New applications of AI are starting to help defenders harden security through faster security analyst onboarding, the evaluation of large volumes of threat intelligence, situation report generation, and more. Various kinds of copilot applications aid security operations through generative AI.

Cyber protection is becoming increasingly complex with the use of these applications and the potential for data leakage. Further compounding matters is an ever-expanding threat surface brought about by hybrid infrastructure and operational technology network deployments. Consequently, securing the use of AI in the carpeted and uncarpeted areas of enterprises for productivity gains must be matched by using AI to deliver improved organizational security. If applied correctly, a reinvigorated approach to AIOps can play a pivotal role in securing multidomain environments and counteract the weaponization of AI by bad actors.

Given these challenges, how do organizations deploy zero trust and AI together to drive better security outcomes? A complete solution must consider protection of the data used in AI tools, the tools themselves, and the underlying algorithmic models. It must also drive improved security outcomes from prevailing security infrastructure vendors that broadly safeguard organizations.

AI is poised to supercharge zero trust architectures. Enterprises demand a complete security platform bringing AI and zero trust together that is easy to deploy and manage. Moor Insights & Strategy (MI&S) believes that Zscaler is well positioned to deliver a zero trust platform infused with AI that is predictive, broad and deep and complements the management and security of burgeoning GenAI applications.

Click the logo below to download the research paper:

Table of Contents

• Executive Summary
• Fighting AI with AI and the Power of Zero Trust
• Securing AI at Scale
• Why Zscaler?
• AI — A Second Line of Defense
• Call to Action

Companies Cited:

• Zscaler

The post RESEARCH PAPER: Zscaler — Empowering Defenders with Zero Trust and AI appeared first on Moor Insights & Strategy.

DDI services, a network foundation that includes Domain Name System (DNS), Dynamic Host Configuration Protocol (DHCP) and IP address management (IPAM), can address these challenges. However, these services have historically been deployed individually by public cloud providers and require operational control that is pinned to each deployment. Consequently, a consolidated approach to DDI services across both on-premises and a wide range of cloud service providers can deliver significant value to enterprises. The potential benefits include simplified management, improved visibility, higher levels of network availability and resilience, enhanced security, and faster application deployment.

Moor Insights & Strategy believes that Infoblox is on a unique path to redefine critical universal network services such as DNS, DHCP, and IP address management in the hybrid, multi-cloud era with its new Infoblox Universal DDI Product Suite. This effort aims to break down the silos that exist between network, cloud, and security operations teams by providing improved visibility, simplified operational management, and higher levels of resiliency realized through three new service offerings: Infoblox Universal DDI Management, Infoblox Universal Asset Insights, and NIOS-X as a Service.

Click the logo below to download the research paper:

Table of Contents

• Executive Summary
• A Universal Approach to DDI Deployment and Management
• Improving Healthcare Outcomes with Infoblox
• Call to Action

Companies Cited:

• Infoblox

The post RESEARCH PAPER: Infoblox — Redefining Network Services in the Multi-Cloud Era appeared first on Moor Insights & Strategy.

Cohesity uses AI and machine learning in two ways. First, AI and machine learning have been built into Cohesity’s platform from the start to help detect threats, classify large data blocks, and protect critical data and workflows. The company continually updates critical functionality including anomaly detection to identify threats, data classification for better management of sensitive data, and cyber vaulting to mitigate any attacks that do occur. Second, Cohesity employs retrieval-augmented generative (RAG) AI for its enterprise data insights assistant, Gaia.

The $7 billion combination of Cohesity with Veritas’ enterprise data protection business, expected to close by the end of 2024, represents a significant shift in the data security and management industry. When completed, this combination will create the largest data protection and management organization globally with a significantly expanded market presence, serving over 13,000 customers, including more than 85 of the Fortune 100 and nearly 70% of the Global 500. The combined entity will have projected revenues of approximately $2.0 billion in the 2025 fiscal year.

According to Sanjay Poonen, president and CEO of Cohesity, “This deal combines Cohesity’s speed and innovation with Veritas’ global presence and installed base. We will lead the next era of AI-powered data security and management by bringing together the best of both product portfolios.” This report explores the background of each company, the logic behind the combination, and the likely path forward for the combined organization in a highly competitive market.

You can download the paper by clicking on the logo below:

Table of Contents

• Summary
• Cohesity Background
• Veritas Background
• Creating a Powerhouse in the Global Data Security Industry
• Cohesity Products and Services
• Compliance and Governance Features
• Expected Benefits of the Cohesity-Veritas Combination for Customers
• The Path Forward: Cohesity’s Plan for Innovation
• Strategic Alliances and Partnerships
• Competitive Industry Landscape and Challenges
• Future Growth and Development
• What Modern Data Management and Security Solutions Must Provide

Companies Cited:

• Cohesity
• Veritas

The post RESEARCH PAPER: Cohesity Creates a New Data Security Powerhouse Through Veritas Deal appeared first on Moor Insights & Strategy.

Without question, higher network bandwidth and lower latency within enterprise networks improve both business operations and customer experiences. But where should one start with a network transformation journey? Most organizations focus on an assessment process to define critical infrastructure requirements, then create plans based on the findings. While this is a logical process, it often results in a proliferation of equipment and applications that forces IT staff into reactive, swivel-chair management, higher operational expense, and complexity. Reaching the best answer requires a delicate balance: infrastructure should not be overprovisioned, yet it should provide headroom for future application and workload needs.

Thanks to factors ranging from globalization to hybrid work, business operations have become highly distributed. The challenges this creates are compounded by the need to accommodate bandwidth-hungry applications such as generative AI that burden networking infrastructure. In this context, enterprises require simpler ways to deploy, manage, and transition the connectivity infrastructure to handle current and future needs. Infrastructure changes come with significant transition costs, and it is often difficult to calculate the return on investment for future use cases with costs that may be unknown. Organizations must also account for enhanced security in an ever-evolving threat landscape, as well as guidance in risk analysis, planning, implementation, and ongoing management of network and security operations. Given these complexities and the many networking choices available, it is no surprise that enterprises do not know where to start.

Moor Insights & Strategy believes that Zayo delivers the optimal balance of support and connectivity that enterprises require to meet their network transformation needs. Besides providing scalable wavelengths, dark fiber, and private networking services tailored to customer use cases and workloads, Zayo can also serve as a trusted advisor throughout the entire lifecycle. The result is a highly engineered set of connectivity services that are easy to plan for, deploy, and manage.

You can download the paper by clicking on the logo below:

Table of Contents

• Summary
• Wavelengths Connectivity Tailored to Application and Workload Needs
• Dark Fiber and Private Networks
• Why Zayo?
• Call to Action

Companies Cited:

• Zayo
• Dropbox

The post RESEARCH PAPER: Enabling Enterprise Network Transformation at Scale appeared first on Moor Insights & Strategy.

Campus and branch networks delivered as a service with cloud-native principles provide many advantages. Among them, low- to zero-touch configuration, provisioning, and deployment; continuous feature updates; and the ability to treat the underlying technology as an operational expense. However, the most compelling consideration is the ability for IaaS principles and SaaS models to be deployed at an organization’s network edge.

IT operators can take advantage of improved connectivity performance at the point of data creation, enhanced by service levels that mimic cloud infrastructure. Tedious network upkeep, software upgrades, and daily maintenance tasks can also be offloaded, freeing teams to provide more value-added, line-of-business support. At the same time, AI is poised to radically transform these operational models, enabling full automation of lifecycle management services and leading to optimized business outcomes. However, there are many solutions to choose from, and many are solely operational expense-oriented and aimed at offering procurement flexibility.

Because enterprise networks are not created equal, organizations seek guidance in determining which connectivity IaaS offerings deliver simplicity in operational workflows, agility in software innovation, resilience across different domain environments, service level guarantees, and the highest levels of security. IaaS is clearly a more flexible and cost-effective deployment consideration than managed service provider models, but many solutions are refactored legacy architectures. The latter is often a result of acquisitions made by larger networking providers aimed at shortening product development cycles and time to market. This manifestation presents challenges to integration, scalability, AI innovation, automation, and interoperability.

Conversely, Moor Insights & Strategy believes that Nile is well-positioned to deliver what enterprises require today from an AI-infused, modern IaaS connectivity offering. The company offers a ground-up, modern, highly containerized, and microservices software architecture that delivers a single networking and zero-trust security service backed by performance guarantees.

You can download the paper by clicking on the logo below:

Table of Contents

• Executive Summary
• A New Set of Requirements Necessitate a New Approach
• Why Nile
• Call to Action

Companies Cited:

• Nile

The post RESEARCH PAPER: Nile — A New and Modern Approach to Enterprise Networks appeared first on Moor Insights & Strategy.

Digital transformation is a term that has existed for some time. It is also a practice (and trend) that is evergreen. In fact, technology has been used to drive better business outcomes for decades. What’s new is the focus on data feeding artificial intelligence (AI) models and analytics engines as key enablers of automated business processes.

The most recent wave of digital transformation has seen a second trend that has caused many organizations to reconsider efforts — generative AI (GAI). The use of foundational models and large language models (LLMs) to drive all facets of business operations has become essential.   As a result, many organizations have rescoped transformation efforts to optimize deployments.

With such a focus on data-driven outcomes, the expectations across an organization are understandably high. Faster, better, and higher quality are not just platitudes; they are key metrics that determine success, regardless of whether an organization delivers a new product to the market or provides public services.

Indeed, digital transformation is the monetization of data.

The challenges many enterprises face when undergoing digital transformation can be mapped across four vectors — culture (people), operational (processes, procedures), technology, and data. Each vector is a critical element to the success of any transformational effort.

This research brief explores the tensions organizations face across these success factors while driving toward an AI-enabled, digitally transformed state. Further, this paper introduces the Iron Mountain InSight Digital Experience Platform (DXP) and explains how this SaaS-based platform is critical to the digital transformation process.

You can download the paper by clicking on the logo below:

Table of Contents

• Situation Analysis
• The Potential of the AI Wave is Significant
  • Successful Outcomes Require Successful Planning
• Digital Transformation is Built on a Digital Platform
  • Defining the Ideal Digital Platform
• InSight Digital Experience Platform — The Cornerstone of Digital Transformation
  • DXP — Looking Under the Hood
• Iron Mountain is the Original Data Company
• Summary

Companies Cited:

• Iron Mountain
• Boston Consulting Group

The post RESEARCH PAPER: Digital Transformation Starts with a Digital Experience Platform appeared first on Moor Insights & Strategy.

The post RESEARCH PAPER: Enterprise AI Made Simple appeared first on Moor Insights & Strategy.

The post RESEARCH PAPER: Fastly: Delivering Exceptional Digital Experiences Through Edge Cloud Infrastructure appeared first on Moor Insights & Strategy.

The post RESEARCH PAPER: Untether AI – Where Performance Intersects With Efficiency appeared first on Moor Insights & Strategy.

The post RESEARCH PAPER: AMD Pensando – Silicon that Supercharges the Modern Datacenter appeared first on Moor Insights & Strategy.

The post RESEARCH PAPER: Fortinet: Optimizing Business Outcomes Through Network & Security Convergence appeared first on Moor Insights & Strategy.

The post RESEARCH PAPER: Infoblox: Unlocking The Power Of Domain Name System For Security appeared first on Moor Insights & Strategy.

The post RESEARCH PAPER: Intel’s AI Strategy, Offerings, And Differentiation appeared first on Moor Insights & Strategy.

While many say we live in the era of the data-driven enterprise, the reality is that we’ve always been driven by data. Three factors make today unique though: how much data we are generating, where we are generating this data, and what we can do with it. Where responsiveness to the needs of the market and customers is critical, organizations that can best utilize data are poised to succeed.

You can download the paper by clicking on the logo below:

Table Of Contents:

• Situation Analysis
• The Changing Enterprise App And Data Landscape
• The Modern Data Estate Requires Modern Platforms
• HCI Is The Foundation Of The Modern Platform
• Nutanix – Where Business Critical And Cloud Intersect
• Platform Modernization Checklist – Eight Questions To Ask
• Summary
• Figure 1: Data Lives Everywhere In The Modern Enterprise
• Figure 2: The Cloud Operating Model Delivered By Nutanix And Intel
• Figure 3: 5th Gen Xeon Delivers Significant Performance Gains

Companies Cited:

• HPE
• Intel
• Nutanix
• Oracle

The post RESEARCH PAPER: Accelerating Innovation Begins With Data appeared first on Moor Insights & Strategy.

The post RESEARCH PAPER: Intel Focuses Anew On The Automotive Market For Strategic Growth appeared first on Moor Insights & Strategy.

The post RESEARCH PAPER: Matter For CE Product Manufacturers appeared first on Moor Insights & Strategy.

You can download the paper by clicking on the logo below:

Table Of Contents:

• Executive Summary
• The PC Market And AI PC
• AI PC Applications And AI PC Future
• Call To Action
• Figure 1: Global PC Shipments – Canalys Quarterly 2020-2023

Companies Cited:

• Amazon
• AMD
• Bing
• Blackmagic Design
• Cisco
• Google
• Intel
• Meta
• Microsoft
• NVIDIA
• Skylum
• Topaz Labs
• VideoCom
• Qualcomm
• XSplit

The post RESEARCH PAPER: The New Era Of The AI PC appeared first on Moor Insights & Strategy.

The post RESEARCH PAPER: Super iPaaS: A Growing IT Integration Trend appeared first on Moor Insights & Strategy.

The post RESEARCH PAPER: AI In The Information-Rich Enterprise appeared first on Moor Insights & Strategy.

You can download the paper by clicking on the logo below:

Table Of Contents:

• Situation Analysis
• Digital Transformation Is Critical For Healthcare Providers
• The Patient Data Opportunity – And The Challenge
• Paperless Clinics – The Manifestation Of Transformation
• The Technology Behind The Paperless Clinic
• Iron Mountain Powers The Paperless Clinic
• InSight – How Iron Mountain Drives Efficiency
• Experience Matters
• Summary
• Figure 1: Digital Transformation Maturity
• Figure 2: Iron Mountain Healthcare Portfolio
• Figure 3: Iron Mountain Patient Decisioning Solution

Companies Cited:

• Iron Mountain

The post RESEARCH PAPER: Maintaining A Patient-Centric Focus In The Paperless Clinic appeared first on Moor Insights & Strategy.

The post RESEARCH PAPER: The Challenging State Of 5G appeared first on Moor Insights & Strategy.

The post RESEARCH PAPER: Driving Efficiency In Mortgage And Loan Processing appeared first on Moor Insights & Strategy.

The post RESEARCH PAPER: Conquering The Last Mile Of Digital Transformation appeared first on Moor Insights & Strategy.

Artificial intelligence (AI) is one of the most significant technological revolutions in recent history, if not the foremost, and is becoming a critical driver of productivity and growth across the global economy. Like many other enterprises, Wells Fargo has used AI technologies in the company’s products and operations for over a decade. Generative AI (GAI), part of a new generation of AI capabilities, now brings the potential to extend AI technology to more customer-facing products, and the bank plans to capitalize on it to position itself for significant forthcoming transformations. The bank’s strategy for the technology is AI-first — and at scale. The institution’s engineering teams are actively constructing foundational model frameworks to expedite and deploy AI securely and sustainably.

You can download the paper by clicking on the logo below:

Table Of Contents:

• Summary
• A Customer-Centric Approach To AI
• Wells Fargo Operations On the Cutting Edge Of Technology
• AI In An Ecosystem Of Partners
• Responsible AI
• Call To Action
• Figure 1: Wells Fargo Responsible AI

Companies Cited:

• Google
• Wells Fargo

The post RESEARCH PAPER: Wells Fargo Is Banking On An AI-First Strategy appeared first on Moor Insights & Strategy.

The post RESEARCH PAPER: MFA – The Alliance For Private Networks appeared first on Moor Insights & Strategy.

The post RESEARCH PAPER: Wells Fargo Is Taking Its Stagecoach To Rocketship Speeds appeared first on Moor Insights & Strategy.

The post RESEARCH PAPER: Intel – Protecting Data and Models Within Emerging AI Workflows appeared first on Moor Insights & Strategy.

The post RESEARCH PAPER:Winning The Data-Centric Digital Business In This Decade appeared first on Moor Insights & Strategy.

Companies Cited:

• IBM

The post RESEARCH PAPER: IBM Research Gets Us Closer to Quantum Advantage appeared first on Moor Insights & Strategy.

The post RESEARCH PAPER: Composable Data Infrastructure In The Digitally Transformed Enterprise appeared first on Moor Insights & Strategy.

Quantinuum recently released its second-generation quantum computer, the System Model H2 with an advanced ion trap shaped like an oval racetrack.

Table Of Contents:

• Summary
• Stealth Research Produces Unexpected Results
• Benchmarking
• Benchmarking Insights
• Why Ion Traps With Grid Configurations Are Necessary
• Future H2 Improvements
• Conclusions
• Figure 1: System Level Performance
• Figure 2: Application Benchmarks
• Figure 3: Components
• Figure 4: H2 Will Explore New Level Of Quantum Advantage
• Figure 5: Scaling High Quality Quibits
• Figure 6: Evolving Trap Design

Companies Cited:

• Google
• Microsoft
• Quantinuum

The post RESEARCH PAPER: Quantinuum Unveils Its Next Generation Quantum Computer appeared first on Moor Insights & Strategy.

You can download the paper by clicking on the logo below:

Table Of Contents:

• Situation Analysis
• The Threat Landscape Is Ever Changing And Never As Expected
• AI Introduces Complexity And Simplicity
• Enterprise Is More Challenged Than Ever
• The Path To A Trusted Environment
• HPE’s History Of Hardware-Based Security
• Summary
• Figure 1: The Devastating Effects Of Cybercrime
• Figure 2: IT Investments Not Paying Off
• Figure 3: Compute Ops Management security Capabilities

Companies Cited:

• HPE
• Microsoft
• Palo Alto Networks
• SolarWinds
• VMware

The post RESEARCH PAPER: Zero Trust – Five Steps For Enterprise IT appeared first on Moor Insights & Strategy.

Table Of Contents

• Situation Analysis
• Digital Transformation And Digitizing
• Iron Mountain – A Pioneer In Protecting IT Assets
• Intelligent Document Management – Making The Complex Easy
• InSight – Iron Mountain Has An App For That
• Iron Mountain’s Key Ingredient – The Human Touch
• Summary
• Figure 1: Data Capture In The Enterprise
• Figure 2: Iron Mountain Spans Physical To Digital
• Figure 3: Intelligent Document Processing

Companies Cited

• Iron Mountain

The post RESEARCH PAPER: Exploring Iron Mountain’s Role In The Digital Transformation Value Chain appeared first on Moor Insights & Strategy.

