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.
Intel spent decades as the bellwether of the semiconductor industry. While it remains a vitally important chip manufacturer — more so than some people give it credit for — it has been working for years under former CEO Pat Gelsinger and current CEO Lip-Bu Tan to reestablish its footing.
Intel CEO Lip-Bu Tan presents at Computex 2026 in Taipei. (Credit: Anshel Sag)
Last week, Matt Kimball published two pieces that analyze major areas of effort for the chip giant. His Research Brief about onshoring leading-edge chip production and attendant issues of IP sovereignty digs into Intel’s background in manufacturing and packaging, and what its new production nodes could mean for the U.S. domestic chip industry and for Intel Foundry. He also published a Research Note about what he saw from Intel at Computex 2026, which he thinks bodes well for the company’s position in enterprise AI — and underscores the progress it has made in its comeback. If you care about the chip industry, both of these are recommended reading.
After a hectic couple of months of conference travel, most of our team is enjoying some well-earned downtime this week. The biggest tech event on our calendar is the Qualcomm Investor Day in New York, which I’ll be attending. For a bit of a preview, see Anshel Sag’s Research Note from last week, where he talks about how Qualcomm’s messaging at Computex sets up what we expect to hear more about at Investor Day. This week, Anshel will also be attending part of the BIO International event in his hometown of San Diego. For news and hot takes from any events we attend — as well as fresh insights and research throughout the summer — be sure to follow us on X and LinkedIn.
Last week, Moor Insights & Strategy analysts’ perspectives appeared across business and technology outlets including InfoWorld, Reuters, TechTarget, VentureBeat, Data Center Knowledge, UC Today, IT Brief, The Source Code, TechStock2, and Tech Times. Coverage touched on AWS’s AI agents, Databricks’ new approach to ontology for AI agents, datacenter buildouts, Everpure’s new data architecture for AI, HPE’s AI networking efforts, Snap’s new Specs AR glasses, SpaceX’s planned acquisition of Cursor, new benchmarking for Snowflake’s agentic AI, and connecting fragmented workplace data for better insights.
Our MI&S team also published 16 deliverables — 1 Research Paper, 3 Research Notes, 1 Analyst Insight, 6 Field Notes, and 5 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
AWS Summit New York — Real Progress, Plus the New Amazon Quick User Agent
Last week’s AWS Summit in New York was a useful checkpoint on AWS progress, and my net read is real gains in security and the developer stack, with the general-purpose agent ambition still unproven. The infrastructure story stayed strong — a new Graviton 5 chip, but more notably Continuum, a family of agents for earlier security at AI scale — and the developer layer moved well beyond Kiro to new DevOps and release-management agents. AWS also made its new Amazon Quick user agent available and positioned it as an enterprise answer to Claude Cowork. I like the embedded knowledge graph, but the user experience could use some more work. The real test will be wiring AWS’s core differentiators of model choice, security, and evaluations into future releases. Read the full Field Note here.
The Claude-ification Effect — Does Microsoft Copilot Cowork Offer Something New?
AWS was not the only player to release a user agent last week. Microsoft also released its Copilot Cowork. After seeing both, my conclusion is that the key for vendors isn’t mimicking the Claude desktop experience but setting themselves apart. For Microsoft that means providing a natural extension of its other products and enterprise capabilities. For both of these areas, my first impression is that Microsoft did a pretty good job. I like the model choice (Claude or OpenAI, with a claimed 30% to 40% consumption savings versus Claude via the M365 Connector) and the provisioned, governed experience that beats today’s rampant shadow AI. The yellow flags: It’s cloud-only with no local file access, and the pricing is more complex, requiring an M365 Copilot USL plan plus consumption fees. So, like AWS, Microsoft has a viable starting point with future upside. Read the full Field Note here.
SpaceX’s $60 Billion Cursor Deal — Why Enterprises Should Be Cautious
SpaceX officially announced its plan to acquire Cursor for $60 billion in stock, and I’m more cautious than the “It’s all about the GPUs” camp — I think enterprise customers will be very concerned. xAI’s models and guardrails are very different from what Cursor has stood for, and the key issue is model choice: Will Cursor still point at models other than Grok? But even that buys comfort only for a while, especially as rivals like AWS’s Kiro, Google, and Microsoft have gotten genuinely good at governance and teaming within their AI developer agents. Outside of the enterprise, a completed Cursor deal would give xAI some new IP in its bid to stay competitive with Anthropic or OpenAI. I shared my full take in this CIO piece by Evan Schuman.
