /Grill-Me is a good start to that effort, there are also more robust model-driven approaches out there. The garbage-in-garbage-out problem doesn’t disappear just because the IDE is agentic. To help improve requirements quality, AWS just announced a new requirements evaluation capability in its Kiro agentic IDE. While this is not the first time we have seen model-driven evaluations in an IDE, it is the first time with models that algorithmically evaluate an input. These new models have already been used in other AWS services like IAM and are showing promise in improving more consistent accuracy. I was able to discuss this with tech journalist Darryl K. Taft in this article.
At Google I/O, the company announced its latest AI model, Gemini 3.5 Flash, which the company says is smaller and cheaper to run than the Pro version. That said, Gemini 3.5 Flash also benchmarks significantly better than 3.1 Pro, providing all-around improvement. Gemini 3.5 Flash is also the basis for many of the company’s updated or new products, such as Antigravity 2.0, Google’s AI coding assistant.
Google also announced Gemini Omni, which is a new multi-modal model that allows users to both input and output content in virtually any format, whether images, video, text, or audio. It will launch first with video capabilities and will be available only to the Pro and Ultra tiers.
Speaking of restricted Gemini models, Google also announced Gemini Spark, the company’s personal AI agent, which is available only to Ultra users for now. (This is not to be confused with Meta’s Muse Spark.) The thing that makes Gemini Spark unique is that it’s always running on Google Cloud, so you don’t have to have your notebook open to keep it running.
At Google I/O, Google also showcased its overhaul of the Gemini App, which adds specific categories like Daily Brief, Images, Videos, and Library. Google also made Gemini Chats searchable, which is a welcome addition for frequent users who want to go back and find previous conversations with Gemini. This experience is available to all users and features visual updates across both the web and mobile apps; it is powered by the new Gemini 3.5 Flash model by default, but can also use Gemini 3.1 Pro or the new Gemini 3.1 Lite. Gemini 3.1 Lite is really designed for speed — even faster than Gemini 3.5 — but likely at the cost of accuracy and depth.
Blackstone and Google announced a joint venture to build a U.S.-based AI compute-as-a-service platform using Google’s TPUs. The partnership combines Blackstone’s capital with Google’s chip technology to offer enterprises flexible, scalable AI compute without the need to build their own datacenters. Blackstone is providing $5 billion in initial equity. The JV targets 500 megawatts of capacity, operational in 2027; the specific U.S. location is undisclosed.
My opinion: This joint venture might cause a structural shift in the AI economy because it decouples specialized AI compute from the traditional software bundles of hyperscale cloud providers. It has the potential of converting AI processing power into an independent, investable infrastructure asset class that could create a new blueprint for financing future AI hardware. It could also create a new competitor to challenge NVIDIA-centric infrastructure providers.
However, achieving the 2027 target will require overcoming major hurdles such as obsolescence of AI hardware. Also, large inventories of TPUs have already been prematurely depreciated before obtaining a full return of their value. Another obvious problem is squeezing 500 megawatts of power from a grid that is already stressed by large demand and regulatory issues.
At Zendesk Relate 2026 in Denver last week, the company advanced the positioning of its autonomous service platform. In particular, Zendesk extended its “Resolution Platform” narrative to both customer and employee services, and it proposed tying AI economics more closely to verifiable outcomes. Instead of presenting CX and EX as separate stacks, Zendesk describes an approach in which AI agents, copilots, knowledge, and workflows are designed to complete requests end to end, not just to log and route tickets. For CIOs and COOs, Zendesk claims this could improve handle time, containment, and customer satisfaction while reducing service operations fragmentation.
On the CX side, Zendesk is leaning into the idea of an autonomous service workforce. More specialized AI agents are designed to work across channels and back-end systems to handle increasingly complex issues. The ambition is a single operational fabric where human agents, AI agents, and copilots share context, follow policy, and can be governed centrally. If realized, this could let leaders scale automation without losing control over quality and compliance. A key message at the event was that much of the value from incremental automation and routing optimization may already be captured. This is why Zendesk is pushing toward autonomy and full resolutions as the next step. Product announcements also emphasized learning loops, governance, and orchestration as necessary foundations for autonomy, not optional add-ons.