Matter, a new smart home standard from the Connectivity Standards Alliance (CSA), defines the next generation of consumer electronics (CE) connectivity. This new industry-standard protocol seamlessly interconnects smart home devices and applications from multiple manufacturers over networks that already exist in most homes. Backed by some of the biggest names in consumer electronics, Matter promises universal product interoperability, better user experiences, and secure, reliable connectivity. “Matter – Making Smart Homes Smarter” provides a detailed explanation of Matter — what it is, how it works, and why it’s essential to the consumer electronics industry. Matter is poised to accelerate smart home growth, but only if consumers are confident that new devices are secure and trustworthy.

Is Matter “secure enough?” In other words, are homes with a large and rapidly growing set of Matter-connected CE products more secure and private than today’s smart homes with a hodgepodge of non-interoperable connectivity schemes? To find out, we researched Matter’s security and privacy and summarized our findings in the first two sections of this paper. Because Matter security is built-in, not added-on, we then looked at the security aspects of Matter product design, manufacturing, and deployment. Finally, we evaluated the practicality of building secure Matter products within existing CE supply chains. We used NXP Semiconductor’s extensive Matter portfolio to show how Matter-enabled chips, software, and services combine to make Matter product development fast and efficient.

You can download the paper by clicking on the link below:

The post RESEARCH PAPER: Matter — Making Smart Homes More Secure appeared first on Moor Insights & Strategy.

You can download the paper by clicking on the logo below:

Table Of Contents:

• Executive Summary
• Defining A Complete CNAPP
• The Value Of CNAPP
• Call To Action

Companies Cited:

• Cisco

The post RESEARCH PAPER: Managing Security At Scale appeared first on Moor Insights & Strategy.

You can download the paper by clicking on the logo below:

Table Of Contents

• Summary
• Data Is The Lifeblood Of A Digitally Transformed Business
• Embracing The Hybrid Reality
• The Hybrid Data Platform
• What To Look For In A Hybrid Data Platform
• Wrapping Up

Companies Cited

• Cloudera

The post RESEARCH PAPER: A Hybrid Data Platform Is Critical For Digitally Transformation Businesses appeared first on Moor Insights & Strategy.

The digitally transformed business is powered by a new class of applications architected for portability, mobility, reusability, and security. However, these cloud-native applications run alongside and integrate with legacy three-tiered applications residing on legacy three-tiered architectures. Indeed, most enterprise datacenters are siloed environments that are independently deployed and managed. From COBOL-based applications running on mainframes to legacy three-tiered to distributed cloud-native workloads.

You can download the paper by clicking on the logo below:

Table Of Contents:

• Situation Analysis
• There’s An App For That
• Business Critical Goes Cloud
• Modernizing Infrastructure Is The First Step
• The Evolution Of HCI
• How HCI Has Modernized
• Not All HCI Is Created Equal
• Executive Summary
• Figure 1: The Modern, Distributed Compute Environment
• Figure 2: Mapping The Requirements For Modern HCI

Companies Cited:

• Nutanix

The post RESEARCH PAPER: Managing Applications And Data In The Digitally Transformed Business appeared first on Moor Insights & Strategy.

You can download the paper by clicking on the logo below:

Table Of Contents:

• Situation Analysis
• The Great Conundrum
• Digital Transformation Without IT Modernization Is Incomplete
• Nutanix, HPE & AMD Deliver The Cloud Operating Model
• Is Your IT Environment Cloudified?
• Summary
• Figure 1: Digital Transformation Requires IT Modernization
• Figure 2: Performance Starts With EPYC
• Figure 3: Achieving A Cloud Operating Model

Companies Cited:

• AMD
• HPE GreenLake
• Nutanix

The post RESEARCH PAPER: How IT Modernization Accelerates Digital Transformation appeared first on Moor Insights & Strategy.

You can download the paper by clicking on the logo below:

Table Of Contents

• Executive Summary
• Defining AI-Infused NaaS
• Why Aruba
• Call To Action

Companies Cited

• Aruba
• HPE

The post RESEARCH PAPER: The Role Of Artificial Intelligence In Networking As A Service appeared first on Moor Insights & Strategy.

You can download the paper by clicking on the logo below:

Table Of Contents:

• Executive Summary
• Introduction
• Smart Homes Aren’t Very Smart
• What Problems Does Matter Solve?
• The Quest For Interoperability
• Interoperability – A Consumer’s Point Of View
• Interoperability – A CE Industry Point Of View
• Three Keys To Matter’s Success
• Inside Matter
• NXP Leadership: Silicon And Software
• FAQ
• Consumer Questions
• Supplier Questions
• The Matter Effect
• Figure 1: The Matter Stick
• Figure 2: Matter-Enabled Smart Home Network Schematic
• Figure 3: Matter Bridging
• Figure 4: NXP Silicon For Matter And Thread

Companies Cited:

• Amazon
• Apple
• Google
• NXP

The post RESEARCH PAPER: Matter – Making Smart Homes Smarter appeared first on Moor Insights & Strategy.

• How the IBM – AWS consulting partnership has developed over the last year
• IBM Consulting and AWS announcement and how this helps their customers
• Why Johnson & Johnson was interested in IBM’s Platform Services on AWS
• How Johnson & Johnson leverages the platform
• The future of the IBM Consulting-AWS partnership

Be sure to subscribe to The Six Five Webcast so you never miss an episode.

You can watch the full video here:

You can listen to the conversation here:

Disclaimer: The Six Five On the Road is for information and entertainment purposes only. Over the course of this webcast, we may talk about companies that are publicly traded, and we may even reference that fact and their equity share price, but please do not take anything that we say as a recommendation about what you should do with your investment dollars. We are not investment advisors, and we do not ask that you treat us as such.

Transcript:

Patrick Moorhead: Hi, this is Pat Moorhead, and we are live in Las Vegas at AWS re:Invent 2022, in the IBM Suite, having a good time. Daniel, this event has been amazing, and it’s just great to see the… I like to call it, the maturation of the cloud. And sometimes I like to repeat myself, but I think it’s important to reflect back and look at the cloud… It’s only 15 years old here… and the partnerships that are required to pull it off, the technology, hybrid, multi-cloud. All these great things is really making it interesting.

Daniel Newman: You’re a little older than me, so I guess it all depends. As a percentage of our lifespan, the cloud has been around pretty much since I got out of high school. So we’re in different stages of our lives. But in all serious, every time we get to AWS re:Invent, you always get a really good sense of what the appetite is for cloud and this shifting demand. We’ve seen hybrid at scale. We’ve seen AI picking up in a big way. We’re seeing sustainability being talked about and hopefully being measured. And these are some of the big focus areas I’ve noticed here at this year’s event. But…

Patrick Moorhead: Hey, let’s talk about another focus here, and that’s consulting. And that’s a great intro for our guests here, Mahmoud and Atul. Great to see you. Thanks for coming on the show.

Mahmoud Elmashni: Thank you for having us.

Patrick Moorhead: First time Six Five guests. This is wonderful.

Mahmoud Elmashni: It’s exciting to be here.

Patrick Moorhead: Yeah. Maybe the first place to start, maybe introduce yourself. Maybe talk about what you do, Mahmoud. Let’s start with you.

Mahmoud Elmashni: Yeah, so I’m Mahmoud Elmashni, and I can assure you that I’ve been out of high school a lot longer than the cloud’s been around. So let’s get that out of the way. So…

Daniel Newman: It’s good to be young.

Mahmoud Elmashni: Yeah, right? I run our AWS business for IBM Consulting, and really took on this role middle of last year. So, second big re:Invent show in terms of us being here in a big way.

Daniel Newman: Welcome.

Patrick Moorhead: That’s great. How about you? I hear… We talked in the green room. They’re really working you, but you’re the customer, right?

Atul Sharma: That’s not true. It doesn’t feel like that way. I manage the ERP platforms and application at Johnson & Johnson. And as you know, Johnson & Johnson is one of the leading healthcare company. It operates in pharmaceutical medical devices and consumer divisions, and shifts products in 175 countries. So you can really imagine what kind of supply chain we might be operating. So just to let you know the scale, the service which I manage spans across 4,000 plus servers, 20,000 plus changes, which we perform every year, and 10,000 plus integrations, and 95,000 jobs, which we run every day to deliver products to our patients.

Daniel Newman: Kind of makes our jobs sound easy, right? Our jobs are fun.

Patrick Moorhead: Not if they’re having fun. But in serious, I always love when the customers come on, because we talk to the vendors a lot. Because that’s our work as analysts, we tend to talk to the tech industry. Customers are such terrific proof points for how complex this really is, and they can always provide-

Daniel Newman: It’s a reality check.

Patrick Moorhead: … kind of that ground truth of the analyst talking to the tech about what we can do. And then the customer comes on and says, “Well, this is what it actually looks like.” And so that’s great. We’re going to get to you in just a minute.

But Mahmoud, I want to start with you. Being on the ground, leading this business with AWS, talk a little bit through your lens about this IBM, AWS partnership and how it’s progressing.

Mahmoud Elmashni: Yeah, so we embarked on it in 2017. And I would say we kicked it into gear in 2020, when our CEO, Arvin Krishna, took over, right. And that’s when he said, “We’re going to lay out, go at this, meet our clients where they are, and really annoy 10 strategic partners to launch into with.” And one of them was AWS. So that meant investment in the millions in terms of capability, in terms of clients making lighthouse accounts, et cetera.

So now, fast forward a little bit. Last year we were named the Rising Star partner of the year, here at re:Invent for the Americas. This year we actually picked up another couple awards. Last night, the Global Innovator Award was awarded to IBM, also, the GSI partner of the year in Latin America, one of the fastest growing regions globally. And now we’re over 15,000 certified individuals. We’ve got 14 competencies. So we’ve really established that question of the capability up front, right, can IBM work with AWS? We’re one of the fastest growing GSIs for AWS. And in turn, AWS will tell you that we’re one of the fastest growing SIs for them.

Patrick Moorhead: That’s really exciting. And it just goes to show how much can change so quickly. And Daniel and I, we just stand back kind of in awe of how much change, how quickly has transpired inside of IBM. So a lot of announcements this week from IBM, here at re:Invent. We saw software. And IBM Consulting had some announcements as well. And I’m sure you love all your children the same, but what was the biggest announcement? And maybe talk about how, in the end, it benefits clients.

Mahmoud Elmashni: So one of the biggest announcements for us from an IBM Consulting perspective is our platform services capability on AWS that we’re launching here, right? And J&J was nice enough to almost be our Guinea pigs [inaudible 00:05:38] work it out there for us, because it’s a major proof point. You want to make sure it works for the client. And in a nutshell, this journey to the cloud, as you guys were talking about front, it’s not a drive to the neighborhood grocery store, right. It takes years, the complexity, hybrid cloud environments, on-prem, in the cloud, et cetera. So there’s a lot of nuances that come with that, right, applications being the backbone of any digital business. So you need to make sure you monitor that.

So we brought together a platform made up of AWS services, IBM consulting assets, IBM software, AI based leverages the database of over a thousand implementations to kind of take care of some of the alert fatigue, take care of proactively identifying problems on the system, et cetera, so that this way people can actually focus on that journey to the cloud and not just getting tagged every time there might be every little situation that needs to get addressed. And it really goes a long way to avoiding downtime for our clients, which is the biggest things in this journey. They all want do the journey, they all want to do it faster, but everybody reminds us, “I can’t afford the downtime,” which is understandable.

Patrick Moorhead: So this gives us an opportune moment to kind of turn to Atul. So the IBM platform services on AWS, I think he said you’re the Guinea pig. I think that’s what Mahmoud said. So talk a little bit about that. Talk a little bit about why you opted to take that early, that beta alpha, and kind of how you’re seeing that become a bigger opportunity for Johnson & Johnson.

Atul Sharma: So, my job, like I just said, we do so many changes. It’s a complex technology stack, which we manage. And generally I try to break it into three parts, core infrastructure, platform and applications, all right. And observability in each one of these areas is the key point, and the right information at the right time to be available, especially to our teams, is very important. Because if they don’t have the right insight, they will be making wrong decision in process, making human errors, and ultimately our customers will be impacted. So what we did with IBM, we already have them as a strategy partner in an application management business. We started looking at the problem which we were facing, and started designing… Actually did a lot of POCs. And one of them, we just put it in production, the version one of that, which is called Mobius, which is actually correlating all the events at these technology stacks, so that our teams have right information at the right time and can make the right decision, so that we are not having so many disruptions and outages, and customers are not impacted.

Patrick Moorhead: Yeah, observability is really… In the analyst… It keeps us really busy. We’ve seen this fractalization of not only the infrastructure, but a fractalization of even applications. Applications used to be very monolithic stove piped, but now you might be hitting four, five, six APIs at the same time, and they all have to be orchestrated together. There’s just a lot more that can go wrong now. So, question for you. You talked a little bit about the benefits, but you extrapolate a little bit more. What are the benefits that this solution is bringing to you?

Atul Sharma: So I actually joke with my team, whenever I try to ask for the data, what is actually impacting us? And actually, it takes almost a month to correlate the data and actually make sense out of it, even to devize a service improvement plan. Just imagine if that is available to you right at the click of the button. Your root cause analysis capabilities goes to a totally different level, so your chances of committing errors also go down substantially. And we are seeing the impact of that. Our outages are actually going down. So as we mature with these capabilities we have built, I think opportunities are endless.

Patrick Moorhead: It’s a great example of the power of observability. You and I have talked a lot about it on our show. We’ve been really following closely across the landscape and watching IBM, among several other companies, have really entered in a big way into the observability space. And you’re seeing like, “Hey, we’ve got all this data, all these applications, this environment. We’ve got to make sure it’s all being managed, run. And then of course, you want to layer in AI, automate as many things as possible.” Your infrastructure’s just massive and you’re a great example of the real challenge that companies have. And so, by the way, thanks for giving us that background.

But, Mahmoud, I want to pivot back to you to kind of give us one last thing. Since we’re here at re:Invent, the IBM, AWS partnership is clearly moving in the right direction. But give us… Can you glean any look into the future. Where is this going to head? What kind of advancements? What’s next?

Mahmoud Elmashni: Yeah, we actually just wrapped up an exercise on a joint three year plan together, going forward. So, how are we going to continue this rocket growth through 2025, right? And now that we’ve got our base capabilities, if you will, out of the way, and establish that in the marketplace, we’re really looking at bringing forward those industry specific offerings with AWS. If you ask AWS, they’ll tell you, “Why do you look to a GSI?” We look to a GSI to bring us industry expertise, right. From our perspective, we go to market by industry, so it’s perfect. As we’re looking to scale, it’s going to be both in terms of personnel as well as asset-based capabilities. So, focusing on bringing solutions to healthcare, life sciences, financial services, manufacturing, travel and hospitality. So that’s where we’re really going to be focused on over the next couple of years. And hopefully we’ll have more announcements throughout next year and even again here at re:Invent next year.

Patrick Moorhead: Yeah, it’s interesting. Us analysts sometimes want to look at everything in a horizontal. But in the end, there’s no business out there that’s horizontal. And they want vertical solutions. And I do understand the economies of scale that you get from having horizontal solutions and vertical solutions, but in the end, that’s really where they want their help.

I just want to end. Thank you both for coming on the show, Mahmoud and Atul. Congratulations on the awards you’ve won. I almost ran into them on the way in here. I was aware of them. But that’s great. And I know you just want to keep pushing the puck forward. And I’m really impressed at the speed at which the company has changed. And good to see the rewards that the market is providing you, and just focusing on those clients. Thanks for keeping all of us honest…

Atul Sharma: Yeah, thank you.

Patrick Moorhead: … IBM, industry analysts and just rolling your sleeves up and making it happen. I’ll tell you what, you have a role in shaping this industry. You’re not just a statistic and a revenue number. You help shape the direction that these trillion dollar market cap companies… I think it’s 50 trillion, if I look across all of them… and where they put their investments in, at the end of the day, and how you help them help you. So thanks coming on the show.

Atul Sharma: Thank you very much. Thanks for having me.

Daniel Newman: Well, that’s whole process of the cloud, growing up from being a teenager to an adult.

All right everybody. Well, thanks so much for tuning into The Six Five here in the IBM suite at AWS re:Invent 2022, from Patrick, from myself, our awesome guests. Tune into all those other episodes. Hit the subscribe button. But we’re saying goodbye. We’ll see you later.

The post IBM Consulting + AWS: From Better Together to Innovation Together – The Six Five In the Booth appeared first on Moor Insights & Strategy.

• Progression of IBM-AWS software agreement
• Highlights and advancements of the agreement
• IBM’s Embeddable AI strategy and how it may be leveraged with AWS

Be sure to subscribe to The Six Five Webcast so you never miss an episode.

You can watch the full video here:

You can listen to the conversation here:

Disclaimer: The Six Five On the Road is for information and entertainment purposes only. Over the course of this webcast, we may talk about companies that are publicly traded, and we may even reference that fact and their equity share price, but please do not take anything that we say as a recommendation about what you should do with your investment dollars. We are not investment advisors, and we do not ask that you treat us as such.

Transcript:

Patrick Moorhead: Hi. This is Pat Moorhead and we are live in Las Vegas at AWS re:Invent in the IBM suite. I’m here with my incredible co-host Daniel Newman. Daniel, we are here. We are back. This place is hopping.

Daniel Newman: It absolutely is. It’s quieter here than you saw when we were on the floor doing the videos and all of the energy.

Patrick Moorhead: That’s right.

Daniel Newman: Got to say, I kind of like the calmness for these kinds of conversations, but Pat, AWS re:Invent is always a hot moment. It’s a big event, and as analysts, something that we have to be paying attention to. Because this is really shaping what’s going on in the market with cloud, with the advancement of the enterprise.

Patrick Moorhead: Absolutely. I’m really enjoying the maturity of the cloud. The cloud is 15 years old. It’s a teenager. It’s not all the way there in adulthood, but it’s getting here. That brings us … Let’s introduce our guest, Tom Rosamilia. How are you doing?

Tom Rosamilia: Not a teenager. I’m doing great. Happy to be with you guys again.

Patrick Moorhead: It’s great. Gosh. We’ve had Tom multiple times on The Six Five. It’s great to see you again. Maybe for the audience, we’d love to hear … For those who don’t know you, what’s your charge at IBM? What are you focused on? What’s your span of control?