AWS Summit New York — Real Progress, Plus the New Amazon Quick User Agent
Last week’s AWS Summit in New York was a useful checkpoint on AWS progress, and my net read is real gains in security and the developer stack, with the general-purpose agent ambition still unproven. The infrastructure story stayed strong — a new Graviton 5 chip, but more notably Continuum, a family of agents for earlier security at AI scale — and the developer layer moved well beyond Kiro to new DevOps and release-management agents. AWS also made its new Amazon Quick user agent available and positioned it as an enterprise answer to Claude Cowork. I like the embedded knowledge graph, but the user experience could use some more work. The real test will be wiring AWS’s core differentiators of model choice, security, and evaluations into future releases. Read the full Field Note here.
The Claude-ification Effect — Does Microsoft Copilot Cowork Offer Something New?
AWS was not the only player to release a user agent last week. Microsoft also released its Copilot Cowork. After seeing both, my conclusion is that the key for vendors isn’t mimicking the Claude desktop experience but setting themselves apart. For Microsoft that means providing a natural extension of its other products and enterprise capabilities. For both of these areas, my first impression is that Microsoft did a pretty good job. I like the model choice (Claude or OpenAI, with a claimed 30% to 40% consumption savings versus Claude via the M365 Connector) and the provisioned, governed experience that beats today’s rampant shadow AI. The yellow flags: It’s cloud-only with no local file access, and the pricing is more complex, requiring an M365 Copilot USL plan plus consumption fees. So, like AWS, Microsoft has a viable starting point with future upside. Read the full Field Note here.
SpaceX’s $60 Billion Cursor Deal — Why Enterprises Should Be Cautious
SpaceX officially announced its plan to acquire Cursor for $60 billion in stock, and I’m more cautious than the “It’s all about the GPUs” camp — I think enterprise customers will be very concerned. xAI’s models and guardrails are very different from what Cursor has stood for, and the key issue is model choice: Will Cursor still point at models other than Grok? But even that buys comfort only for a while, especially as rivals like AWS’s Kiro, Google, and Microsoft have gotten genuinely good at governance and teaming within their AI developer agents. Outside of the enterprise, a completed Cursor deal would give xAI some new IP in its bid to stay competitive with Anthropic or OpenAI. I shared my full take in this CIO piece by Evan Schuman.
Databricks has opened up its Unity AI Gateway — the layer that governs which models and agents can touch enterprise data — to outside tools. Instead of building its own versions of agent security and identity controls, it has turned the Gateway into the place where the tools that enterprises already use plug in. That keeps governance behind one front door instead of fragmenting it across another point product. This matters most for a problem I keep hearing about from data leaders, for whom the big AI worry has flipped from going too slow to running up a bill nobody can explain. The Gateway is built to let you set a budget per employee or per agent, then automatically drop to a cheaper model on its own when the work doesn’t need the more expensive one. These spending controls belong in the same layer that already governs access, because that’s where the money actually gets spent. For a tech buyer, the question shifts away from which agent-security point product to chase, and toward whether one governance layer can hold all of them.
Cohesity takes on the same problem through recovery — the part most people skip when they talk about governing agents. Its new Maestro architecture exposes data protection and recovery as something you drive from inside Claude, ChatGPT, or Gemini, with no separate console to open. Ask it what changed overnight, find out where a recovery gap exists, or kick off a restore, all from the assistant a team already has running. (Note that much of Maestro is announced rather than shipped, with the fuller version still to come.)
Agents are heading toward acting on data on their own, and when they do, recovery can’t continue as something humans do by hand in a separate tool after a problem hits. It has to be reachable where the work happens. That’s the challenge these vendors are working to solve.