Zendesk is also extending this platform story into employee experience (EX), positioning IT service management and internal service as a natural fit for the same resolution-centric foundation. The company highlighted Employee Service AI Agents, native asset management, and integrated workflows as ways for IT, HR, and other internal teams to apply automation, measurement, and policy controls similar to those used by their customer-facing counterparts. For executives looking at CX and EX together, Zendesk is making the case that a shared platform could simplify how service is delivered and measured across the enterprise.
Relate 2026 also continued Zendesk’s push for resolution-based pricing, where AI charges are tied to what the company defines as confirmed resolutions rather than simple interaction volume. For CIOs and COOs, that model has obvious appeal because it frames AI more as a variable cost tied to outcomes than as a fixed innovation expense, though it also raises important questions about how resolution is defined, governed, and audited in practice. Those questions are likely to matter as much as the pricing construct itself.
My Six Five Media On The Road videos from Relate 2026 add useful context to the product announcements. This includes my discussion with Zendesk CEO Tom Eggemeier on the autonomous service workforce as well as a session with Shashi Upadhyay, Zendesk’s president of product, engineering, and AI, on resolution as architecture. These conversations help frame how Zendesk is thinking about CX, EX, and outcome-based AI. As part of this, the company outlines a practical playbook for leaders: Start with a small set of high-impact use cases, define success around both efficiency and experience, and treat process and workforce change as key to any autonomy program. The conversations about what resolution actually means, and what “good” or “excellent” looks like in different environments, were thoughtful. It will be interesting to see if this framing helps shape a broader industry conversation about outcome pricing as the market develops clearer standards.
Informatica World genuinely surprised me last week, and I don’t say that often about a company six months into life under a new owner (Salesforce). I really liked that the company led with the cross-platform story instead of the Salesforce story, shipping headless data management across AWS, Microsoft, Google Cloud, Snowflake, and Databricks in a single day, with its CLAIRE conversational AI reaching into those same environments. Also awesome to see that Snowflake and Databricks were both willing to ship Informatica integrations on the same morning, because that’s exactly what a lot of us doubted would survive the Salesforce deal. The whole event resolves a question I’ve had since the deal closed: whether Salesforce would keep Informatica open or quietly wall it into its own stack. Salesforce clearly chose open.
Informatica is now positioning itself as the data trust layer that any AI agent leans on, no matter where it runs, whether that’s Agentforce, Copilot, Gemini, or something built on Bedrock. I’ll be honest: Going this open also turns up the pressure on the standalone data players who’ve sold themselves as neutral, vendor-agnostic choices, because Informatica just became one of the most aggressively multi-platform vendors in the category while sitting inside one of the biggest software companies around. The real test is whether that headless layer earns real production adoption on platforms that have nothing to do with Salesforce, and that’s what I’ll be watching for over the next year.
Monte Carlo is the other governance story I’m watching this week. The whole company has rebranded around agent trust, moving off of data observability and over to montecarlo.ai, with the pitch that it will watch your AI agents the way it used to watch your data pipelines. It’s framing this as a first, though I’d push back there because plenty of players are already chasing agent monitoring from the model side. What makes Monte Carlo’s version interesting to me is that it’s tying agent-behavior monitoring to data lineage, so when an agent does something dumb in production, you can trace it straight back to the upstream data-quality problem that usually caused it. For customers, that’s the difference between knowing an agent went off the rails and actually knowing why. Monte Carlo is onto something real, because as enterprises put agents into production at scale, someone or something has to keep them honest. What I’ll be watching is whether the production customers actually show up. In a space this crowded, real adoption is what makes a rebrand stick.