Tom Rosamilia: My focus is software. I have the privilege of running the software business for IBM. I’ve been doing that for about 18 months now. Prior to that, with hardware. When you and I first met.

Patrick Moorhead: That’s right.

Tom Rosamilia: But it’s a real great challenge and a privilege and an honor to do it. My boss has called the play. It’s all around hybrid cloud and AI. We’re fully embracing that. The great thing is everybody in IBM can get behind it. Everybody can find themselves in the strategy, so there’s really very little confusion. There’s a lot of focus. With all the moves we’ve made, he’s made, with the acquisition of Red Hat, with the spin-out of Kyndryl. This shift really to ecosystem of partners like AWS has really helped us.

Patrick Moorhead: It’s been an incredible run. I’ve been in and around IBM for, gosh, 32 years now. Competed with IBM in 1990, but watching the growth and the changes. It really has been an amazing thing to watch. Thanks for making our analyst firms look really smart. Congratulations on the success.

Tom Rosamilia: That was our goal.

Patrick Moorhead: I know, Tom. It’s all about us. We’re in the analyst business.

Daniel Newman: Somehow when they were in the war room figuring out their earnings announcement, I’m sure they were like, “That Pat and Dan.”

Patrick Moorhead: I know. I know.

Daniel Newman: But in all serious, there is a lot to be proud of right now. I remember listening the other day on CNBC. There was a comment about a 52-week high for the company. Obviously, you said, I think you and I backstage, something about, “We do really well in rough waters.” It’s a tough time. Even going back six months ago, we sat … It was right around six months ago.

Patrick Moorhead: Yup. In Boston.

Daniel Newman: I can’t believe how fast that’s gone.

Tom Rosamilia: I know.

Daniel Newman: But we came to you and you were actually just announcing a big partnership that IBM was rolling out with AWS. Here we are at AWS re:Invent. Talk a little bit about that. Where is that partnership at?

Tom Rosamilia: Well, we started about a year ago and signed our strategic agreement in May to collaborate on moving our software. Lots of IBM software available on AWS, but now to make it available as a service on AWS. Rather than bring your own license. And so, we announced back then 19 products. Now, it’s up to 20 products that will be available on AWS as a service. Since then, in August, we’ve been teaming with IBM Consulting on bringing these patterns to life and to our clients, which is great.

We made some strategic announcements around enabling partners, VADs and VARs, or value-added distributors and resellers and global system integrators, to be able to take advantage of and get credit for selling our software on AWS, which is a big breakthrough for them and for us. Lots have happened. Back then, I was telling you that there were about 10,000 certified practitioners within IBM consulting. Now, it’s up to 15,000. They’ve added 50% more skill.

Certifications really matter to partners. They know you’re investing and they know you’re committed when you get that many people certified in the technology. Today, we’re making a three-in-one beta product available as a service. And so, things like App Connect and things like Content Services, Envizi, and a beta around some analytics work that we’re doing with planning analytics. That should be available in the first quarter, so pretty soon. We’ve gone from zero to four eventually to 20 products and there’ll be more. It’s been a great journey. I really value the partnership I’m getting from Amazon.

Patrick Moorhead: A lot of progress. I know some people might say, “Well, IBM, they don’t move quickly.” I have to tell you. This is quick. The fact that it’s one year. This is not some 10-year, five-year, and we’re here now. You have products that are going, that are ready. That are going GA.

What I love about it the most is it’s really hitting … We call it trends, but it’s what clients are looking for. No longer are we debating if the future is hybrid. It’s hybrid. Okay? Some people are debating if it’s multi-cloud, but those are typically people who don’t want there to only be one cloud. But the fact is, I’ve never talked to a Fortune 1000 client that wasn’t on multi-cloud. It is the reality.

Tom Rosamilia: The data would say it’s greater than 90%. We can debate whether it’s a 100% or 92%.

Patrick Moorhead: Right.

Tom Rosamilia: They’re multi-cloud and they’re going to be. That’s really the shape of the world. They’re going to be hybrid. We’re seeing great collaboration. I was just talking to a client here about spanning their mainframe to AWS and being able to use some of the same software in both places, have the same skills, and unlock the potential that they get from both IBM and AWS, which is great.

Patrick Moorhead: Rob Thomas talked about embedded AI or embeddable AI. Can you talk a little bit more about that and how it relates to what you’re doing with AWS?

Tom Rosamilia: Actually, we made three libraries available largely to ISVs. These came out of IBM research. They’re things like NLP, natural language processing. Text-to-speech, speech-to-text, and some the capabilities around supply chain. We don’t talk about AI as a thing anymore. It’s embedded in a lot of what we do.

A lot of the products that we are making available have embedded AI in them. We had ISVs, independent software vendors, who wanted to do the same. We’re making those available to them, so they don’t have to write their own. We have some of the best researchers around speech and NLP. And so, by making those features and capabilities available, lots of people can do it. I think it’s a great way of showing that it’s AI everywhere.

Daniel Newman: Do you see … Because I was listening to the Adam Selipsky keynote this morning. Data was in focus. There was a few other things, but data was one of the things that are in focus. And of course, what you said hit home to me. AI is no longer like a category. AI is something that has overlaid everything in technology.

With what Rob talked about, that was within the IBM ecosystem. But within the IBM plus AWS ecosystem, do you see that progressing into the public cloud and into those partnerships?

Tom Rosamilia: I think it’s possible. I think what’s closer to see is that the products that we’re making available on AWS have the embedded AI in them. Products like Envizi for environmental use AI. Our security suite absolutely uses AI in figuring out what the bad guys are trying to do and looking for patterns. It’s everywhere now.

Daniel Newman: Seems like observability too, which has been another pretty big focus, would be a big opportunity for you guys to embed AI and make it available?

Tom Rosamilia: Absolutely. We’re really pleased with what progress we’re seeing with Turbonomic and Instana. Instana being not the market leader yet, but one designed for containers, which is great. And Turbo really being a market leader around resource management and the ability. By the way, we are making it available with Envizi.

Part of the way you can lower your carbon footprint is turning down things you don’t need to use. And so, Turbo resource managing your way to better outcomes. You can even imagine saying, “I’ll run it in this data center versus that data center, because I have a lower carbon footprint. Because the electricity there comes from wind. Over here, I’ll make it come from coal.” Your carbon footprint could be modified depending on where you put the work.

Patrick Moorhead: Tom, we’re early days here in AI. First of all, to have a good AI strategy and implementation, the data’s got to be clean. It has to be good. Not all data structures work best with AI. In fact, analytics can do a lot now. The way that I like to explain AI to the layperson is it’s an advanced version of analytics that’s using different algorithms and big data.

Tom Rosamilia: I told you that.

Patrick Moorhead: Did I lift that off you and give you zero credit? I may have. If I did, my apologies. I’ll be sure to credit you next time. My macro question though is … Do you see this speeding up everything for the enterprise? We see AI and ML being used in consumer, big hyperscaler applications, but it’s not nearly as fast as it is in the enterprise. You’re offering a PaaS service here. You’ll do anything as a service. But it seems like this would speed everything up for enterprises?

Tom Rosamilia: I think it will. I think the greater access to this technology, the better. The faster it’ll go. I have said it. I’m sure you came up with it yourself as well, but I think in some ways the industry chose to call it AI. And in some ways, it scares people. If we just said it was analytics plus, plus, plus … It probably would’ve been fine. But embedding it in different places. I’ll give you an example.

Daniel Newman: 5GE.

Patrick Moorhead: Exactly. Or 5G+.

Tom Rosamilia: 5G+.

Daniel Newman: 4GE. Whatever.

Tom Rosamilia: We’re working with McDonald’s on automating their drive-throughs. It’s speech recognition and figuring out what your order is. I got to see the demo, it’s really … You probably saw it.

Patrick Moorhead: We did at Think.

Tom Rosamilia: We had it at Think.

Patrick Moorhead: It got my entire order correct. It didn’t want to remove my pickles for some reason.

Tom Rosamilia: Well, we’ve probably worked that out since then. And it’s a really hard job to work in a drive-through. You do that for a couple of hours, and it’s just exhausting.

Patrick Moorhead: I did it. I did it as a kid. It’s tough.

Tom Rosamilia: There you go. There you go. Automating some of these jobs that not everyone wants is a good way to do it. But I don’t think AI is this bad thing that’s going to put people out of work. I really don’t. I think it’s really enabling people to work differently. Automating back office operations and HR functions and things like Watson Orchestrate that people can go in. You can even do some level of recording. Say, “I’ll do these steps. Watch me do them. Do it again a thousand times.”

Patrick Moorhead: There has to be something in supply chain. What I love about this is you’re hitting on the biggest conversations in the C-suite. This is what customers care about. It’s human capital. Whether it’s frontline workers or it’s information workers or it’s supply chain. Everybody’s spreadsheets in supply chain are changing. It would seem, because that is a big data problem, it has a lot of history behind cause and effect. And it’s multivariate. It seems like we could do something with that.

Tom Rosamilia: Absolutely. I think AI’s really going to help us with … Despite some downturn in tech firms and hiring, we still have a shortage of technology people. And so, we’re going to need to make this simpler. We’re going to need to make it more accessible for people, and we’re going to need to automate as much as we can.

Daniel Newman: Tom, you said so many sage things. I love that you talked about the spreadsheet. We’re going to have to wrap up here, but when I wrote Human/Machine, we actually studied this one very thing, Tom. In the future, is automation replacing and displacing? Or is it augmenting?

I think the answer is, “If we do it right, it’s augmenting.” As we saw with the talent shortage of these past few years, we threw a lot of bodies at problems and we ran out of bodies. That’s actually what happened. Now, we actually have to go back and finish all these projects, which is a great opportunity for IBM and I think part of why you’re doing so well. So congratulations, Tom.

Tom Rosamilia: Thank you. We’ll stay humble.

Daniel Newman: Loved having you here at AWS re:Invent in the IBM suite on The Six Five. Got to get all those things in there.

Patrick Moorhead: I know.

Daniel Newman: We appreciate you. Veteran to this show. We’ve got to have you back sometime soon.

Tom Rosamilia: Appreciate it, guys.

Daniel Newman: Thanks, Tom.

Tom Rosamilia: Thanks again.

Daniel Newman: Well, everybody, thank you so much for tuning in. Hit those other episodes of the IBM episodes of Six Five in the booth, on the road, in the suite.

Patrick Moorhead: Pretty much everywhere. IBM suite. This is a new product.

Daniel Newman: We’re everywhere. At AWS re:Invent. We really appreciate you tuning in. We’ll see you again really soon.

The post IBM-AWS Software Agreement: Alliance Update with IBM’s Tom Rosamilia – The Six Five In the Booth appeared first on Moor Insights & Strategy.

• Lattice Avant FPGA platform’s Power Efficiency, Performance, & AI Processing demos with competitive and generational comparisons
• The importance of thermal management and its effect on the overall cost
• Applications that benefit from lower power
• How Lattice Avant improves AI acceleration

Be sure to subscribe to The Six Five Webcast so you never miss an episode.

You can watch the full video here:

You can listen to the conversation here:

Disclaimer: The Six Five On the Road is for information and entertainment purposes only. Over the course of this webcast, we may talk about companies that are publicly traded, and we may even reference that fact and their equity share price, but please do not take anything that we say as a recommendation about what you should do with your investment dollars. We are not investment advisors, and we do not ask that you treat us as such.

Transcript:

Patrick Moorhead: Hi, this is Pat Moorhead, and we are live at Lattice’s Avant launch at the Computer History Museum in Silicon Valley. Things are rocking, huge announcement today. Daniel, this is awesome. You know how much I like product announcements.

Daniel Newman: I love being around the Computer History Museum. It’s so much of what we do. We love Silicon, we love big announcements, and we love hearing about the future of FPGAs. And Lattice has been a really exciting company, so big day today.

Patrick Moorhead: Yeah. What I love, too, and especially I love to geek out on the technology, but what I also appreciate is the doubling of the addressable market of the company. And that’s a big deal. How many announcements could potentially double the size of the company?

Daniel Newman: Well, we’ve watched, over the last eight to 12 quarters, Lattice Semiconductor has been there. And you and I both published numerous articles, Market Watch, Forbes. We’ve put a lot of opinion out there and it’s been a really impressive run. And I think it’s great that we’re going to have the opportunity here to show our audience a little bit of what they did today, some of the announcements of Avant. You’re talking power efficiency, we’re talking better performance, we’re talking AI. We’ve got an expert, Deepak, here that’s ready to show us what’s going on and show you. So why don’t we do that? Deepak, what are we looking at here at the first demo?

Deepak Boppana: Yeah. So in this demo, we’re going to measure the power consumption of the Avant FPGA, relative to similar class of FPGAs from other vendors. So this board that you see here has an REFI FPGA from Intel, Kintex-7 FPGA from AMD Xilinx, and of course Avant FPGA from Lattice. Now many of the applications for this class of FPGAs typically have frequencies in the 100 to 350 megahertz range. So what we’re going to show here is as we modulate the frequency in that range, so starting with a hundred megahertz, we can see the Avant FPGA, we have up to two and a half times lower par consumption. And as we continue to modulate the frequency throughout this range, we can see how Avant continues to have up to two and a half times lower power consumption than other FPGAs. And this really helps simplify thermal management and lowers operating costs.

Patrick Moorhead: Yeah, let’s dive, do a double click on this. First of all, I just want to say I love your demos and your marketing department. I like these so much because it’s hitting on something that a lot of embedded companies don’t do. They don’t do a very good job showing what this might mean. So I appreciate that. I want to do the double click though on thermal management and cost. Why does this equate, this benefit, equate to lower cost for your partners?

Deepak Boppana: Yeah, absolutely right. So in many of these class types of applications, especially if you think about edge computing, there’s so much intelligence moving to the edge. A lot of these devices have to do a lot more computing than before, but they have the same constraints in terms of form factor and power consumption. So they just cannot afford to have things like fans and heat sinks and stuff.

Patrick Moorhead: More to fact, there are some implementations that you can’t put a fan in and also fan, spent a little time at a real product company before, fan equals less reliability in the end.

Deepak Boppana: Absolutely. So that definitely helps from that standpoint as well. And then there are battery operated equipment too, like industrial robots for example. I mean, every milliwatt of power that you can save just helps extend the battery life longer.

Daniel Newman: Right. So Deepak, people always want to be able to correlate a technological advantage, or in this case say power efficiency to the application. So you kind of alluded to it a little bit with the edge, but talk a little bit more about the applications your partners and customers are coming to you looking to solve.

Deepak Boppana: Absolutely. So power consumption, low power consumption is so important across so many different segments. So we see that in the industrial side, for example, with some examples like machine vision cameras that again are getting smarter but still have the form factor and thermal constraints. We have industrial robotics, we have edge servers that are being placed closer to the edge. We have automotive, infotainment, kind of applications. And then of course the traditional communications applications, with 5G, base stations and small cells in the future that are really compact in size and that they can really benefit from the slow power.

Daniel Newman: Deepak, that’s terrific. Hey Pat, we got two more demos. Why don’t we go over and have Deepak show us a little bit about the performance advantages?

Patrick Moorhead: That’s great. Let’s do it. Deepak, thanks so much for showing us the power advantage demo for the new Lattice Avant, but you know, you can hit low power, but the next question is, can you hit the performance that you’re going after?

Deepak Boppana: Absolutely. So that’s what we want to be showing in this demonstration. So we’ll be measuring the performance of Avant FPGA in a high speed data transfer application. So we have the same board as before, the exact same board with the REFI FPGA, Kintex-7 FPGA and Avant FPGA.

Patrick Moorhead: So, Intel, AMD, Lattice?

Deepak Boppana: Lattice, exactly. And the channel from each of these FPGAs is connected to a different board that does the clock and data recovery and monitoring of the data transfer. So the key thing is we are going to go ahead and start the data transfer and you can see how the Avant FPGA completes the data transfer two times faster than the other FPGAs, and basically this delivers increased bandwidth and lower system costs.

Daniel Newman: Okay. So you’re dealing with power, you’re doubling performance, where are customers going to be, what are the applications they’re going to be coming to you and saying, “This is something we explicitly believe Lattice is going to be able to sell for us that these others can’t.”

Deepak Boppana: Yeah, I mean performance and even connectivity performance, which is what we are showing here, is going up in so many applications with especially all the video resolutions going up in the industrial space, the data coming from cameras and emit sensors, as well as going into the displays. So the video related traffic is going up, the speeds are going up, and then obviously on the side we have the usual networking and 5G data rates that are basically going up as well. So this faster connectivity helps in all those applications.

Patrick Moorhead: So, let’s say something in networking, it would be the difference between, it would literally be the line speed of that network connectivity. And then for other applications it could be the difference between either let’s say the number of cameras coming into the system or even the resolution of the video coming into the subsystem itself.

Deepak Boppana: Exactly.

Patrick Moorhead: So automotive, robotics and pretty much anything that connects via network, which is pretty much everything.

Deepak Boppana: Absolutely right. And we touched on this before with the whole intelligence aspect. All these devices are getting smarter and there’s so much connectivity in the automotive space, connectivity within the car, the industrial networking space with ethernet and TSN protocols.

Daniel Newman: Sounds like you’ve got speed, you’ve got power. One of the big applications everyone’s asking about now though is AI.

Patrick Moorhead: That’s right.

Daniel Newman: So, I heard you’ve got something to show there. How about we head over and you give us…

Deepak Boppana: Absolutely.

Patrick Moorhead: Let’s do it.

Daniel Newman: Deepak Artificial intelligence is a really big topic this year. Patrick and I are constantly covering it, talking about it and everyone’s asking in the FPGA space with this Avant launch, AI’s got to be a part of your story. Tell us about that.

Deepak Boppana: Absolutely. So we have a really cool demo here where we’ll be showcasing the Avant FPGA based AI inferencing solution and this performs real time analytics on a video stream and we show vehicles operating under different traffic conditions and we’ll see how the solution performs in terms of detecting and tracking the vehicles. So let’s go ahead and play the first video clip. So here we see a bunch of slow-moving vehicles and AI solution, as you can see, identifies the vehicles with the green box around them and tracks the movement of the vehicles. Now in the second clip, the conditions are much harsher with fog, snow, faster oncoming traffic, but the AI solution is still able to keep pace with all that and continues to track the movement. Now this last clip has extremely fast moving traffic and the AI solution is still able to identify and track the different cars on the road. So this is really an example of how Avant can deliver scalable AI performance for a variety of different edge computing use cases.