I’ve watched Everpure inch toward this, and at its Accelerate show last week it finally made it official: The company is done positioning itself as purely a storage vendor. Its argument now is that data carrying governance and context at the storage layer is the asset, while raw storage capacity is the commodity underneath. Two pieces actually shipped in support of that. The data-management technology the company bought in February now ships as a real product. How integration would play out was the big open question after that deal, and shipping an actual product is the first real answer. The company also launched general availability for its data-prep engine for AI, which is built to cut months of grunt work out of getting raw data ready for models. Put together, these two moves are the most concrete proof yet that the company’s bigger vision is becoming product, and this is the strongest that Everpure’s data-platform story has looked.
HPE is making the same bet — that the governed data foundation belongs on-prem where the data already lives — and the piece I want to focus on is Data Fabric. The 8.2 release turns this into a governed data layer for agents, with a global catalog and native support for MCP, the open standard agents use to reach data, so an agent can find and use what it needs across a sprawling data estate under one set of rules. That’s HPE doing for its installed base what the data-platform players are doing in the cloud, keeping governance attached to the data wherever it sits. Meanwhile, its new Alletra storage — built for AI — keeps the KV-cache that models reuse between requests close at hand. That’s one of the quieter levers for inference cost, and it ties straight into the spend conversation running through the rest of the week. Taking all of its announcements together, HPE is making a credible case as a governed, on-prem home for production agents. For a lot of regulated data, that’s exactly where it’s going to stay.
Running in parallel to HPE Discover in Las Vegas last week was the annual Everpure //Accelerate conference. While it’s a little bit of a pain trying to run back and forth, it was certainly worth the Uber rides. Everpure had a compelling story, and throughout its event, the company did a great job of walking its customers (and analysts) through its transition.
That transition from Pure Storage to Everpure is easy to understand. The industry is moving from a conversation about infrastructure to a conversation about data. AI has amplified that shift. Data quality, governance, accessibility, and management increasingly determine whether AI projects succeed or fail. It’s not surprising that Everpure wants to participate in that conversation. And the opportunity is significant.
What’s also significant is the challenge in making that transition without losing sight of what made the company successful in the first place. It’s easy for companies to look at the market and decide they need to become something entirely different. We’ve seen it happen before: A company finds success in one market, sees growth in an adjacent market, and suddenly starts talking as if its history no longer matters. I think that’s a mistake.
Pure earned its position in the industry by solving storage problems better than many of its competitors. Simplicity, reliability, performance, and customer experience mattered. I don’t think these are things to move beyond. Rather, they should be built upon.
And this is why I don’t really see the company renaming as Everpure and the focus on data as a drastic pivot. Instead, I see it as a transition. Storage remains the foundation. Data management becomes the next layer built on top of it.
For CIOs, that’s an important distinction. Most organizations aren’t looking for another vendor telling them to rip-and-replace what they already have. They’re looking for partners that can help them evolve. The question isn’t whether Everpure can become a data company. The question is whether it can help customers make that same journey.
That’s where I think the opportunity is. Not in becoming something different, but in helping customers move from managing storage to managing data without losing what made the platform valuable in the first place.
HPE Discover took place in Las Vegas last week and, to nobody’s surprise, AI was the theme of the show. The star of the show was Juniper — the first time HPE was able to show it off since the Juniper acquisition closed in July 2025.
It was important for HPE to show customers what the combined company looks like. And it looks something like this: Networking matters. Not only does it matter, but as AI environments scale, moving data efficiently becomes every bit as important as the compute. Not somewhat important, but as critical as the accelerators waiting for the data to process and act upon.
It sounds like a good message. But it also happens to be true.
That said, I’m not sure that networking was what resonated the most with me. Yes — it’s critical. But it’s also kind of an obvious storyline. (I don’t mean that negatively.)
Here’s the setup for what I found more compelling. The industry has spent the last several years treating AI infrastructure as a procurement exercise. Buy GPUs. Build clusters. Add power, cooling, and networking. Repeat. This has fed the training wave — getting organizations started with AI. But it’s only a start and doesn’t solve the long-term challenge.
The long-term challenge is how enterprise IT organizations operate and manage the enterprise AI environment. AI introduces new infrastructure, new data flows, new security concerns, and increasing operational complexity. Most IT organizations aren’t struggling because they can’t buy technology. They’re struggling because they have to manage environments that are becoming dramatically more complex — without seeing a similar increase in staff or expertise.
That’s why last week I kept coming back to GreenLake Intelligence, OpsRamp, Morpheus, and HPE’s broader automation story. The company appears to recognize that the next phase of AI isn’t about deploying infrastructure. It’s about operating it.