I spent last week at Dell Technologies World, and Dell is staking out on-prem as the home for agentic AI more aggressively than I’ve seen from the company before. Deskside Agentic AI was a standout for me, running autonomous agents locally on a workstation with the data staying on the device. That goes straight at the two reasons most agent projects never leave pilot — runaway token costs and the fear of exposing sensitive data. The partner story landed incredibly well, with Gemini, OpenAI, and Grok now running on-prem at the model layer and Palantir’s Foundry and ServiceNow at the application layer. Dell’s bet is that the model layer eventually commoditizes onto whoever controls data gravity and sovereignty, and it wants to be that floor no matter which frontier lab comes out on top. That only holds if Dell controls the data underneath. The AI Data Platform is the piece doing that work, indexing scattered unstructured data into governed pipelines, with a data orchestration layer on top to move and prep it for the models. And I’ll keep saying it: The data platform bet is the most important one Dell is making going forward.
Day 2 was when the datacenter substance showed up, and it backed the Day 1 story. PowerStore Elite is the new flagship, a real generational jump on performance and density, with ransomware detection now baked into the array itself. Then there’s the Automation Platform, an agentic layer for IT ops where agents watch the telemetry, flag problems before they turn into incidents, and tee up the fixes under human oversight. Stack that on top of storage, data protection, private cloud, and servers, and Dell is selling all of it as one operating model. That breadth is exactly what a lot of enterprises want right now. Some of it, though, is just keeping pace. The non-disruptive, multi-generational upgrade promise is table stakes now, and most of the serious storage players have a version of it. The real test comes after the launch. Can Dell’s services and channel scale all of this as customers start customizing? The first real deployments will answer that.
T-Mobile’s Live Translation service is now in beta for eligible postpaid customers. Broader commercial availability is expected later this year. The service runs in the network, eliminating the need for separate apps, downloads, or dedicated hardware. It supports real-time voice translation across multiple languages, and the company says it also preserves the caller’s natural tone. This should help maintain conversational context and nuance. With low-latency, high-throughput characteristics, T-Mobile can point to its network as a platform for AI workloads — both for translation and for future use cases.
The primary commercial use for this service is in contact centers and customer-facing operations that need multilingual support. A network-based translation solution streamlines deployment, as it is independent of existing applications or devices; all of this should simplify integration across legacy and modern systems. This approach also enables additional AI services — such as agent guidance, compliance monitoring, or call summarization — to be layered in at the network level via APIs. The main new development here is embedding AI directly into the communications network, which could make carriers critical partners in shaping enterprise communications.
From an integration standpoint, enterprises could treat network-based translation as a shared service between carrier connectivity and existing CCaaS or UC platforms. One pattern would route calls through the operator’s AI translation and analysis functions before they reach the contact center or collaboration system. Translated audio, transcripts, and metadata can be provided as enriched inputs with minimal changes to the downstream platform. Alternatively, these capabilities could be exposed as APIs for CCaaS and UC vendors to call on demand. Enterprises can maintain their current agent desktops and workflows while selectively adding network-level AI services where they offer value.
Alteryx used its Inspire conference last week to claim a part of the agentic stack that most vendors skip right over — what Alteryx is calling the business logic layer. The shift is away from their old “clean your data before the AI touches it” story toward something sharper that encodes and governs the actual rules your AI has to follow. It’s a smarter place to stand, because business rules are far harder to copy than data cleaning, and they sit right where Alteryx is already strong, in finance, audit, tax, and reconciliation. Think about it: Every one of those workflows has thousands of rules that have to run exactly right, and that’s the hard part when you’re putting an improvising AI agent in charge of work that has to come out the same way every single time. That gap is real, and Alteryx is betting it can own it.
Teradata’s Autonomous Knowledge and data sovereignty launch is one of the rare on-prem AI stories in a market where a lot of companies are putting all their eggs in the cloud basket. The centerpiece is Teradata Factory, a pre-built system on Dell and NVIDIA hardware that handles analytics, lakehouse, and AI workloads on a single box. The smart part is which customers it targets: the regulated shops in banking, defense, healthcare, and the EU that can’t put their most sensitive data in the cloud. That’s a group of potential customers the cloud-native platforms struggle to serve. Teradata is meeting them where their data already lives. On pricing, Teradata is pushing fixed, predictable economics instead of the per-query and per-GPU metering that turns cloud AI bills into a guessing game. For a company people love to write off, this is easily one of the strongest pitches Teradata’s made in years.