Patrick Moorhead: Now, this is great. And I particularly love that last one because it was a good, did you send that video from how you drive Dan in Texas?

Daniel Newman: The third one. Just the last one.

Patrick Moorhead: I got you. Okay. This is good. So AI is definitely more about automotive, but I think it’s something we can all relate to. How about smart factories, smart distribution, other types of applications?

Deepak Boppana: Absolutely right. So AI is being used in so many different industrial applications. You mentioned smart factory, robotics, both the fixed and mobile robots that need to move around and basically do object detection and collision avoidance and stuff like that. You have machine vision cameras that basically do the quality inspection. So that’s a use case for AI and the typical surveillance cameras as well. And this could also be an example of a traffic management system.

Patrick Moorhead: Right. Hey, let’s get another smart city, right?

Deepak Boppana: Yeah. That’s the smart city, yeah.

Patrick Moorhead: So smart retail, smart factory, smart distribution, pretty much intelligence anytime you have a camera that you want to pull into intelligence.

Deepak Boppana: Exactly.

Patrick Moorhead: Okay.

Deepak Boppana: So yeah.

Daniel Newman: So basically, anywhere that inferencing needs to be done at the edge, Avant is there.

Deepak Boppana: Absolutely.

Daniel Newman: Anytime there’s a launch, it’s always about generation to generation. This is the launch of Avant, but Nexus to Avant, talk just a little bit about what are some of the improvements of performance that people can really expect in terms of AI.

Deepak Boppana: Absolutely. So yeah, I think spot on, even with Nexus, we have very good success in traction with AI use cases. In fact, we are building on that with Avant. So the way to think about it is, I mean even if you take this particular example, the Nexus products will probably do a good job of the first slower moving traffic and the second one, but it’s really use cases where you need faster frames per second processing or higher quality resolution. That’s where Avant really adds to what we already have. And the beauty is the FPGAs, both are programmable and as you know, the AI world is still evolving, with all the networks and models still changing. So both of them have the basic value proposition of FPGA programmability.

Patrick Moorhead: Yeah. So Deepak really appreciate you taking us through these demos of the new Avant product. We looked at power, we looked at performance, we also looked at AI performance. So I appreciate you taking us through that.

Deepak Boppana: Oh, my pleasure, thank you.

Patrick Moorhead: So, it was a great launch here. We are live here at the Lattice Avant launch event at the Computer History Museum in Silicon Valley. This is rocking it. Great demos, great presentations. Tune in to check out some of our other videos on the event we are tuning out here. I want to thank you for tuning in. If you like what you heard, hit that subscribe button. Take care.

The post The Six Five In the Booth: Demo Showcase at the Lattice Avant launch event appeared first on Moor Insights & Strategy.

• Announcement of mid-range FPGA platform, Lattice Avant
• Lattice’s investment in customer experience
• Lattice’s competitive edge/product differentiation
• Investor outlook after this announcement

Be sure to subscribe to The Six Five Webcast so you never miss an episode.

You can watch the full video here:

You can listen to the conversation here:

Disclaimer: The Six Five On the Road is for information and entertainment purposes only. Over the course of this webcast, we may talk about companies that are publicly traded, and we may even reference that fact and their equity share price, but please do not take anything that we say as a recommendation about what you should do with your investment dollars. We are not investment advisors, and we do not ask that you treat us as such.

Transcript:

Patrick Moorhead: Hi, this is Pat Moorhead, and we are live with the Six Five at the Lattice Semiconductor Avant Launch event in Silicon Valley at the Computer History Museum. It is rocking here. We love good tech and we love even more talking to the most influential people in the industry. Awesome stuff. Dan, how are you?

Daniel Newman: Doing well, always good to be here in the Silicon Valley, especially when we’re talking about silicon. And today was a big day for Lattice Semiconductor. It’s great to have him here on the Six Five on the road. Pat, we’ve got a lineup of the stars here from the company. You want to go and introduce him to the show? Yeah,

Patrick Moorhead: Absolutely. So Jim, Steve, Esam, welcome to the show and congratulations on the big event. Y’all know I love product launches and in my role I get to do about a 150 a year.

Jim Anderson: So do we.

Patrick Moorhead: But congrats.

Jim Anderson: I’m jealous.

Patrick Moorhead: No, it is. Oh, we can sign you up. Just we’ll talk after the show. No, but seriously, congratulations on a big launch. It’s been years in the making and it is fun. I admit as an analyst, seeing the front end and the planning and where you’ve taken the company, Jim and your team, and now we are here today, but I know you like it’s not time to even, let’s pat ourselves on the back for five minutes and move on to make this thing even bigger a success.

Jim Anderson: For sure. We are definitely even working on the next big thing. But yeah, today is definitely a big day for us, a major product announcement for us. And that’s the launch of Lattice Avant. And what that does for the company is it doubles our addressable market. So if you look over the history of the company and Lattice will be 40 years old next year. We’re all a little bit older than 40-

Steve Douglass: Some more than others.

Patrick Moorhead: I don’t know how many in some more than others crowd.

Jim Anderson: But Lattice, if you look back over that 40-year history, this is hands down the biggest product expansion in the company’s history. So we’re super excited about that, but more excited about what that means for our customers because for our customers, what that means is we’re bringing just a tremendous amount of innovation and product differentiation to our customers. Lattice has historically always been really strong in small FPGAs and now we’re taking all of that strength and we’re bringing it to mid-range FPGAs and our customers are super excited about that. Very excited about the competitive differentiation that we’re bringing.

Daniel Newman: We start out the morning, we got a chance to get a little sneak preview. Can’t say too much about that because we didn’t want to get out ahead of everyone, but it was really interesting.

Patrick Moorhead: Well, maybe we did. You asked us not to.

Daniel Newman: We did. We wanted to. We didn’t do it. But it’s always good. I like getting around these kinds of launches because frankly we just need to see innovation continue to be pushed and Lattice over the last several quarters. I think both of us have written our pieces about market outperform, consistently growing, being in the right market spaces. I love that you’ve had such low exposure to the consumer because of course as this kind of market is rotated over, some of those bets you made in communications and in automotive and in the edge in 5G have really started to pay off big. And so congrats on the announcement. By the way, that moment when you were sitting there, I think you may have even had your shoes off, Jim, and you were just meditating, like an hour before the launch.

Jim Anderson: You’re not supposed to say that, Daniel.

Patrick Moorhead: He’s a real people’s person.

Daniel Newman: Like I said to you, I said, you look like you’re in the zone and you’re ready to go. And you were taking that one minute and then you came up on stage, you had that perfect moment, holding the chip. And I even tweeted, I’m like, that’s that CEO moment where-

Jim Anderson: That was fun. Yeah, it’s tiny.

Patrick Moorhead: The challenge though is you have a chip that small.

Jim Anderson: I think I’ve got it here.

Patrick Moorhead: It’s like, get this picture. Well, where is it?

Daniel Newman: I zoomed in.

Jim Anderson: Well then, I got to make sure that I… Yeah, this is the chip that is the platform. But yeah, it’s also small, but I had to make sure I had the writing up on the right direction too.

Daniel Newman: But yeah, you’re like-

Jim Anderson: Yeah, very fun to show that.

Patrick Moorhead: So, Jim, it obviously takes a village and a team and you’re the coach of the team, but there’s a lot of deep tech that goes into that. And I’m curious, Steve, you can double click and listen, I know you love all of your technology babies, but maybe talk about some of the highlights of the innovation, the investments that you’ve made in terms of people and IP to get to Avant.

Steve Douglass: So, Avant has really been an exciting program for us and it really has been a focal point for adding new innovations. And as you mentioned, adding new people and having talent to be able to scale our technology to that next level. When you look at the types of solutions that we are providing and the problems we’re solving with mid-range compared to the small end of the market, there really is a lot more new technologies that you need to have. And it really scales faster when you can have your core team leverage and catapult from bringing in talent that is expert in these other areas.

Steve Douglass: And when you look at the types of innovations our customers needed for their next gen products, it wasn’t just in the hardware, it was in the software as well. And so we had a lot of innovations on both fronts. Our hallmark innovation has always been power efficiency. We’ve always been really good at cramming in a lot of technology and a small footprint with very low power. But it was also very important as we scaled up to the mid-range, we significantly increased the amount of bandwidth that they have to get data in and off the chip and scale the compute capabilities as well. So those were some really key focus areas for us when we were scaling our technology to the next level-

Patrick Moorhead: What did you do on the AI side? I mean was it a 6x increase in performance of AI or maybe it was even more, correct me if I’m wrong.

Jim Anderson: 30x.

Steve Douglass: Well, we have 30x compared to our prior generation.

Patrick Moorhead: Oh my gosh, okay, there we go.

Daniel Newman: So, six times five.

Patrick Moorhead: Exactly. No, thank you very much. No, but how did you implement that? Was this classic new design more LUTs, smaller form factor or was there some secret sauce in there that enabled you to do it? You can’t do 30X with scaling.

Steve Douglass: No, it doesn’t just happen if I go into a new technology.

Patrick Moorhead: So, if you can share a little bit of that secret sauce, it’d be great.

Steve Douglass: So, it was really a blend of factors and first having more logic sales, that’s a big factor. So we’re five times bigger, so that was a decent chunk of it, but the fabrics running 50% faster than the prior generation too. So that gives you another kicker on top of that. And then we further increased and scaled amount of DSP and embedded memory that we have in the core with all that logic. And each of the DSPs can be further decomposed into smaller compute elements. And we have dedicated logic for implementing some of those AI inferencing functions. It’s all about matrix multiply, guys. And if you’ve got the math engine that can compute those things efficiently, you’re going to get a lot more scale and performance out of that.

Patrick Moorhead: Do customers have to toss out their software investment in Nexus to be able to take advantage of what you’re bringing the table?

Steve Douglass: Absolutely not. We are going to have the exact same development tool flows supporting Avant that are already supporting Nexus today. So our Radiant software, our Propel embedded tools, they’re all leveraging Avant architecture.

Daniel Newman: So, I want to spend just a second, maybe Jim, maybe Steve, but software seemed to be a highlight of the show. Obviously when you talk to semis, we talk chips, people tend to always want to drive to the hardware, to the physical, the wafer. And it seems like a lot of your innovations coming out of leading and leading into software, Jim.

Jim Anderson: Yeah, we definitely try to emphasize that today because it’s been a very big part of our strategy. So software for us, the key part of our strategy is about using software to make it very, very easy for customers to adopt our Lattice solutions and get to market quickly because we want customers to be able to easily take advantage of all the great innovation that Steve and the team are putting into the silicon. We want that to be very easy. And so from day one of the avant development, all of our software thinking has been how do we make it really easy for customers to adopt Avant.

Patrick Moorhead: Customers have a lot of choices out there. I mean, they can go all the way from a controller to a GPU to an NPU to an APU, to an ASIC and everything in between. And I know DSP fits in there somewhere, but how do customers decide which direction they go? It is pretty amazing. I look at some of the capabilities that you put up today, particularly in the AI part. I’ve seen at other companies who basically doing different types of chips than you are. Maybe, Esam, we could start with you on that one.

Esam Elashmawi: Yeah. And before that, I just want to comment on what Steve talked about, the innovation from the Lattice development team and just the consistent pace of execution. And when we started this, we spent time with over a hundred customers and you can imagine… I go visit the customers, I come back, Steve will ask me, so what do you learn from the customers? I’m like, Hey Steve, you just got to make it more capable, significantly lower power, much smaller, and it has to leverage all of our tools.

Patrick Moorhead: Did you have to talk-

Steve Douglass: That’s when I said let me talk to the customer, I’ll get the real scoop.

Patrick Moorhead: Did you actually have to talk to customers to get that?

Daniel Newman: Does that feel like the Office Space moment? I take the information from the customer and I bring it down.

Patrick Moorhead: Then why can’t we just directly talk to the customer.

Daniel Newman: The engineers can’t talk to the customer.

Esam Elashmawi: But tremendous work by our development teams to bring out the Avant platform. And to your question about how to decide what to use, there’s a lot of factors, but they’re engineers, they want technology. They want a solution that makes it easier for them to get to market. They want to innovate on their products, they want their end products to be differentiated. And having a flexible programmable FPGA with these innovative capabilities around power efficiency, meeting their performance and application needs, and having that tiny physical size that Jim just showed you that makes a big difference for them. It helps them innovate, get to market faster, and differentiate on their own products.

Patrick Moorhead: It seemed like the industry used to be a lot simpler and in semiconductors it was performance, power, and area. PPA. That was it, baby. But really we’ve gotten to more of the experience, which is how do I make the connection between the customer and even the customer’s customer. And experience no longer is a bag, just a bag of parts, going to suffice. So I’m curious Esam, how are you enabling new experiences? We’ve kind of tiptoed around that a little bit, but I want to hear from the guy who spends a lot of time with customers enabling this and how are you different? I mean at the end, how are you different in enabling their killer experiences?

Esam Elashmawi: And there’s a few things that we do. Number one, customer intimacy is really, really important. But like you talked about you have to understand the customer’s customer as well. And one of the things that we’ve done within Lattice is actually we’ve brought in people that worked from our customers that know the end product. I’ll give you a good example. When you look at our compute market where we do server and client type applications, we actually have folks within our marketing team, we call them marketects that actually came from our end customers. So they know the challenges, they know what they’re trying to do, they can take… And we’ve got all the FPGA experience within Lattice, but that helps close the bridge because now you’ve got open conversations within the company that we can go speak their language. So now we’re using the same terminology as they’re using or that their end customers are using. And that really helps, not just from the customer intimacy and building the trust, but it helps us identify new ways to leverage this flexible programmable FPGA to build those new sets of applications.

Daniel Newman: Just building on that, Esam, one of the things that I know from doing so many events that we always look for are those customers. And you guys kind of broke out customers, IP partners, OEMs, and that slide was impressive. And I said, I wish you’d have shown it generation to generation. Because I think over the last few generations that slide went from very few to very full and getting them to show up and talk though we constantly are talking to customers, let us talk to your customers off the record, let us take them on the record. And that’s hard. You guys had a nice showing of customers today. What do you attribute the fact that they’re so willing to step up and be vocally supported?

Esam Elashmawi: And as you noticed as well, those customers… There were more videos, we just didn’t want to show them all because at length of time. But if you noticed the titles of those individuals, they were all decision makers and engineers and there are people really in the development cycle themselves. But it really goes back to again, the customer intimacy. The need to have and work with an FPGA supplier that’s willing to build a roadmap aligned with their needs. And that customer intimacy and us being able to work closer with the customers, I think that’s a testament of how we’ve made it easier for them to adopt our solutions, both hardware and software as well. And kudos to the sales team. I mean the sales team does an outstanding job in reaching out and building that intimacy.

Patrick Moorhead: Would you say customer proximity is your biggest differentiator as a company or is it the core tech? I’m going to make you decide here, Jim, is it…

Esam Elashmawi: We always pass on the tough questions.

Patrick Moorhead: Well, and I’m pretty sure I know the answer is going to be it’s amalgamation of all. But a lot of questions I get, Jim, from your customers or ecosystem and even investors are, why are you different? How are you able to do this? Because it’s not that often you see a lower left, up to the right on almost every metric, including things like, it means a lot to me, product velocity. It’s one thing to… Listen, and the financials are important, but there’s also partners, there’s stickiness, there’s them coming back to you for more and doing more with you. There’s the basket across product. There’s so many metrics and all those look really good to me.

Jim Anderson: I think of course you have to have a great product roadmap and that’s certainly what we’ve been building. But I think culture is important to customers as well. I’ve had a lot of customers talk to us, talk to me about, hey, the culture of Lattice, when we interact with Lattice, it’s different and we appreciate that.

Patrick Moorhead: How so?

Jim Anderson: From when I first joined Lattice, I noticed this, it’s a very collaborative, customer focused culture. Every Lattice employee loves supporting and interacting with customers. And when I first joined the company, I said, hey, I think this is a real fundamental differentiator for the company. And I think that that’s not the case across all companies, but I think that that can be a real differentiator for us. And it’s a basic DNA that’s within Lattice, which I think has been cultivated over our 40-year history. And so when I joined, I really wanted to encourage that. Like Let’s absolutely be even better about that. Let’s do even more around that. And so I think yes, of course we have to build great products for our customers and we have to innovate for them. But I think taking it to the next level with a customer is having a culture that really embraces the customer and wants to bring them in and form a relationship that’s not just about one design win or two design win, but it’s a multi-generational-

Patrick Moorhead: It’s more strategic.

Jim Anderson: Absolutely. Okay. Absolutely.

Daniel Newman: Okay. So Jim, the ground truth as Pat and I always like to say when we’re on our earnings podcast is always what you guys come out with when you share results. But the investor that are here, several were here, saw their badges walking around, and so while it wasn’t your investor day, that’ll be later, I believe in ’23. The investors tend to be the, what you say, the arbiter of how good a product is. And they basically… It’s how the company performs in the market over a long time. We’ll just say that. Maybe we’ll agree, maybe we won’t. But it tends to be companies that perform well.

Patrick Moorhead: If anybody’s listens to the podcast, Daniel and I look at it a little bit differently, but-

Daniel Newman: We like to talk about it. We call it the ground truth. That we really do. So you know said you’re going to double the SAM. That’s obviously in itself a really big opportunity for investors. But how do you think the investor community should be looking at this announcement? Is there anything more to it than just that double the SAM opportunity?

Jim Anderson: I think, it’s for investors, the really simple way to look at it, which I think is the right way to look at it, is the products that we announced today, the platform that we announced today creates a totally new revenue stream for the company. A totally greenfield brand new revenue stream that is completely additive to the company’s existing revenue stream. We’re certainly focused on continuing to grow in the part of the market that Lattice has always been traditionally strong in, which is small FPGAs. So we’re certainly focused on continuing to grow there, but Avant as it enters production and ramps with our customers, that creates a completely additive revenue stream that we believe will accelerate revenue growth out in time for the company. And as an investor, that’s kind of music to my ears. Additive greenfield revenue growth on top of the existing business. And it’s a very straightforward extension of our existing business. When we look at the target customers of Avant, 90% of those target customers are already customers today. So it’s a natural extension of our existing business. I think it’s about revenue acceleration. That’s what’s exciting for the investors.

Daniel Newman: Do you have a sense of revenue expansion? You said 90% of them are out there. Do you have a sense of how quickly they’ll adopt? I mean, I saw the chart, so a lot of them are saying yes.