For CIOs, I think this is the conversation that matters the most. GPUs? Yeah — critical. Data? Absolutely important. But managing all of this and abstracting away all the complexity is what matters the most.
Most organizations can acquire technology. Far fewer can consistently, securely, and efficiently operate increasingly complex environments. Networking may have been the headline at Discover, but I came away thinking that operations is where HPE has the biggest opportunity to differentiate itself over the next several years.
There were a lot of announcements at the Databricks Data + AI Summit last week, and one of my favorites sits underneath the flashier Genie relaunch; it’s a new context layer called Genie Ontology. It’s built to learn what your data means from how people actually use it, blend that with a curated set of definitions, and thus give an agent a real shot at understanding what your business means by a potentially ambiguous word like “margin.” I like that Databricks keeps taking active metadata seriously, and this is the most direct evidence of that yet. The payoff for a data team should be consistency, such that if you ask the same question about “margin” through three different agents you get the same number back.
Trust is the issue where I’d push back. Ranking context according to which source gets used most measures popularity, but popularity is a shaky stand-in for correctness, with nothing apparently in place yet to catch an answer that’s confidently wrong. So although Genie Ontology buys you consistency today, it’s worth remembering that consistency built on the most-used answer could mean consistently repeating a wrong answer. So if I were the tech buyer, I would want to make Databricks prove this tool’s correctness before trusting it with the calls that actually matter.
LTAP, its new architecture, raised the most questions for me — in a good way. It pairs Databricks’ transactional database with the analytics engine and keeps both on a single copy of data in open formats. Unifying transactions and analytics is old ground; the new part is the operational data landing in open formats, instead of being locked in a proprietary store while only the analytics half stays open. I’d still want the Databricks engineers to walk me through how both engines truly share one copy with no quiet sync step in the middle, but the direction is right. For a company that doesn’t want its operational data chained to one platform, an open option on the operational side is a genuinely different bet, and overdue.
The more aggressive move was Databricks walking into the customer data platform business, which puts it head-to-head with companies it has partnered with for years. The bigger pattern I took from the week was about cost. A new engine that removes a whole tier, a gateway that caps what an agent can spend, pipelines that LTAP makes unnecessary, and all of it aimed at spending less rather than doing more. Most of the market is still selling how to do more with AI. For where budgets actually sit right now, leading with cost is the smarter bet.
Applied Materials has launched its SENZ platform to extend its reach in the AR waveguide business. Although the company is best-known for its semiconductor manufacturing tools, more recently it has started providing optical waveguides used in the AR displays of smart glasses. SENZ is the company’s effort to more tightly integrate into the AR display optical pipeline, adding features to handle electrochromic dimming, prescription lenses, displays, and light engines in a single pre-integrated solution. This should make it faster and easier for more companies to enter the AR wearables market. This is partially possible thanks to a partnership with Qualcomm on its Snapdragon START program, which helps create reference designs for OEMs and ODMs to build from. Ultimately, Applied Materials brings to this challenge extensive manufacturing know-how, along with the ability to scale in ways that many competitors do not. I believe that the Applied Materials SENZ platform will allow OEMs to invest more in innovation in software and features for smart glasses, rather than spending too many resources on hardware design and integration.
CSA Unify Conference and Matter 1.6
The Connectivity Standards Alliance held its inaugural Matter conference June 16 to 18 in Austin. Unify was the first public Matter conference since the standard’s initial release in October 2022. The CSA used the opportunity to roll out Matter 1.6, the latest version of the specification, and to present a comprehensive long-range vision for connectivity. The vision began with a memorable “guru” experience — a fireside chat with IoT pioneer Kevin Ashton. The guy who coined the term “IoT” stitched together three decades of IoT evolution, from the first simple connected devices to a future where off-the-shelf, standards-based technologies make distributed computing pervasive. Matter is a big step along that path, so Ashton’s remarks were an insightful beginning to two days of detailed sessions.