The XR space was buzzing at Google I/O last week with the official announcement of XREAL and Google’s collaborative Project Aura, which I had been lucky enough to try out (under NDA) at CES back in January. This product is slated to ship commercially this year, featuring XREAL’s 70-degree field-of-view optics along with a high-performance compute puck powered by Qualcomm Snapdragon chips. This is all while running Android XR and delivering — based on my trial of it — a highly immersive experience as a pair of glasses rather than a headset like the Samsung Galaxy XR or the Apple Vision Pro. I believe this form factor is what most people will gravitate toward for full AR because it delivers the best performance and user experience in a truly portable package. Since I last tried Project Aura, almost everything has improved significantly, including Android XR, which was natively built with Gemini AI in mind.
NVIDIA Edge Revenue
NVIDIA reported $81.6 billion in Q1 revenue last Wednesday, up 85% year over year. The company breaks its revenue into two segments: Data Center and Edge Computing. The Edge Computing segment is my primary interest, and it covers AI on diverse devices including gaming consoles, PCs, workstations, AI-RAN base stations, robotics, automotive, and (presumably) industrial and physical AI. Q1 revenue for this segment was $6.4 billion, up 29% year over year.
Many financial analysts interpret the Edge Computing segment as an expanded gaming segment, with Blackwell workstations as the dominant near-term driver. But Huang was explicit on the earnings call when he identified physical AI as “the next wave.” The company cited robots, factory automation, and autonomous vehicles as growth drivers, generating more than $9 billion in revenue over the trailing twelve months.
My take: Industry attention is on agentic AI for IT applications, as it should be — just follow the money. The physical AI opportunity is real, but it’s still early-stage. What matters for OT, robotics, and industrial AI isn’t the revenue mix today, but NVIDIA’s edge platform architecture. When physical AI breaks out as a material line item, the infrastructure decisions will already be locked in. Watch the platform evolution, both hardware and software — not the current revenue numbers.
Synaptics and Google
Synaptics and Google Research brought the Coralboard to Google I/O last week. This developer board runs on the Astra SL2619 SoC with dual NPUs — Synaptics Torq and Google’s open-source Coral — targeting always-on, multimodal edge AI for IoT, robotics, and industrial vision applications. The board supports hardware-accelerated Gemma 3 at 270 million parameters, and it’s suitable for constrained edge deployments.
In October 2025, I covered the SL2610 as the first commercial deployment of Google’s open-source, RISC-V based Coral NPU. The Coralboard is the next chapter: a developer platform bringing the Coral IP stack to a much broader audience, with Google Research co-presenting at Google I/O alongside Synaptics.
Coral is Google’s full-stack, open-source edge platform. The co-presentation at Google I/O is a useful indication of ecosystem validation, because Synaptics is the first commercial home for this technology. The Coralboard now has developer-community visibility, Gemma model support, and a silicon partner with a clear industrial edge roadmap.
My take: Six months ago, Synaptics had a chip with Google’s Coral NPU inside. Now it has a developer board with Google Research co-presenting at a major developer conference. That’s a meaningful step on the path from IP to deployable platform.
At Google I/O last week, the company gave an update on its partnerships with Samsung, Warby Parker, and Gentle Monster for AI smart glasses. Google has shifted its language from calling them “AI glasses” to describing them as audio or display glasses, differentiating by display capabilities or lack thereof. Warby Parker and Gentle Monster glasses were shown on stage, but none of the press or analysts really got to see them up close. Google said that these will launch in the fall, which I think is good for product maturity, though it misses the entire summer season. These are highly fashionable designs and extremely lightweight, which I think Meta struggles with in its Meta Ray-Ban Display glasses. Google and Samsung’s display glasses are almost half the weight of the Meta models, and you can feel the difference on your face.