Jim Anderson: Definitely, but we got to leave a little bit for the investor day next year. Daniel, come on. I mean-

Daniel Newman: Leading the witness, sir.

Patrick Moorhead: Jim, I think final question on the investor part. Is there any play between kind of lowest power and the mid-range, meaning if you’re in mid-range and it’s good experience, does it help you in the current business at all?

Jim Anderson: Definitely. I think when you look at, especially industrial and automotive customers, but I think this is also true with communications customers as well. But the majority of the types of FPGAs that an industrial and automotive customer uses is actually mostly mid-range and small FPGAs. That’s the majority of what they use. And so now with this product line expansion, Lattice is able to service almost all of the FPGA needs of the vast majority of our customer base. And so that certainly helps us.

Patrick Moorhead: Okay.

Daniel Newman: Well, Jim, Steve, Esam, I want to thank you all for joining us here on the Six Five big day for you all at the Lattice Avant launch. And we really appreciate y’all joining us here and we look forward to having you on our show again sometime soon.

Jim Anderson: All right. Great.

Steve Douglass: Thanks for having us, guys.

Esam Elashmawi: Appreciate it.

Daniel Newman: All right everybody, thanks for tuning in here to the Six Five on the road at the Lattice Avant launch, at the Computer History Museum, in Mountain View, in the Silicon Valley. For Patrick and myself, hit that subscribe button if you enjoyed what you heard. We would love to have you for all our episodes, but we got to go. So we’ll see you later. Bye-bye now.

The post The Six Five On the Road: Executive panel at the Lattice Avant launch event appeared first on Moor Insights & Strategy.

• Evolution of the MongoDB & AWS partnership
• How customers are utilizing MongoDB Atlas on AWS to build cutting-edge applications
• Data-driven approach to the cloud
• Cloud strategy in a hybrid world

Be sure to subscribe to The Six Five Webcast so you never miss an episode.

You can watch the full video here:

You can listen to the conversation here:

Disclaimer: The Six Five On the Road is for information and entertainment purposes only. Over the course of this webcast, we may talk about companies that are publicly traded, and we may even reference that fact and their equity share price, but please do not take anything that we say as a recommendation about what you should do with your investment dollars. We are not investment advisors, and we do not ask that you treat us as such.

Transcript:

Patrick Moorhead: Hi. This is Pat Moorhead, and we are live at AWS re:Invent 2022 in the MongoDB experience. It is rocking, as you can hear. It’s early morning, and people are actually awake here in Las Vegas.

Daniel Newman: Yeah. I always give them a lot of credit. They get up, they get out of bed. It doesn’t really matter what time of day or night it is here. It’s always daytime, and they don’t let us out of the casinos for just that reason. But AWS re:Invent, always a rocking event. Very excited to be here in that MongoDB experience. We’re having breakfast, drinking coffee, and talking about data.

Patrick Moorhead: No. I know. It’s great. Data, as we’ve talked this entire week, is a huge enabler and a huge game changer to the enterprises who know how to deal with that. Hybrid cloud, multi-cloud, huge topics here. Why don’t we introduce our guest here?

Daniel Newman: Absolutely. Andrew Davidson, how are you? Welcome to the show.

Andrew Davidson: Great to be here, guys. Yeah. I’m Andrew Davidson, SVP of product at MongoDB. Love being back in Vegas. I’ve had my coffees. I needed my coffee in the room to get out of the room, and now I’m getting more down here with you guys.

Daniel Newman: Absolutely. When we’re done, we’ll have one more.

Andrew Davidson: There you go.

Daniel Newman: Enjoy the opportunity to sit down here. We know it’s a fast-paced event.

Andrew Davidson: Absolutely.

Daniel Newman: Let’s start off in the macros. What’s going on in the developer space, the demand around data, the need for your services, MongoDB? And maybe even just a little quick background around MongoDB itself.

Andrew Davidson: Yeah. Before I dive into MongoDB, I think it’s almost useful to demystify this broader topic of data. People talk about data being the new oil. Everyone has this intuitive understanding of, “Hey. I can answer questions from data. That’s got to be valuable.” But there’s something more to this data ecosystem that gets lost. It’s this magical hidden layer, and it’s what we call operational data. You could think of operational data as almost the lubricant of the digital economy. It’s what enables all these software layers and these businesses to interact in real time.

You think about, if I’m buying tickets to fly to Vegas, all of us doing that at the same time, tens of thousands of people, I’m on some flight search engine, I’m seeing results in real time, I’m seeing which prices exist, which tickets are available, how many seats are left. To enable all that, there’s all these layers of software that make that possible, and each of those layers of software, the ticketing engine, the search engine, the airline, they all have their own software with operational data in the mix.

Sometimes, we call this operational data transactional data. That’s where MongoDB’s sweet spot is. That’s where we enable this shift to the software of the digital economy in all these kinds of companies that exist all on top of each other, hidden away. But I like to demystify that because it isn’t the same as answering questions from data, though that’s also a part too. That’s important as well, downstream.

Patrick Moorhead: Yeah. In fact, that type of data you have to treat differently because, essentially, the antithesis of the operational data is data that might make the experience better in a way, downstream, but if you don’t get the operational data right, your business has stopped.

Andrew Davidson: That’s exactly right. You think about, if I’m an executive, a CTO today, what am I trying to do? I’m trying to make sure that my business can respond to threats, take advantage of new opportunities. That means that I have to be operating as a software company, bringing in elite architects and software engineers and retaining them and making them productive. To do that, I have to structure myself in a way where I’m building software. I can’t just buy that off the shelf anymore.

If you look at where these developers spend most of their time, these software engineers, it’s really wrestling with this data problem. This is the part of the stack that, going back decades now, it’s been slow moving and tough to deal with. MongoDB comes in with this new approach, an elegant, modern model that enables those software engineers to just feel natural, and MongoDB gives this super set of workloads behind the scenes to drive the vast majority of the applications needs for the vast majority of applications. That standardization that comes about for this operational data layer, it’s a game changer for people.

Patrick Moorhead: It is important. We’re here at AWS re:Invent. Obviously, things are going on between MongoDB and AWS?

Andrew Davidson: Oh, yeah.

Patrick Moorhead: You brought out Atlas, big play there. Can you talk a little bit about where the relationship is, maybe a little bit about Atlas, and what customers, how they’re optimizing it, using it for their benefit?

Andrew Davidson: Totally. Totally. Yeah. MongoDB Atlas is our global database service on AWS. We conceptualize it as a developer data platform. If you think about our relationship with AWS, it goes back to the very beginning in that origin moment of AWS when IaaS-

Patrick Moorhead: That’s very unique, by the way.

Andrew Davidson: Very unique.

Patrick Moorhead: We’ve talked to a lot of partners who literally just started working with AWS in the last two years.

Andrew Davidson: I know. It’s amazing because, in that origin moment of IaaS, when we used to think about it as … I remember when Google and Amazon started to realize, “Hey. Commodity hardware. I can change everything,” and Amazon just pioneered this game-changing industry of selling that commodity hardware as a service. In that moment-

Patrick Moorhead: 15 years ago, by the way.

Andrew Davidson: In that moment, a bunch of virtual machines weren’t going to be that useful if you didn’t have software that could give it the distributed system that made it relevant for building modern software. That’s where MongoDB comes in, this elegant model, this distributed system that basically allows developers to build incredible game-changing apps. Then, you see the rise of the mobile revolution, levels of scale never before seemed possible, and MongoDB just rode that wave on AWS.

But as our customers demanded more from us, more mission criticality, transactional capabilities that go deeper and deeper, richer global and geospatial capabilities to ride that mobile wave, eventually we realized, “We’ve got to go up a level of abstraction. Nobody wants to be managing virtual machines and all this low-level plumbing anymore.”

Patrick Moorhead: Especially when all the infrastructure is all over the place. Your own data center, colo, on the edge, and like you said, even now on mobile devices, when you’re looking at containers, it’s everywhere, and there’s so many different public clouds out there too.

Daniel Newman: Absolutely. It’s interesting what you said too about the layers of abstraction. We’ve been talking to AWS folks about things like their container services and how you’ve got all the levels of totally doing it yourself to wanting that entire App Runner experience. It sounds like, with what you’re doing, you’re really trying to say, “Hey. If you want to focus on developing, you want a platform that you can focus on building the best apps possible.” This is where Atlas-

Andrew Davidson: You’re hitting the nail on the head. Yeah. MongoDB Atlas says, “Hey. Instead of me having to think about all this plumbing, I should have a truly declarative model where I just say, “I want this database cluster anywhere in the world, over a hundred regions, all three cloud providers.”” Of course, AWS is where we’ve been the longest, and it’s the center of so much gravity. You can build so many great things on it. To be able to just have that developer data platform give you the back end of your app so you can use those with Lambda and App Runner and all the great AWS application tier services, services that specialize in stateless applications that interact with the stateful layer, the data portion in MongoDB Atlas. It’s two peas in a pod.

Daniel Newman: You talk about data developer platform.

Andrew Davidson: Developer platform. Yeah.

Daniel Newman: You keep saying it.

Andrew Davidson: Yeah.

Daniel Newman: Why are you guys leaning in so hard on that?

Andrew Davidson: Sure. If you think about going back to that CTO, they’re bringing in these elite architecture software engineers to build those truly transformational parts of their business.

Let’s think about some examples. I mentioned the airline ticketing before. That has to be an amazing experience. If I’m using Expedia, it needs to be just right in my face, everything I need, and it’s so competitive building that customer competitive experience. Making it the best experience possible, that requires you to focus on the customer, not on the plumbing. Having this layer that allows the developer to move super fast and operate with agility so that they can focus on all this white space that gets opened up, product experience, developer experience, the features in the application, it’s a game changer.

So many other kinds of customers build on this platform. Epic Games building the back end of Fortnite, the most popular game in the world. Companies like Canva disrupting the graphic design ecosystem in an amazing SaaS experience. The unsung heroes of the pandemic, government customers who recognize the need to modernize. I think about the UK Government Department of Work and Pensions who had chosen to modernize and move to this model of agile modern development teams. They did that before the pandemic, perfectly timed on MongoDB Atlas to enable 1.2 million claims to be processed every day, keeping the lifeblood of the economy moving through that tough time.

Patrick Moorhead: Andrew, I love your ability to go very customer-centric, talking about the true business benefits all the way down into the guts of the tech. I think that’s important, and I think it’s one of the characteristics we’ve seen of truly successful technology companies.

What I’d love to do now is maybe do the double click on two of one of the most impactful trends out there today, and that’s hybrid and multi-cloud. First off, I can’t even believe now there was a debate on whether the future was … By the way, check my white papers 10 years ago.

Andrew Davidson: There you go.

Patrick Moorhead: We called it. Okay. We were called cloud deniers, but it’s like, “No, no, no. This is not the way it’s going to work,” and here we are in multi-cloud. But everybody agrees, including AWS, the world is going to be hybrid. Now, the entire industry, there’s a little bit of question on how we’re going to get to multi-cloud, but the fact is I have yet to talk to a Fortune 1000 CIO who doesn’t have multiple public clouds. It’s here. It’s just not very efficient. How is MongoDB optimizing the experience for its customers on both hybrid and multi-cloud?

Andrew Davidson: That’s a great question. I think MongoDB is truly unique in this space because folks have been able to, before they went to the public cloud, take advantage of what MongoDB brought to bear in the on-prem context, which was scratching a lot of that same itch, giving those software engineers the ability to move fast, be agile, be cloud ready, and to be able to shift into the public cloud in almost a migrationless way. Imagine not having to rewrite your application in any way once you’re ready to move in. We can synchronize that data straight up to MongoDB Atlas the second they’re ready.

What I’ve seen is it doesn’t really matter when your cloud journey’s going to begin. Obviously, now, we see tens of thousands of people here. Everyone’s doing it, but pockets and critical use cases staying close to systems of record and the mainframe, those are all modernizing on prem. That’s okay, but they’re all going to be cloud ready if they start on MongoDB in that migrationless model. Then, once they’re in the public cloud with MongoDB Atlas, they can truly pick and choose from the best cloud provider for their needs.

Patrick Moorhead: Okay. Just a clarifying question. Regardless of the platform, and I saw what you did with IBM, and incredible work, whether it’s on prem, mainframe, x86 server, ARM server, whether it’s singular experience for the developer, whether it’s public cloud or on prem.

Andrew Davidson: Yeah. It’s truly a game changer to be able to lean into creating a modern standard. Think about it. For decades, folks have thought about data through the lens of tables, this model that feels like a big spreadsheet. We all get that. But that model is not how developers think and code today. That’s not how you bring in those elite software architects.

If you want to be able to upgrade that mindset, how do you light this fire and get millions of developers around the world to care about it? That’s where we have to focus. How do we enable all these folks, whether they’ve been in the industry for decades or they’re that next generation of developer in academia today, how do you invest in making millions of them able to understand it? You have to be everywhere. You have to be in every platform, and you have to be investing in broad-based, best-in-class documentation.

Our MongoDB University platform, literally, it’s like an online MOOC, massively online course, for MongoDB. We’ve got hundreds of thousands people signing up for that every month, and MongoDB Atlas gets 150,000 sign-ups every single month, people all over the world. It just shows that you can feel this movement as the change happens.

Daniel Newman: We only have a few minutes left, but I am curious. Nothing is as easy as … We’re great. We’re analysts telling the story of, “Hey. Everything to the cloud is easy,” the easy button, and you’re making it … Obviously, these clients are buying in. They’re working with you. They’re saying, “Hey. We see it. We get it.” But what are the challenges of those that have been building with Mongo for, say, a long time on prem moving to the cloud? Has it been fairly easy to overcome because of this transitional technology that you’re building, or are they still running into challenges, and how are you overcoming that?

Andrew Davidson: That’s a great question. Let’s be honest. Nothing’s easy. That’s why you need these elite software architects and software engineers to build what really makes you a software company, and everyone’s struggling with this challenge. But I would juxtapose it against the alternative model, is one in which … What we’ve seen people over the decades do is they’ll have to layer in a different engine, a different system for every little part of their software, and this leads to a massive sprawl of complexity and governance debt and different interfaces, and every app, every environment, it’s totally different.

What we’ve seen is, with this idea of a developer data platform that can do the vast majority of the needs for the vast majority of your applications, what that means is, if you take a big step back, you’re a CTO of a global enterprise, I’m going to have hundreds of different teams, two-pizza teams, probably, right, each operating with their own agility. But if I can have all of them operate on a standardized model rather than each of them doing this crazy chaos that I’m going to be super worried about in a couple of years when I realize I can’t govern this anymore, if all of those hundreds of two-pizza teams are operating on a modern developer data platform and taking advantage of their own customization to allow themselves to move fast, not saying everyone needs to be just shackled, but allowing everyone to do their own thing with standardization, that’s the need of the moment. We’re seeing that trend in partnership with AWS left and right. All the focus they’re putting into governance, we’re right there, front and center, in that shift.

Patrick Moorhead: That’s good. Andrew, it’s been a great conversation, and I can’t believe how much we got to accomplish in the booth here.

Andrew Davidson: I know.

Patrick Moorhead: But the great news, we’re all fast talkers, and we actually know what we’re talking about.

Andrew Davidson: That’s right.

Patrick Moorhead: You put those combinations together, and it ends up working. I just want to thank you for coming on The Six Five. This is your first time on the show. I think it is.

Andrew Davidson: First time on the show. Yeah, yeah, yeah.

Patrick Moorhead: Yeah. No. This is great.

Andrew Davidson: Great to meet up with you guys in person.

Daniel Newman: It’s not going to be the last.

Patrick Moorhead: No. We’d love to have you on again. Really appreciate the time.

Andrew Davidson: I look forward to it. Gentlemen, thank you so much for having me. Let’s go grab a coffee at the bar behind us.

Daniel Newman: Let’s do it. Let’s do it. All right, everybody out there. Thank you so much for tuning into this Six Five in the booth at the experience here at AWS re:Invent 2022 for MongoDB. We appreciate you tuning in. Check out all the videos that Patrick and I did here at AWS re:Invent 2022. Great show. Great event. Hit that subscribe button. But we got to go.

Patrick Moorhead: We’re out of here.

Daniel Newman: See you later.

Andrew Davidson: Thank you so much.

The post The Six Five On the Road at AWS re:Invent 2022 w/ Andrew Davidson, SVP, Product Management, MongoDB appeared first on Moor Insights & Strategy.

• EC2 and silicon innovation including an overview of the news that was announced this week
• Continued investment in Intel-based and AMD-based instance types
• AWS Compute wherever it is needed

Be sure to subscribe to The Six Five Webcast so you never miss an episode.

You can watch the full video here:

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Disclaimer: The Six Five On the Road is for information and entertainment purposes only. Over the course of this webcast, we may talk about companies that are publicly traded, and we may even reference that fact and their equity share price, but please do not take anything that we say as a recommendation about what you should do with your investment dollars. We are not investment advisors, and we do not ask that you treat us as such.

Transcript:

Patrick Moorhead: Hi, this is Pat Moorhead and we are live in Las Vegas at AWS re:Invent 2022. The Six Five is here. We’re having incredible conversations about incredible technology, with great people. Dan, how are you?

Daniel Newman: I think we’re having incredible conversations with incredible people about very capable technology.

Patrick Moorhead: Gosh, I know. One day I’ll get it right. I’m glad this is not the last, oh, it is the last interview. How about that?

Daniel Newman: It is, but the people watching may or may not know that.

Patrick Moorhead: Yeah.

Daniel Newman: Because we did a lot of these and they were all really good and because we’re The Six Five, people watched this stuff all the time. It’s not just live at the event. But Pat, what a great AWS re:Invent it has been.

Patrick Moorhead: No, totally. I have to bring in the maturity of the cloud. The cloud’s 15 years old and it’s walking, it’s getting into a little bit of trouble. Everybody hasn’t figured it out yet, isn’t a full adult yet. But isn’t it amazing we’re like 10% there? At least that’s what Andy Jassy said on CNBC. But we create so much. By the way, Compute has been one of the game changers that has made this happen. I have to be a little bit biased. I love compute.

Daniel Newman: You’re a silicon guy?

Patrick Moorhead: I once was, in a previous life, silicon guy. And then even before that I was the OEM buying this stuff. So selecting it for my products. But I love chips. And by the way, we need to get to introduce our guest. Gadi, how are you my friend?

Gadi Hutt: I’m doing great. Thanks for having me. Super excited to be here.