Matter 1.6 was the week’s big reveal, and the headline feature is Joint Fabric. It’s a new approach to multi-admin device sharing across multiple ecosystems, such as Google, Apple, Alexa, and SmartThings. Say you’re using a mix of Apple and Google smart home application ecosystems in your home. In current versions of Matter, each ecosystem maintains its own connectivity fabric, and you add devices to each one separately. If you add a device using Google, you have to do it again with Apple. It’s not as bad as it sounds because onboarding credentials are (mostly) shared, but Matter 1.6 onboarding is one-and-done, which is how it should have been all along. It introduces a shared Joint Fabric — a single shared Matter device network. Any device added to the joint network is accessible to all ecosystems, dramatically simplifying device administration and making ecosystems plug-and-play for the first time. Add a device using your Google phone app, and it’s immediately usable in all other apps — Apple, Alexa, and SmartThings.
It’s hard to overstate the improvement in usability. However, nobody knows how long it will take for all ecosystems to support Joint Fabric because implementation requires these large companies to collaborate beyond the Matter specification. Past multi-admin efforts have not delivered speedy results. But I’m hoping for rapid progress in this case because making ecosystems plug-and-play is a usability gamechanger, and outliers who are slow to implement will lose mindshare and market share.
Three other refinements round out the release.
In my opinion, Joint Fabric cannot happen fast enough. A single authoritative database is a huge usability improvement over parallel ecosystem-specific copies that require redundant user actions and drift out of sync. This concept should have been included in Matter 1.0, but implementation is genuinely difficult and requires very high levels of ecosystem collaboration — and trust. The specification and SDK are now available, so the task for Apple, Google, Amazon, and Samsung is to treat implementation as a high priority and test the architecture in shipping products. Please join me in evangelizing this critically important feature.
In other news, the Aliro 1.0 spec for access control released in February, so there was no new specification at Unify. However, the event brought evidence of something more important: rapid deployment, with locks certified or on the way from Aqara, Kwikset, Nuki, Last Lock, Assa Abloy (Yale, HID), Allegion (Schlage), Kastle, and Ultraloq (Xthings). That’s impressive take-up for a four-month-old standard.
Finally, it was an honor to host the “futures” panel at Unify with an expert panel — Gilles Drieu of ADT, Jim Kitchen of Vessel Technologies, Spencer Koehl of Knaq, Neal Kondel of NXP Semiconductors, and David Loadman of BuildQM. Matter is more than just a spec. It’s a design pattern for connectivity standardization. Aliro is the first proof point of a new standard with a unique specification based on Matter’s design pattern. Our panel discussed practical ways to standardize connectivity in other vertical industries, either by extending Matter or by applying its design pattern. I expect to see examples of both before the next Unify conference.
Qualcomm Linux 2.0: An Upstream-First Reset for Dragonwing
Qualcomm announced Qualcomm Linux 2.0 for Dragonwing IoT platforms on June 19, 2026, with a livestream technical demonstration scheduled for June 30. The company frames the release as a fundamental reset rather than an incremental update. The new model is upstream-first, with platform-specific packages that track the mainline Linux kernel. A unified BSP, a single system image, and an overlay-based architecture should keep customizations clean across releases. The platform supports both the mainline kernel and the current long-term-support kernel, version 6.18. The June 30 session is expected to cover lifecycle and release strategy, core architecture and Yocto changes, migration paths from earlier versions, and a live bring-up on the Dragonwing IQ9 running end-to-end AI inference.
My take: Linux on vendor silicon has a long history of fragmented board support packages and platform-specific kernel forks, and the upstream-first framing directly addresses that history. Tracking mainline reduces the per-platform deviation that historically pushed each new product generation toward its own forked distribution. Execution is the open question, and the June 30 demonstration should tell us a lot. The direction is sound. A unified BSP and overlay-based customization is the right posture for a vendor supporting an expanding family of IoT and robotics SoCs without multiplying its own maintenance burden. I’ll have much more detail on this strategy in the coming weeks.
Prometheus: $12 Billion for an ‘Artificial General Engineer’
Prometheus, the physical AI venture co-led by Jeff Bezos and scientist/tech executive Vik Bajaj, raised $12 billion in Series B funding on June 11 at a $41 billion valuation. Total funding now exceeds $18 billion since the company launched in November 2025.
Prometheus is building what Bezos calls an “artificial general engineer”: AI tooling meant to compress the design-to-manufacturing cycle for complex physical products, from jet engines to drug compounds, by an order of magnitude or more.