Also at Google I/O, the company showed off its Beam spatial conferencing solution built with HP. In fact, the partnership with HP Dimension includes three different experiences, two of which are brand-new and not spatial. I believe that offering Beam/Dimension as more than just a spatial conferencing solution is a great idea because it could get people to use it more often and make it more likely that someone can make a spatial conferencing call. The ability to display multiple users on the screen and ensure that their audio is spatially accurate is functionality you simply can’t find anywhere else. This means that whether you’re using Zoom or Meet, you should get the best group call experience even when the other users don’t have a Beam/Dimension display. The second 2-D experience involves speaking with an AI avatar in multilingual settings powered by Gemini 3.5, and I found it compelling — even though I believe it should be in 3-D. Even so, it was clear that Google wants to demonstrate greater utility and reposition Beam as a high-end conferencing solution rather than just a spatial teleconferencing platform. Doing that could help the companies justify the $25,000 purchase price and $500/month subscription cost.
In a strategic shift from traditional subsidies to direct investment, the U.S. Department of Commerce has signed letters of intent to invest $2.013 billion across nine quantum computing companies. By acquiring minority, non-controlling equity stakes, the Trump administration aims to secure vital supply chains and counter China’s tech advancements before quantum systems hit commercial scale. The funding spreads across diverse, competing quantum methodologies — including superconducting, neutral-atom, and silicon-spin systems — to tackle core engineering hurdles like high error rates.
Key Allocations include: $1 billion to IBM to launch a superconducting wafer quantum foundry, $375 million to GlobalFoundries for a secure U.S. quantum foundry, and $100 million each to Atom Computing, D-Wave, Infleqtion, PsiQuantum, Quantinuum, and Rigetti.
Initiated by the Trump administration, funding was provided through the CHIPS and Science Act of 2022. It is a long-term investment in U.S. national security, drug research, and financial modeling, and it follows the pattern of the administration’s 2025 equity play with Intel.
My opinion: By removing the constant pressure on pure-play startups to secure highly dilutive private venture capital just to survive error-correction engineering and other development cycles, this investment allows these quantum companies to focus squarely on basic science. Ideally, the speculative timeline for achieving fault-tolerant quantum computing could contract by several years. It also puts the U.S. on an equal footing with China, where all tech ventures are state-funded. Also, by standardizing manufacturing protocols at the IBM and GlobalFoundries facilities, this move seeks to ensure that the U.S. and its allies have a guaranteed pipeline of critical cryostat architectures, low-loss photonics, and highly specialized microelectronics.
Six Five (Patrick Moorhead)
Aster Labs / Stock Surge / Patrick Moorhead / Invezz
Astera Labs stock jumps 16%: is AI inference the next big catalyst?
Dell / AI / Matt Kimball / TechStock
Wall Street Pushes Dell’s AI Run Again But Sends a Caution
Dell / AI, PowerRack Systems / Tech Target
Dell AI Factory gets rack-scale infrastructure refresh
Dell / New AI- Ready Launches, Storage, Servers, Cloud / Matt Kimball / Security Brief Australia
Dell launches AI-ready storage, servers & cloud tools
Dell / New AI- Ready Launches, Storage, Servers, Cloud / Matt Kimball / TechEDT
Dell updates storage, servers and automation tools for AI data centres
EnterpriseClaw / AI Agents / Jason Andersen / CIO
EnterpriseClaw wants to bring governance to the OpenClaw era
Google / Neocloud, Data Center / Jason Andersen / Fierce Network
Google is building a new $5B neocloud with Blackstone – but why?
IBM / Quantum / Paul Smith-Goodson / Data Center Knowledge
US-Backed IBM, D-Wave CHIPS Deals Expand Quantum Push
Microsoft / AI / Matt Kimball / Computer World
Microsoft, EY to spend $1 billion on helping customers buy agentic AI
Salesforce / Informatica, CIOs / Mike Leone / CIO
Salesforce extends its headless push into enterprise data via Informatica
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.
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.
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.
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.
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.
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.
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.
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.
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