Patrick Moorhead: I know. It’s so exciting. We’ve had folks, we have talked on The Six Five with Dave Brown, I think twice on The Six Five, did The Six Five summit. And those were some of our highest viewed episodes. People love chips. Maybe a great place to start is what do you do for AWS?

Gadi Hutt: Yeah. Nice to meet you guys. I’m running the business for an Annapurna Labs. Annapurna Labs is the company that AWS acquired in 2015. And we are focused on serving AWS customers across network, storage, compute, and machine learning.

Patrick Moorhead: Huge success story. I remember seeing the press release when I think it was Amazon bought it, not Nest. And I’m thinking, “What are they going to, do their own chips or something?” There’s no way that’s going to work out. That never works out. And here we are now with just this incredible portfolio of multiple ways of doing. It truly is a success story.

Gadi Hutt: We use the silicon innovation across these domains to help customers do more. And we invest, we have a very good advantage to know the customers really intimately and understand what they need and learn from them and also innovate on their behalf in some cases, just to make sure we get the right products at the right time. Silicon design times are, it’s not like a software product, you write something, you deploy, you test, you change. Silicon is a longer design cycle. So we actually have to predict the future quite a bit, in a lot of cases. So it’s a very engaging environment. Just make sure we kind of keep up with the demand and customer innovation.

Daniel Newman: Except for those fun programmable FPGAs.

Patrick Moorhead: Oh, yeah.

Daniel Newman: That’s not what we’re talking about here.

Patrick Moorhead: Exactly.

Daniel Newman: Silicon innovation has been a big part of this story for AWS over the past few years. Started with compute, traditional monolithic compute for the data center and then progressed rapidly into AI, which is what I think we’re going to really focus on here today.

Patrick Moorhead: Don’t forget about Nitro. I mean Nitro’s pretty awesome too. Let’s not cut it short.

Daniel Newman: All right, all right. We’re going to talk about Nitro?

Patrick Moorhead: No, we’re not.

Daniel Newman: All right. So Nitro, Graviton, Trainium, Inferentia. Talk to me a little bit about the customer response to, let’s start with Inferentia.

Gadi Hutt: Yeah, so Inferentia we launched in 2019. Back then, most of the usage, the models were relatively small, hundreds of millions of parameters for machine learning models. And customers were spending most of their money or inference workloads. So we started with an inference chip called Inferentia. Huge success both with external customers. We just had, you saw in Adams keynote, Qualtrics. I was on stage today with Pinterest. Last week, ByteDance, the company that runs TikTok and the Douyin in China. They announced that they’re using Inferentia and they’re saving 80% on their cost and improving latency by 25%. And small startups and large organizations like Entity and Airbnb and Snap are all using Inferentia today. So huge success from external customers. Also, internal customers. We talked in the past on Alexa still growing strong, but we also added a lot of other services. Amazon Search, Amazon product search is using Inferentia, again, saving 80% on the cost of search, running search. Other teams like robotics, ads, Amazon ads. So just huge adoption, of course, multiple use cases, language, computer vision, and recommended models.

Daniel Newman: It’s great that you get to build so many of the models and stuff for your own businesses. That’s kind of cool.

Gadi Hutt: For us, they’re all our customers. So for our team that designs those products and kind of brings them to market, we don’t differentiate between internal customers or external customers. They all are demanding and they have a huge commitments to their customers that we have to make sure we can help them meet.

Patrick Moorhead: Yeah. Really good traction with inference, with Inferentia, Inf1, new Inf2. But it doesn’t stop there, it’s training too. And quite frankly, there’s not a lot of choices when it comes to training. I was pretty excited that you brought out Trainium. How is that going?

Gadi Hutt: Like you said, we start with the success we had with Inferentia. What happened in the meantime is that the models grew in size. So for customers to get more accuracy out of their models, typically what happens is they increase the size of the model, which increases the compute requirements, which also increases the spend on those models. So in some cases, I’ll give you an example, large language model, that takes a few weeks to train to completion, can cost millions of dollars to train. And what happens if you made a mistake? You need to start all over again.

So these are huge spans that our customers are facing today with GPUs. And this is why we decided, okay, let’s replicate the success we had with Inferentia in lowering cost and bring this into training. So today customers see 60% lower cost, up to 60% lower cost on the training work jobs, which means two things. They can either train more for the same budget or use the budget for other things to serve the customers better. So it’s really giving customers more choices in what to do with the resources that they have.

Daniel Newman: So as Pat alluded, to training, it was a bit of an greenfield because there’s only really one player that kind of really. And of course you have a great relationship as a company, but I think also opportunistically, you’re looking at things like power. You were looking at things like cost and saying, “Hey, with training only going one way, the amount required, we want to have another alternative.” And that’s been really a strong spot for AWS. But I’m imagining you’re not coming in saying, “We’re going to do all the training for all the workloads.” You have some types of workloads that you’re probably thinking Trainium1 is really suited for.

Gadi Hutt: Yeah, it’s a great point. So we are giving customers all the choices. We have accelerators from NVIDIA, we have accelerators from Intel, we have accelerators from MD. All of those are available for customers to use. And basically, they should choose which is the accelerator that gives them the best performance, the best cost for their workloads. In the case of Trainium, what we are trying to do, we’re trying to focus on three areas, increase performance significantly. So today, Trainium, in a cluster single node, we are up to 50% faster as compared to the latest GPU instances. When we go to a cluster, our scaling, because we have tight tightly coupled technology together with EFA, which is the Nitro chip, we can do some innovations for customers. So our scaling is almost linear and we get up to 2.6 better costs for large models as compared to the GPU instances. But customers also are used to using GPUs. We’re not taking that away in any way, shape, or form. We’re just giving customers more options.

Patrick Moorhead: One thing that, as a previous chip guy, I appreciate about your claims, is that they end up being true over time. And a lot of times what you see is they’ll be a claim out there and it’s true for a month. You go back and check, and I think you and I talked about all the work that you did on Inf1 one with software and how you were able to get just so much more performance out of that. But that doesn’t mean that there’s not value for the next generation of chips. And now we have Inf2 with the second generation of Inferentia. What were some of the improvements and what are some of the benefits that, that’s bringing to customers?

Gadi Hutt: Yeah. So like you mentioned, we invest a lot in software. We actually have more software people than Silicon people in the organization just because we want to make sure the ease of use is there for our customers. So the new one, SDK, spans all of these product lines. It’s the same in [inaudible 00:10:02], natively integrated into popular frameworks like Pytorch and Tensorflow. Specifically for Inf2, what we have done, we have taken the same silicon architecture like we have in Tranium, but we cost reduced a lot of the elements that are needed for training for large instances that are training connectivity within the instance, and a lot of networking going out of the instance. All of those are not needed for inference.

What we did, which is unique, that doesn’t exist in an inference, in other instances that are inference optimized, we did keep high speed connectivity between all of the accelerators. What that gives, it gives customers options to deploy these large models that they’re spending millions of dollars to train on. The only option before Inf2 was to use the same training machine to deploy the model because you cannot fit the single model into a single chip. So Inf2 is giving that capability, basically all the capabilities we have in the training machine, but in a cost reduced package, that will give customers the ability to deploy these large models up to 175 billion within a box, which is unheard of.

Patrick Moorhead: It really is. It’s nuts. Nuts in a great way.

Daniel Newman: Clarify, why is Inf2 so good for these larger models?

Gadi Hutt: Yeah.

Daniel Newman:

What did you do?

Gadi Hutt: So again, so what we have done, we kept high speed connectivity, we call neural link between the accelerators, and now you can deploy across single model that doesn’t fit into the memory of a single accelerator. We can deploy the cost multiple accelerators in a cost effective way.

Patrick Moorhead: And did you do something with HBM as well?

Gadi Hutt: Yeah, so the chips are integrated with HBM memory. We kept the same HBM memory. We didn’t cost reduce the chip itself. We kept the same configuration as we have on the training, just to make sure the customers that are training these large models can also deploy these large models in a cost effective way. The way we reduce the cost is on the system level in the cloud itself, in the system level, like I said, the reduced connectivity within the server and reduce the EFA and ENA bandwidth, which are not needed for inference workloads.

Patrick Moorhead: I really appreciate, it’s funny, it’s not even fair enough to say you’ve taken a systems level approach. You’ve taken an entire data center level approach.

Gadi Hutt: Yes.

Patrick Moorhead: And that’s something I find myself having to explain to people out in the marketplace, which is this isn’t just a chip that is uniform and fits into everything. This is custom tailored for the architecture inside of AWS’s data center. And I think as we saw Moore’s law, very hard to keep up with, we had to find more novel ways of hitting the higher levels of performance. And you go systems, but I feel like you took it one step further, which is with all your clusters and how do I connect that. But listen, I love monster performance and efficiency. But the other thing I think we have to talk about as we get into this accelerate computing is sustainability.

Gadi Hutt: Oh, yeah.

Patrick Moorhead: And what does all this efficiency mean for sustainability? What’s your take on that?

Gadi Hutt: Yeah, so we are a hundred percent focused on sustainability, this coming direct request from customers. Because these are compute intensive workload, it’s super important for customers to minimize the carbon footprint and the impact. For Inferentia, we are 53% lower powers compared to the comparable GPU. With Trainium, we are 50% lower power. But it doesn’t end there because the performance and the scaling, like we discussed on the clusters, is higher, what happens is there is a compounded effect. So let’s take an example. If someone wants to deploy a million inferences in an hour for a document retrieval service, we actually tested it, if they need to use GPU, the G4 instances, they need to use 33 of those instances to run a million inferences per hour. If they will use Inferentia, they will need only six, to run the same million inferences. What this means is that not only compared the instance to instance with we are lower power, we’re actually using less instances. So the compounded effect is 90% reduced energy.

Patrick Moorhead: Interesting. It’s compounded almost.

Gadi Hutt: Yeah.

Daniel Newman: So I can say though, because we had a conversation with the director of sustainability at AWS and we were talking about economical sustainability. And I think as the economy slows, a lot of these sort of pet project ESG stuff, it’s all important, but it’s been kind of like, okay, I can’t justify this to my shareholders as much right now. This just makes sense.

Gadi Hutt: Yeah.

Patrick Moorhead: Well, it’s a twofer, right?

Daniel Newman: Well, I’m saying you’re getting more performance, you’re lowering your footprint, you’re getting to that lower carbon number that everybody’s aspiring to, and you can show it on the income statement, the balance sheet, like we’re actually delivering value. It’s so smart you almost wonder what are the hurdles? Why are people not, and by the way they are, but why is it not like a-

Patrick Moorhead: We had this, we had a fun… Was it a year ago or 18 months ago? It’s just like, hey, it’s one thing to do it internally, who else… By the way you delivered. Okay?

Gadi Hutt: Yeah, thank you.

Patrick Moorhead: That slide’s getting bigger and bigger and bigger with some very credible customers that I know need your technology a lot like Qualtrics, as an example.

Gadi Hutt: Yep. Again, Trainium instances this just launched recently. So we are still building the customer base. We have good references that I can talk to. We have a company called HeliXon that will do important folding on Trn1. We are collaborating with PyTorch quite heavily. We have a startup, really promising startup called Magic that are doing core generation on Trainium and a few other companies. But we are just getting started.

We have actually what we call the large language model companies. They’re just starting to do that on Trn1. We just recently launched. And so we are not announcing it yet. And it takes time to train these large models, it’s weeks to months. So you should expect more good announcements coming soon.

On the Inf2 side, Inf2 is also enabling innovation that doesn’t exist today on Inf1 because the field evolved. So we can support things like dynamic input shapes that are critical for voice type of workloads. We can support customer operators. One of the feedback we got from customers is, it’s great that you have this compilation, but what happens if your compiler doesn’t know my operators? So we’re giving now customers basically a very simple C++ API. You just write your own custom operators and they run natively on the chip.

So there is a lot of innovation going on as well just to enable more and more customers will feel comfortable. And one key aspect of that is also called industry collaboration. Like I mentioned, we collaborate very closely with PyTorch and we are actually co-founded with PyTorch and Meta and Google and NVIDIA and Apple Foundation called OpenXLA, which will open up the front end, all sorts of compilers that can target different hardware machines. So for Apple, it will probably be something like the iPhone. For us, it’ll be Trainium and Inferentia, for NVIDIA it’ll be Dell GPUs. And what that will help is an open source community around compilers, making sure folks can continue to contribute that and we will all benefit from those contributions.

Daniel Newman: So you talked about PyTorch, we kind of jumped from the sustainability and now you’re talking developers.

Gadi Hutt: Yes.

Daniel Newman: And so developers that are watching this or that are paying attention, seeing the great advancements, seeing the sustainability benefits, seeing the larger models, they’re going to be saying, Okay, I want to develop on Inferentia and I want to train on Trainium.” How do they get started?

Gadi Hutt: So it’s super easy to get started. One of the leading tenants for us is not asking our customers to change the models to fit our hardware. That’s a big no-no. We are investing heavily in the compiler technology to make sure that customers will bring the models, the compiler will do auto optimizations for them and lower the model into the hardware. So in practice, what happens is that you basically need to make sure we include our SDK, you write one line of code, which is the compilation phase, and that’s it. After that, there are no changes to the model, no changes to the training scripts, no changes to the deployment of your inference, all the applications that customers are using today to deploy the models apply as well.

Patrick Moorhead: Whenever I hear the one line of code, I just want to smile. My son could figure this out, right?

Daniel Newman: He could.

Patrick Moorhead: He could. Yeah, sorry to interrupt, but I just love the one line of code. Just so I understand, if I’ve been doing this work on a GPU for the last three or four years with one line of code, I can put that over?

Gadi Hutt: Yes. We are testing with open source models. So anything on HuggingFace will run on Inferentia and Trainium. Anything that is coming from Meta will run natively on Trainium. We actually have a public roadmap of features that we are adding over time. So customers are informed, well informed, on what we are still working on. And the only caveat will be if customers have CUDA related code that is very specific to GPUs, that’s something they will have to take out of their applications. But open source models usually don’t have those CUDA dependencies. So that’s a great starting point for everybody.

Daniel Newman: I just love talking silicon. I don’t know about you.

Patrick Moorhead: Oh, you know me, you know about me.

Daniel Newman: I don’t know about you, but I’m just saying this is exciting. By the way, really, it’s hitting on all the fronts. It’s hitting on performance, which that’s going to be an area that people are going to constantly be monitoring. Like you said, every claim has about a month shelf life in this era. But you’re hitting on some other things. You’re hitting on the economics and the cost and helping people. You’re democratizing, making it more available. You’re hitting on sustainability, which is really important as well. And then of course, the developer community, in the world of AI, it’s kind of like mobile apps. You can’t become successful if you don’t get the developer communities. You really got the quadfecta.

Gadi Hutt: Yeah.

Daniel Newman: You’ve got to be feeling pretty good about your situation.

Gadi Hutt: Yeah, we are super happy. Our customers are happy, we’re getting good feedback. There is a close relationship between all those vectors, right? Because if we increase performance for and make the chip more efficient in doing what it needs to do, that also helps with sustainability because it reduces the performance by what aspect of the workload. When we improve visibility, it makes it easier for customers to come and make use of those accelerators. So all of those are interconnected with each other.

We are doing a lot of work out in the open with PyTorch, contributing features into the PyTorch ecosystem just to make sure it’s really easy to use those instances and we’ll continue to contribute. We have more features coming really soon. So, that’s a really exciting time. And because the models are becoming so expensive to use, we really see more customers looking at these large language models and large computer vision models like Stable Diffusion and others just to kind of, before on GPUs, it was cost prohibitive to many customers. And now we are starting to get you with more and more developers, academia and independent researchers that never had this ability to run on these platforms cost efficiently.

Patrick Moorhead: I love the customer angle and I love that, but I also like the overall story about, I don’t think anybody thought you would be able to pull this off. And that’s a fun part. By the way, it’s a story that doesn’t get told a lot. I wish it did because it’s one thing, I think everybody said, okay, Nitro, internet working, okay, I get this. But then, hey, we’re going to do a general purpose CPU. But hey, it’s going to be limited to what it can do. And then Graviton2 and then Graviton3 for high performance computing. Okay? Now, inference, ah, there’s no way that’s going to work. Right? And then this happens. And then Trainium, which again, different stage. It’s the most recent architecture you brought… Yeah, yeah. No, essentially Nitro is their DPU.

Daniel Newman: I know. I said the DPU.

Patrick Moorhead: Yeah. So it’s been a really fun story and I just want to thank you for coming on and sharing this with us. I’m a little enamored with this whole thing. I’m not usually this positive-

Daniel Newman: No fanboy.

Patrick Moorhead: … On this. But I also know how hard this is. And I look back at the last 30 years of either systems companies who’ve been able to crank out really good silicon, and you can count them on two fingers. Okay? And you’re one of them.

Gadi Hutt: Appreciate it.

Patrick Moorhead: Congratulations on it. The other great part about is you’re unrelenting. I mean, you’re just like, you keep coming out at this rate.

Gadi Hutt: We have to, Pat. Customers are asking us to do this. We just have to. There’s no other choice. This space is moving so fast, you should expect more soon. And we’ll keep improving what we have and we’ll keep improving inference software and of course, new silicon generations for years to come. This is a super exciting vertical for us and segment, and we just see a lot of good feedback from customers. And thank you for the kind words.

Daniel Newman: Sure. Gadi, thank you so much. Everyone out there, thanks so much for tuning in to this episode of The Six Five On the Road at AWS re:Invent 2022. Daniel Newman here, joined by Patrick Moorhead. Great event, great week. If you like what you saw, hit that subscribe button, watch all the other episodes here and of course, everywhere that Patrick and I are with The Six Five. But for this episode, it’s time to say goodbye. We’ll see you all later.

The post The Six Five On the Road with Gadi Hutt of Annapurna Labs at AWS re:Invent 2022 appeared first on Moor Insights & Strategy.

• The announcements AWS made regarding sustainability
• What it means to be Water Positive for AWS customers
• Why the cloud is a more sustainable option than other solutions
• A recap of other sustainability announcements from 2022

The Six Five On the Road with Chris Wellise of AWS at re:Invent 2022

You can watch the full video here:

You can listen to the conversation here:

Disclaimer: The Six Five On the Road is for information and entertainment purposes only. Over the course of this webcast, we may talk about companies that are publicly traded, and we may even reference that fact and their equity share price, but please do not take anything that we say as a recommendation about what you should do with your investment dollars. We are not investment advisors, and we do not ask that you treat us as such.