My take: These are early days, and the new venture has released no demonstrations or benchmarks. The scale of the fundraising makes Prometheus the largest bet to date on this specific thesis: that proprietary real-world engineering and manufacturing data, not digital data scraped from the internet, is a defensible moat in AI. That thesis sits squarely on the physical-edge terrain that my research tracks. The company merits a closer look once there is a product to evaluate, rather than only a financial valuation. I’ll be watching it like a hawk.
Qualcomm has unveiled its latest chip, the Snapdragon Reality Elite, which is being featured first in XREAL’s Aura headset. What makes that product pairing so interesting is that Qualcomm worked closely with Google and XREAL to upgrade the chip inside the Aura to give it a performance and battery life boost — and it seems to have made a significant improvement. Qualcomm claims a 30% CPU and 60% GPU boost, which are both welcome in an environment where graphics and compute create ever-increasing demands on processors. The Reality Elite’s NPU increases by 160% to 47 TOPS, which represents a significant improvement that should be welcome for agentic workloads. I believe that the Reality Elite will also power more than split-compute devices like Aura; it will likely find its way into mixed-reality devices as well.
Last weekend, Qualcomm also demonstrated its Dragonwing platform running local AI workloads to predict real-time overtaking probability during the Anduril 250, a NASCAR Cup Series race held on the Qualcomm Circuit at Naval Base Coronado in San Diego. I attended the race, where I witnessed the BattleIQ prediction software in operation. With demonstrations like these, Qualcomm continues to show the range of edge-AI compute use cases it can enable, and I think the company is looking to expand its AI capabilities as well.
At the AWE 2026 industry conference, Snap announced SPECS, the company’s sixth generation of glasses. While Snap has been criticized for some of its design choices, one thing is certain: This is the first pair of fully standalone AR glasses. Most AR devices today are dependent on either an external battery or an external compute unit to reduce power or weight. While some of the criticisms of the size and weight of SPECS are valid, the reality is that nobody has ever built anything like it before, and a product like SPECS needs to be built first to enable future versions that are smaller and more capable. Time will tell how quickly Snap (or its competition) can deliver those versions.
Six Five (Patrick Moorhead)
AWS / AI Agents / TechTarget / Jason Andersen
AWS AI Agents hone DevSecOps chops amid GitHub troubles
Databricks / AI Agents / Mike Leone / InfoWorld
From RAG to ontology: Databricks bets on context as the key to trusted AI agents
Databricks / AI Agents / Mike Leone / VentureBeat
Databricks says it solved the decades-old data pipeline problem that’s been slowing AI agents
Datacenter / Matt Kimball / Data Center Knowledge
Missouri Emerges as the Next Hyperscale Frontier Amid Growing Power Demands
Everpure / Data Intelligence / Matt Kimball / IT Brief
Everpure launches data intelligence for AI projects
Everpure / Data Intelligence / Matt Kimball / The Source Code
Everpure bets its roadmap on data primacy, sequencing every release around one path to production AI
HPE / AI Tools / Mike Leone / TechTarget
HPE drives forward with AI networking push
HPE / HPE Integration Process / Mike Leone / TechTarget
HPE customers say the key to integration success is communication
Snap / Earnings, Smart Glasses / Anshel Sag / Reuters
Snap Bets on Life Beyond Smartphones With $2,195 Specs Augmented-Reality Glasses
Snap / Earnings, Smart Glasses / Anshel Sag / TechStock2
Snap shares trade lower heading into break after $2,195 Specs debut
Snap / Earnings, Smart Glasses / Anshel Sag / Tech Times
Snap Specs AR Glasses Open for Preorder at $2,195, But Resolution Stays Secret
SpaceX / Purchase of Cursor / Jason Andersen / InfoWorld
SpaceX’s planned $60 billion deal for Cursor raises questions for CIOs
Snowflake / CoWork, CoCo, AI Tools / Mike Leone / Tech Times
Snowflake Agentic AI Beats Claude Code on Its Own Benchmark: What That Means
Workplace, Modern Enterprise / Melody Brue / UC Today
How to Connect Workplace Data Sources That Were Never Designed to Work Together
Unless otherwise noted, our analysts will be attending the following events in person.
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。