Transcript:

Patrick Moorhead: Hi, this is Pat Moorhead and we are here live at AWS Reinvent 2022 in Las Vegas. This has been an incredible event. I mean, we’re seeing the maturity of the cloud. It’s 15, it’s a teenager, kind of awkward, not perfect, but growing a ton here. But I got to tell you this, is this a great event?

Daniel Newman: That 15-year thing and that awkward teenager metaphor, I mean, it’s going to stick with you. It’s going to be with you for the rest of your career.

Patrick Moorhead: It is, and I’ve only used it for the opening for half our videos, so not all of them. But no, you have to admit though, I mean putting these things in perspective, I think is important because it gives you that idea kind of where it is. And I think we all know that. I think even Andy Jassy said on CNBC, we’re 10% into the cloud journey, so we’re definitely not there. I mean, 15 is pretty good, right? That’s like well, 25% of your life.

Daniel Newman: It’s still early days, but there’s no question just walking around the show floor or the hall that the demand for compute, the demand for the cloud, and what you can do with the data, with the apps. And then of course, with all the infrastructure, it’s only growing. And any speculation of the world, the technology is in some downward cycle misleading. I mean, the truth is there’s not a single part of the technology stack that you can look out five years and say it’s going to be less important. So yeah, we hit bumps, we hit cycles, we hit ups, we hit downs. Straightforward, walk through the halls here. You’ll see every enterprise, governments, academic institutions, organizations, non-for-profits, they’re all here looking at ways that technology can drive their businesses into the future. And of course, they’re all here looking at ways to do that in a sustainable fashion that helps not only their companies be more successful, but helps the planet too.

Patrick Moorhead: Yeah, it’s a great lead in to introduce our guest. Chris, how are you doing?

Christopher Wellise: I’m doing great. Thanks guys. Thanks for having me.

Patrick Moorhead: Yeah, good to see it. Really fun green room conversations. Sometimes I’m thinking we should just turn on the camera during those and maybe kind of a blooper around.

Daniel Newman: Bloopers, B-roll. Maybe some of the actually best and most intimate interactions that we have on the show.

Patrick Moorhead: Oh, I agree. So Chris, maybe a good place to start is let’s talk about what you do for AWS?

Christopher Wellise: Absolutely. So I’m the director of sustainability at AWS. And I really, to simplify it, I focus on driving down the environmental impacts of our infrastructure as well as helping customers to meet their own sustainability related needs.

Daniel Newman: So that shirt was made out of recycled goods, right?

Christopher Wellise: That’s right. Bamboo.

Daniel Newman: I love it. I love it.

Patrick Moorhead: But is it edible?

Daniel Newman: We’re working towards those kind of things. You guys had some big announcements that happened here. One of the biggest ones. And Adam’s keynote was largely focused, had an underpinning and theme of sustainability all the way through it. But talk about the water announcement. You guys made a big announcement, something that you were very excited and it came out very early in his keynote, which is always an indicator that this is front center and in focus. Sustainability is really important to AWS.

Christopher Wellise: Absolutely. Well, first of all, it’s great because Adam not only understands what we’re trying to accomplish from a sustainably perspective, he’s got a personal passion for it, which is great. And he knows what it means to our customers. So that’s always helpful when you’ve got leaders that really get it, understand the deep complexity around some of these topics. Our big announcement was around being water positive. Most people when they think about cloud services and applications that they’re running, water’s not diversity that comes to mind. But we do use water. There are a lot of resources that go into powering the cloud, like any resource intensive or energy intensive infrastructure or operation.

Water’s used primarily in cooling processes through evaporation. We do employ technologies to limit the need for water. So things like IoT analytics and sensors that only allow or require water when absolutely necessary. But in taking that into consideration, what the team did is we thought, we understand that water’s a scarce resource. Half the world’s population is going to be within water stress regions by the end of 2025. It’s predicted, primarily driven through climate change. And we understand that it’s an important resource. And so we wanted to essentially really focus on our water stewardship. So what water positive means is we will give back more to the communities and watersheds that we operate than we require for our direct or use for our direct operations. That’s what water positive means.

Patrick Moorhead: No, that’s great. And I’m sure you look out into the future, and it’s going to be even more challenging for a lot of reasons. So first of all, Moore’s law has slowed down. And essentially what that means is you have to do more work with more power. And with more power comes more heat. And with more heat, you need more water to cool that off. In fact, a lot of supercomputers today and even high performance computing use water to cool things down. By the way, as a byproduct, a research lab in Canada is using that excess heat to melt ice and snow on its roads, which is pretty novel.

But the challenge is going to get even harder in the future. And one of the things that I’ve appreciated is you have even homegrown processors with the graviton line that use 40% less power than other instances. So it’s not that you’re putting your money where your mouth is, making some pretty big investments to be able to do that as opposed to maybe just buying something that’s different. Is this a water initiative? Is this a new thing? How long have you been working on it?

Christopher Wellise: We’ve been working on it like we do many things before we announce large targets, as we work on them internally. We’ve had an internal goal around water positivity, establishing and fine tuning the metrics associated with being water positive. And we’re really focused on accomplishing water positive through four key strategies. You brought up an important one. AWS now designs and develops our own silicon around the third generation of graviton. That’s an important element. So efficiency is the first strategy to limiting water use in our operations. And I mentioned we deploy things like IoT analytics and sensors in the data center environment. In countries like Sweden, and Ireland, for instance, 95% of the time we don’t use any water, because we’re able to monitor the ambient temperatures. We use free air cooling when possible. And only when we exceed those certain flip points do we actually begin to use water. So really focused on efficiency. Doing more with the less is really critical.

The second way or second strategy that we employ is around recycled water. So we actually invest in some of the communities where we operate. Loudoun County, Virginia, as an example, Northern Virginia. We have worked with the local municipality to develop infrastructure to reuse waste water. So you don’t need drinking water quality.

Patrick Moorhead: Is it called brown water?

Christopher Wellise: We call it purple. The pipes are purple. But there’s gray water.

Patrick Moorhead: Gray. That’s what I meant to say.

Christopher Wellise: Yeah. There’s gray water. It’s one level up from the brown water. But again, I mean through engineering controls, treatment and processes, you can actually get to a point where it can be readily used for things like cooling, and therefore you’re actually recycling and reusing water in a way where you’re limiting the need for extracting or consuming that high level potable resource. The third strategy is through reuse. When we run the water through our evaporative cooling systems, we can do it several times and then it needs to be treated. You’re essentially concentrating the dissolved solids through evaporation. You’re not polluting it per se, but you’re concentrating the dissolve solids that need to be removed.

But after removal, that water can still be used for things like irrigation. And that’s exactly what we do in places like Umatilla Oregon, in eastern Oregon where they’ve got a drought issue. Farmers use our waste water from our cooling operation to irrigate crops. And then our fourth strategy is really around rehabilitation projects, rehabilitating watersheds, replenishing groundwater tables where we can through groundwater recharge projects, making water available to communities where we operate, which includes things like sanitation, hygiene and so on. So through those four strategies, we’re working to achieve our water positive goal by 2030.

Daniel Newman: Cool. Very ambitious. And of course, being the largest infrastructure provider in the world, you’re using a lot of compute. To Pat’s point, you’re doing things innovating on the tech side, but you’re also innovating on the sustainability side. I do have a bigger question. So both Patrick and I follow very closely, the broader Amazon. It’s been one of the most outspoken global leaders in terms of talking about sustainability, the climate pledge, some of the things the company has done with Rivian in the electric vehicles, in the electric delivery. I’m just curious, do you work closely? Is there a lot of coordination that exists between AWS and Amazon? I know some of the parts of the companies are like this and other parts are like this.

Christopher Wellise: Absolutely. We work very closely. As a matter of fact, I was meeting with customers with Kara Hurst, our global VP of sustainability this morning. They’re AWS customers, but they often move beyond, depending on their business model, they might want to understand what we’re doing around logistics, what we’re doing in our fulfillment centers, how we’re working to procure additional renewable energy. There are a number of different ways where we might engage across the company. So we really work very closely in tandem and have a one Amazon approach to sustainability. I

Daniel Newman: Think that’s great. And thanks for answering that. I’m curious because like I said, I know you often feel like we’re this group and we’re this group, but the world does see Amazon as Amazon.

Christopher Wellise: Absolutely.

Daniel Newman: And so I think the more that narrative stretches across the company, and you kind of tell this very consistent story, a lot of value in that. So let’s stay on the AWS front though. I’ve run off the road. I’ll go back on. What other announcements were important for you on the sustainability side at this event?

Christopher Wellise: Well, we’ve had several over the course of the year. The big one at Reinvent in 2022 in Vegas was our water positive announcement. But we’ve rolled out a number of things this year. We rolled out last March, the customer carbon footprint tool, which was really critical to helping our customers understand from the analytics perspective what the carbon related to their deployment of workloads on AWS cloud looks like. They want it not only for reporting purposes, but they want to figure out how they can change behaviors to optimize for sustainability. So that’s one of the things that we’ve been really working on closely, and we’re hoping to roll up more features in that tool in the future.

Patrick Moorhead: That’s great stuff. I love the new pieces of data that customers can use to help make better choices. And sometimes if the ability for somebody to pick something that it helps them meet their sustainability goals and it’s good enough performance and the cost is right and they can go for it, I think choice is good. So there are many debates out there on on-prem versus the cloud. I mean, I’ve been running my business for 11 years and I think I’ve had 11 years full of debate on which model’s better? Well, I think, or it’s this mature, right? We talked about this 15 years where it’s like, okay, the future’s going to be a combination of OnPrem and public cloud, A lot of AWS public cloud. Of course. Congratulations. Why is the public cloud so much? I heard a statement that y’all made, said the public cloud is more sustainable, that OnPrem. Can you take us through that?

Christopher Wellise: Well, sure. Well, I mean, first your point around just technologists now focused on sustainability is an interesting one. And I’d like to riff on that for a second. Because one of the things that’s so interesting to me, having worked in this space for a number of years, the number of CTOs and CIOs that have become sustainability experts. I think it’s just awesome. A number of the discussions at Reinvent this year have been CTO CIOs that are directly engaged in sustainably related goals associated with their IT estate. That’s pretty exciting. You wouldn’t have seen that even three, four years ago.

Patrick Moorhead: I agree.

Christopher Wellise: It might have been one or two that had a personal passion for it and we’re self taught, but now we’re seeing more and more of it. So that’s really cool. And the sustainably benefits of AWS cloud are largely recognized by these folks. They are partnering with us. They understand the benefits, primarily driven through economies of scale. So about two-thirds of the benefits that you see from an environmental standpoint. And those benefits could be summed up, on average, you gain anywhere between 3.6 and five x improvement and efficiency depending on the region you’re in. And you reduce your carbon footprint anywhere between 80% and 90% depending on the region that you’re in, on average globally. Two-thirds of that comes from economies of scale. We’ve got our engineering teams are heavily focused on building the most efficient, sustainable infrastructure that has high utilization rates and runs as efficiently as possible. The other third is related to our use of renewable energy. We’re now the largest purchase of renewable energy in the world. Over 370 projects.

Patrick Moorhead: That was a 2021 announcement, I think.

Christopher Wellise: That’s right. And we continue to accelerate. We’re now at 85% renewable energy. That’s for Amazon wide, by the way, not just AWS. And we are on track to meet our 2030, 100%target five years ahead of schedule. So the other third of those benefits come from our renewable energy procurement.

Daniel Newman: Well, that’s really impressive. Now, as you go through this exercise, Andy Jassy did say we’re probably about 10% of enterprise workloads, a little bit more than that when you look at the whole massive workloads that are in the cloud now. But we’re still somewhat early days, and as customers are in this migration period, are you working with them to quantify that number of, hey, this is how much more you’re driving towards ESG goals and sustainability goals, not to mention maybe some even economies of scale for their businesses by moving to the cloud?

Christopher Wellise: We do. I work closely with colleagues. A team within our group is a DC Divest Team, which actually helps to recycle old assets, handle old assets in a responsible way. And we also do some quantification for some of those customers that are interested in large scale migrations. Our customer carbon footprint tool actually gives you a snapshot of what your workload would’ve looked like in an on-prem environment versus AWS environment. So those are some of the data points that we’re providing for customers.

Patrick Moorhead: Yeah, I love that tool. We talk a little bit about this in the greenroom. I want to bring this up. I do a tremendous amount of C-suite advisory and consulting. And when you come in there, there seems to be a couple different camps. I’m not going to pretend like everybody in the C-suites on the same page. They aren’t. You have one camp who’s trying to, maybe they’re operational, and they’re just trying to squeeze every piece of efficiency out of that. You have another camp that they have to hit their ESG goals, and then have people who have CEO typically who at the end, has to make money and do it in a sustainable way as possible for the shareholders. So my question for you is, first of all, is this something that you’re seeing as well? But is it possible, I think it is, to be sustainable, good for the shareholders and good for the environment. Is that possible? Because, I see that really that gear is locking in with the audiences that I talk with.

Christopher Wellise: Well, the term triple bottom line was coined 30 or so years ago, and we still see that as very true today. Business success and being sustainable are not mutually exclusive. I really believe. And you know, could really break it down into the most common denominator too. You can look at carbon as a proxy for a euro or a dollar. If you’re optimizing for carbon, I can’t think of a single instance where you’re optimizing for carbon and you don’t benefit from a cost perspective as well. Because, you’re typically driving additional efficiencies into the processes that you’re engaged in.

Patrick Moorhead: That actually makes sense. If I look at the Brownfield environments in particular, whether it’s manufacturing, whether it’s transportation, whether it’s warehousing, what I’m seeing a lot of action is, and whether we call it IoT on a horizontal basis, but it’s smart manufacturing, smart distribution. It is saving energy, and using less resources, and it’s having a positive impact. The bottom line now, I mean, look in Western Europe with energy prices, there is no doubt if you can find a way to use less energy, you are going to save money and it is going to hit the bottom line. And I don’t like sides. I mean, it is a reality as humans sometimes we bunch up together. But I think if we are going to do this right, we need a rallying cry that there’s something in it for everybody.

And I have yet to find a person on any side that doesn’t agree with… Using less to do the same, that’s actually a good thing. We should try to do that. And that everybody we’re unfortunately in some polarizing corners. I see. And I think that’s inhibiting the ability to move this forward as a planet.

Christopher Wellise: Absolutely. And I mean, if you look at just simply investment and the rise of ESG funds, for instance, sustainability over the last 10 years has become so fundamentally integrated into the C-suite and core business strategy. Adam on stage is a perfect example of that. And Andy’s commitment as well to sustainability. You’re seeing that with our customers as well. I’m not surprised when I meet with customers, and I often hear that sustainability is on parody sometimes with cost that they’re really evaluating. That’s how important it is to our customers. Not all, but some of the sustainability leaders express that sentiment. So it’s really become embedded in the core strategy of businesses.

Daniel Newman: We’ve done a remarkable amount of research on sustainability investment. What’s driving some of the biggest concerns, by the way, from some of our studies, is really that companies don’t feel that they’re moving fast enough to meet the goals that they’ve committed to. And obviously they feel like they’re doing a lot now, but they still see so much in front of them in order to accomplish. I do think that there is a bifurcation between those trying to utilize ESG as a short term architecture versus companies that understand a long term business strategy. Companies that see the horizon, because you might have to rebuild a new factory from the ground up to be sustainable. You can’t always brownfield these things. The point is, year one and two, you may take on some significant capital expenditures to get there. You need to get your shareholders in line, you need the business line.

But over the 10 years that follow that, it’s like lead buildings. There’s been a lot of debate about the lead, because the problem is, in the short term, companies had to absorb a lot of cost to get these buildings to where they needed to be over the next 10 or even businesses, which should be thinking 50 and 100. These are generational. That’s where the economics get really good. But a lot of businesses don’t see past the quarter. And I like to yell at Wall Street about that, but they can’t see long enough to get out there. So having sustainability, a new narrative, a new track that talks about it through this lens of 10, 20, 30 years like we do about the Paris Accord like we do, doesn’t unfortunately always match the street. But if it does, I think we’ll get a lot more buy in.

Christopher Wellise: Yeah, I agree. And people often ask me, where do I see this field heading? The space around sustainability? And I think that the future Amazon’s maybe 10 years from now will be sustainability native companies.

Patrick Moorhead: Interesting. I’ve never heard that term.

Christopher Wellise: Not just tech native companies, they talk about decarbonization. It’s highly complex. We’re faced with the same challenges with our own business as our customers are. And half the technologies that will drive decarbonization, they say, don’t exist today. So it’s going to require major investment and major innovation. We’re going to need to source low carbon components for the racks we build that get deployed within our data centered environment. So we work carefully with our suppliers to see how we can partner on that front. But that’s going to be required in every industry. And it’s not an easy task.

Patrick Moorhead: It’s not. And if you look at where some of how our biggest challenges in the past hundred years have been solved, a lot of it has to do with a combination of, there’s research which is funded by various entities and governments and the VC community, and then there’s big companies. So I’d like to see the big companies come together and find really to try to solve some of these biggest challenges. Not in an incremental way, but truly in a way that makes parabolic improvements in this. I know it’s there. I mean when mankind put puts its stamp and its desire to do something, we figure out how to do that and that. That’s what I hope we can do Chris.

And Chris, I just want to thank you so much for coming on the show, and it’s great to talk face to face about this because we’ve done so much research. It’s funny, we got a PDF where we have a small conversation here, and we’re trying to figure out who I am. But getting you in front of our audience and all the folks out there, I think is a really big benefit. If nothing else, just on the education of maybe how they can strive to do things different, maybe based on some of the things that you’ve done, where you were able to trail blaze out there. So we’d hope to have you on again at a future time.

Christopher Wellise: We’re always learning, right? It’s a big challenge. So we continue to iterate and learn constantly. So it’s been a great conversation with the both of you. I really appreciate it. Thank you.

Daniel Newman: Thanks. Chris. All right, everybody. You heard it here. We are, The Six Five on the Road at AWS Reinvent 2022. We are broadcasting from the win, enjoying each and every one of these conversations, talking cloud, talking business, innovation, sustainability, and the future. If you like what you heard, hit that subscribe button. We’d love to have you as part of our community, check out all the videos that we did here at AWS Reinvent. And of course all the videos that Patrick and I do on The Six Five. For this episode though. Time to say goodbye. See you later.

The post The Six Five On the Road with Chris Wellise of AWS at re:Invent 2022 appeared first on Moor Insights & Strategy.

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Transcript:

Patrick Moorhead: Hi. This is Pat Moorhead and we are live at AWS re:Invent 2022 in Las Vegas. The Six Five is back and we are rocking it with awesome tech conversations and great people. Daniel, how are you?

Daniel Newman: It is good to be here. It’s good to be back. This week has been compelling, let’s just say, from day one. It doesn’t really matter if we’re talking about practical implementations of the cloud all the way to ambitions and sustainability and who knows, the future of data and even quantum.

Patrick Moorhead: No, I know, and that’s great to lead in. Let’s introduce our guest. Richard, how are you?

Richard Moulds: Well, it’s Wednesday, so I still got a voice, but it’s an amazing show. Two and a half days in, wow. A lot of people, a lot of conversations.

Patrick Moorhead: No, it’s great. In some ways, industry analysts are professional event attenders, right?

Richard Moulds: Right.

Patrick Moorhead: Literally, we’re like a pack of clowns going from show to show. One of the ways that I gauge a show is the amount of people per square foot who’s walking around in the hallways. If I go to the Venetian, a little bit of it here at the Wynn, a little bit calmer, but this show is absolutely rocking and totally understandable. AWS is at the epicenter of so many things that essentially invented IAS 15 years ago. One of the things in our research and we talk to your customers that they do so well is you have so many variations of compute and the ways of doing things. Also, some of the more future forward ones are looking at what’s next, and quantum computing is absolutely what’s next. I don’t think there’s anybody debating, is it going to be big? The only thing we’re debating is when.

Richard Moulds: Right.

Patrick Moorhead: Maybe a good place to start to talk is what do you do for AWS?

Richard Moulds: Sometimes I think I have the best job in AWS. I’m the general manager of Amazon Braket. Amazon Braket is the quantum computing service of AWS. We launched a service three years ago at re:Invent and we’ve just gone through our second anniversary as a GA service. So mainstream service, making it easy for customers to knock down the barriers to adopting and learning about quantum computing. So yeah, we figure out the roadmap. We manage the decisions around which quantum hardware we think is interesting to customers.

Patrick Moorhead: Right.

Richard Moulds: I’m sure we’ll get on to talk about it, try to span a massive range of diversity in terms of experience and focus. Some people are just looking for a quick win in this industry. Some people are thinking 20 years out as to where this technology’s going to expand. This is a builder’s conference. It’s not really a marketing schmaltzy conference.

Patrick Moorhead: No.

Richard Moulds: It’s all about building. What’s great about re:Invent is it’s training people. It’s getting people up the curve, getting people to adopt technology, serves a super, super curious crowd, this show, which is obviously perfect for quantum.

Daniel Newman: We were talking to one of his peers, Raj on the semiconductor side and I think we had a comment that 75% of the attendees of AWS re:Invent are geeks.

Richard Moulds: Absolutely.

Daniel Newman: The other 25% are wannabe geeks. So it really is for builders. It is [inaudible 00:03:32].

Richard Moulds: Absolutely. And that’s why I think it’s so valuable because it feels like a very practical event.

Daniel Newman: But there might be nothing geekier than quantum.

Patrick Moorhead: It really hits new levels.

Daniel Newman: It does. And we’ve been following it. We’ve been close. You can follow our work. We’ve done a lot of episodes, done a lot of research, published a lot of opinion on it, talked to a lot. By the way, it is a mostly PhD field, mostly. It is mostly a group of very academic. Most of the conversation’s been very academic, but one of the things that I’ve been really longing for is some people that can help democratize it. Let’s start making it make sense. Let’s start talking to the audience and just figure out how to take the cloud at scale and build really impressive stuff and do the same with quantum. So that, to me, is a lot of what Braket was all about, was starting to experiment with all the different companies that are building different quantum computers, using different quantum technologies, super conducting, ion trapping, atom splitting. So talk a little bit about the experimentation, the process that you’re going through, deciding who you’re partnering with and what Braket’s really setting out to do for quantum.

Richard Moulds: Yeah. Economy has been on the radar of a lot of companies obviously for 20 years. It’s a potentially disruptive technology as you said in your intro. So there’s a lot of companies that are nervous about being disrupted or see an opportunity to be the disruptor. There’s a lot of interest naturally that generates. We’ve been debating for 10 years whether or not and when AWS should be in the quantum industry.

Until a few years ago, we had concluded that it really wasn’t worth the time of most of our customers to focus on this technology. There just wasn’t quite enough center ground. The technology just wasn’t mature enough. As I’m sure you’ve heard many times, we’re customers-first company. We don’t do stuff just for the sake of technology. We do it because customers find it useful. So for a long time, it just wasn’t quite there.

Three or four years ago, we said, “You know what? It feels like it’s starting to come together.” As you said, different modalities, different ways to build a quantum computer, interestingly different in terms of how they fit use cases, clearer sense of what those use cases are. We felt it was worth the time and trouble for some of our customers, not all, to start investing in resources to really grapple this technology and to plan for the future because they all have the obvious question. When’s it going to happen? What sort of people do I need to hire? Which bits of my business are going to get impacted first? Is this a threat or is this an opportunity? So we launched Braket and I suppose the overriding goals are just to make it simpler. We think a lot at AWS about what we call the innovation flywheel, just generally try and knock down the barriers, bring the right people together and try and just accelerate the process.

As you said, it’s a super fragmented industry. The guys that build the hardware aren’t particularly plugged into the applications and vice versa. The educators don’t have easy access to the machines, unless you know a physicist or are prepared to spend potentially millions of dollars a year to get access to one of these devices. If you’re a corporation, you couldn’t get your hands on these systems. All different commercial model, different developer toolkits, different user experiences, super fragmented. So in a sense, it sounds a little bit cheesy, but we think of Braket almost like the town square for quantum computing. We want to bring the hardware guys, the software guys, the consultants, the educators, the researchers, corporate users together in one place. If you can take away some of the barriers, then perhaps you can accelerate innovation and just get there quicker and that’s what we’re all about.

Patrick Moorhead: Yes. So not only are there different modalities in the core technology, which by the way-

Richard Moulds: Which is a scary word for some people.

Patrick Moorhead: No, I know, but what I do love is I think we all realize in the industry that not one single technology is going to rule them all. In fact, I like to make parallelisms to things that have happened already and let’s take GPU compute. Not all solutions are best for different model sizes. One is gigantic models. We have training, we have inference, and we have everything in between. There’s also different familiarity with the technology that on the AI and ML side today, whether you want to go to the metal and you serve up hardware or you want a completely abstracted SaaS app with Textract or something like that, you have it. It looks like you’re trying to build with Braket opportunities for different levels of experience for people to be able to experiment. Am I reading that correctly?

Richard Moulds: Yeah, absolutely. That word experiment, that is the key word. There’s a lot of mythology in this industry as I’m sure you’ve detected in a-

Patrick Moorhead: We’ve hit quantum advantage. We’re done.

Richard Moulds: Right. Yeah.

Patrick Moorhead: Three years ago, supremacy. Sorry. Yeah.

Richard Moulds: Yeah, I know. This is still at the research stage. Companies are not using quantum computers in production systems to save money or do stuff faster. We’re not quite there yet and we might not be there for a while, but that’s not to say there’s not value in getting started. This is radically different stuff. It is applicable in a certain set of use cases, radical different set of skills. This is not something you pick up overnight. Our job, of course, is to break down some of the barriers so you don’t need a physics PhD to actually do this stuff, but still, it’s a curve and you need to get started. But yeah, you’re right, some customers really want to be abstracted away from the hardware. They don’t necessarily care how you build your qubits. Fair enough. But some other folks actually literally want to get in and play with the microwave pulses that literally control the atoms on the chip.

We just have some features in that function in that area, so we could talk about that later, but yeah, it’s a tool stack and it gets thinner the higher you get. A lot of the partners we’re working with are trying to close the gap between the hardware there and the actual use cases. Some people actually have problems that we think quantum can solve.

Daniel Newman: I see parallels with AI and HPC. I know it’s different state of maturity, but there was a period of time. I remember meeting someone that was like, “I got a PhD in AI in the ’70s,” and I’m like, “Whoa.” It was 50, 40 years before that really started that theory. So quantum obviously, we’re in this stage now where people are starting to realize there’s certain genomic challenges that it’s going to be able to solve. So you’re seeing large pharmaceutical and biotechnology companies investing. They know that eventually, they’re going to want to be there. Or financial services firms that want to be able to use it for anti-money laundering and fraud and understand that that heterogeneous relationship between quantum and classical is going to be a game changer.

But what you said is really great, is more experimentation now. The work they’re doing, they could still do it faster on a HPC with what they know today, but they know in the long run, they’re going to be able to do it faster tomorrow. These people you’re working with, these personas, these are almost like the innovation labs inside of big corporation, government, cities. Talk a little bit about that. Who are the people that are locked in the lab right now plugging into Braket, starting to figure out how to make this work in that parallelism of moving away from classical to what I think is mostly going to be a marriage of quantum and classical in the future?

Richard Moulds: Yeah. I think that’s one of the myth, again, one of the preconceptions, it’s not a myth, that somehow, a quantum computer is a supercomputer. It’s just the next biggest machine that somehow replaces existing classical hardware. That’s not the case at all. Quantum computers are really co-processors. They do certain sort of mathematical functions. In the end, we think better than classical machines, but they don’t do everything. You’re not going to be running a spreadsheet or video rendering on a quantum computer. It’s always about a classical world.

Daniel Newman: Where’s the keyboard? You know what I mean?

Richard Moulds: In the same way we think about a math coprocessor or a crypto coprocessor. It’s being developed.

Patrick Moorhead: How about a GPU as an example of that where it’s API-driven? Listen, the original AI algorithms or ML algorithms were developed in the ’60s. The problem is it takes big data to do machine learning and it was too expensive and we didn’t have the accelerators to be able to train it and do inferences. Then the folks at the University of Toronto came up with this ability to find images. By the way, I worked for one, a GPU vendor and I saw banging our heads against the wall trying to use this thing something other than playing games or doing 2D drawing. Is that a good analogy that we should look at where quantum is the accelerator and classic computing is running a set of the application?

Richard Moulds: I think it is. Yeah, I think it is. I think you could imagine an HPC workload today that’s struggling in certain areas. You can imagine leveraging a quantum computer in the same way we leverage other instance types. So use them in parallel, a manager workflow across a variety of resources, some of which are quantum, most of which are classical. So the notion that at some point, there’ll be GPU, CPU, QPU instance types, it does make sense, yeah. Calling it a quantum computer is almost a misnomer. You should think of it much more as a quantum processor for doing a certain set of things. What we are doing now obviously is enabling customers to explore what those things are and to ultimately figure out how do you coordinate a workload that spans these different processor types. How do you actually formulate a problem so you know this bit of the problem can go onto a quantum machine, this bit of the problem should stay classical?

And how do you actually orchestrate the workflow between those things? Because look at AWS, it is effectively an infinite pool of classical resource. Quantum computing is not there. These are individual machines that aren’t even alive for most of the day. You got very different operational characteristics, so organizing what we think of as hybrid algorithms that have quantum and classical components. Actually managing those workflows and orchestrating these things together is not so simple, and that’s what Braket does. We don’t just provide access to quantum computers, but we actually manage that interplay between classical and quantum compute.

Daniel Newman: So-

Patrick Moorhead: Oh, go ahead.

Daniel Newman: No. You got me thinking, so I kind of hit you on the personas.

Richard Moulds: Okay, which I don’t think I ever really answered.

Daniel Newman: I don’t think you ever really got you, but I’m going to join two thoughts together because I think it’s going to be persona plus enterprise. Because these personas are being hired by these companies, universities, these government entities, and they’re asked to explore. Amazon and AWS is that company that says, we want to meet our customer… They’re customer-obsessed, meet the customer where they are. So you’ve gotten into this business because there’s a journey going on.

Richard Moulds: Absolutely.

Daniel Newman: So I’d love for you to align that journey of the enterprise, the persona and how it’s all tying together that you’re helping them get there. Because they got to be coming to you saying, “All right, we know we need to do this. Help.” How do you take them through that journey?

Richard Moulds: There’s a couple of different ways. Some enterprises, I think of them as the quantum pioneers. They’re doing exactly that. They’re recruiting professors from universities and creating teams, significant teams in their organization.

Patrick Moorhead: Typically within the research group of the CIO or the-

Daniel Newman: CTO.

Richard Moulds: Yes.

Daniel Newman: Big banks.

Richard Moulds: Yes. Yes, exactly. They are scouting their organization to find the use cases and try just to get ahead of that curve. Some companies, they hear about quantum, they’re intrigued by quantum, they come in. They want a quick assessment. Is this a quick win for me? I’ve got problems to solve. I’m busy.

Daniel Newman: That’s easy. The answer is no.

Richard Moulds: Which is most companies. They have day jobs. Oftentime, the conversation, we’ll pivot to, “Yeah, quantum’s interesting. You should keep an eye on it and this is how you should do so, but in the meantime, we can make your classical stuff just go better.” Oftentimes, surprise, surprise. Not everybody’s maxing out what they could achieve classically. So a lot of that conversation turns back to classical technology.

Daniel Newman: It’s an $80 billion business for you guys now.

Richard Moulds: And growing quickly. Some people, hyperfocused. They are leading us in many ways. Part of that obsession is to listen to what they tell us in terms of evolving the service. We do have a professional services team. We launched it at the same time we launched the Braket service. We call it the Quantum Solutions Lab. It’s within the AWS Pro Serve organization, but it’s a specialist in quantum computing. It’s entirely staffed by quantum PhDs and really, that’s geared to corporations that really want to dig in, collaborate around algorithm development. In addition to that, we have a lot of partners or software partners. It’s interesting. In this industry, everyone focuses on the hardware because it is super cool. The way these work is amazing.

Patrick Moorhead: Right?

Richard Moulds: Awesome pictures. They definitely have the best pictures, but people don’t often think about the solution guys, the quantum software providers. Different folks, different skill set. Don’t really know anything about how to build a machine, but boy can they close the gap between the machine and the use case. There are dozens of software partners that have come out of the woodwork since we launched Braket. Our proposition to them is A, it’s an operational platform that can actually build a service on themselves, but also gives them the choice. It gives different instance types and they tend to be very industry-focused. They’ll be focused on chemistry problems or they’re focusing on application problems or fraud detections, the sort of things you were talking about.

They oftentimes are the first interface to corporations because they’re intrinsically solution-oriented and they can, in some cases, bridge the gap between quantum future and classical now and they can start taking those customers through that journey. In a way, Braket, we’re still at the infrastructure there. That will evolve over time, but now we are really keen on building that community of software experts to find those chinks of light in terms of where this technology really fits into different industries.

Patrick Moorhead: By the way, that’s how GPU compute started.

Richard Moulds: Exactly.

Patrick Moorhead: And now, look where it is today. It’s evolved and it’s delivering incredible value. I wanted to shift the conversation to academic research and open source. As far as I can remember, some of the biggest breakthroughs have been driven by research. I always like to clarify, don’t confuse R&D. I know we say R&D, but research is very different from development. Research has higher risk, has a higher failure rate, but boy, when it hits, it hits. If you look at whether it’s wireless technologies, compute, storage, anything that was huge out space came from first, research, and then we answered a bunch of questions and then we got development. A lot of that research is done through academic institutions for quantum. In fact, every company seems to be aligned with an academic.

Richard Moulds: They’re oftentimes direct spinout.

Patrick Moorhead: Exactly, and there’s funding. Then you have open source, which at least in the past 20 years, has been this growth multiplier where the promise is you make some improvements and get share of everybody else. You can take advantage of all the improvements that they put in and AWS has basically built on open source software. I’m getting to a question here. How does Braket help these two audiences, academic research and also the folks dealing in open source?

Richard Moulds: I mean the research piece is essential and it’s good that you drew the line between R&D.

Patrick Moorhead: Drives me a little crazy, I’ll admit.

Richard Moulds: Yeah. Sure, there’s a lot of engineering work going into building quantum computers these days, but we should never forget that to get to the point of a fault-tolerant quantum computer, there are significant scientific advances still to be made. There’s Nobel prizes to be had in this area. This is not done and dusted. This is not just making it faster and cheaper. One of the primary use cases for quantum computing right now is figuring out how to build better quantum computers fundamentally. A lot of researchers out there are diving into, really, I think two areas. We have the dream of delivering a fault-tolerant quantum computer, which we know can deliver exponential compute power. But we don’t have one of those machines yet and we might not for a while. So in the meantime, we are living with these error prone noisy machines. People have coined this phrase NISQ, noisy intermediate-scale quantum, which is a way of saying we’re going to use classical computers to make quantum computers work better in the near term. In the long term, we think quantum computers make classical computers work better.

Patrick Moorhead: Entanglement.

Richard Moulds: Right, but for the next, who knows, five years, maybe longer, classical computers make quantum computers work better, which is what NISQ is all about. We have researchers that are absolutely focused on the long-term game, error correction. Quantum error correction is the elephant in the room. We have to fix that problem. There’s no chunking out a million qubits of today’s quality.

Patrick Moorhead: Sure.

Richard Moulds: You just simply couldn’t use them. So we got to fix the world of error correction and there’s a ton of research going into that. Part of what Braket does, some of the functionality we talked about the stack. The reason why you want to dive down the stack is so you can research error correction, so you really can understand how these qubits actually interact at the physical level. The other area of research is the near-term stuff, is NISQ. It’s, how can we think about almost the notion of a self-training quantum algorithm? We used to trade the concept of training, obviously machine learning, but you can take similar approaches, iterative, convergent processes to enable quantum algorithms to be tweaked over time to better deal with the error profile that they see on machines that they’re actually working with.

There’s a whole discipline of figuring out what these hybrid or variation or algorithms actually look like. A lot of researchers are in that space because that’s where the near-term win is. That’s potentially the route to quantum advantage and is an endless debate in the industry as to whether that’s ever going to be fruitful. But you have to try because if it’s not going to work, you just have to wait until a fault- tolerant quantum computer comes along, which might be a while. So in the meantime, we have to see what value we can squeeze out of current devices, and that’s all about, again, [inaudible 00:23:14] together quantum and classical resources. As quantum computers get bigger, that amount of classical resource to support the quantum computer gets bigger quickly because there is this exponential gap. If there wasn’t, we wouldn’t be bothering building these machines.

People shouldn’t think of a quantum computer in isolation. You don’t go behind the curtains and tinker with the device. It’s all about really how you bring these resources together and today, that’s the algorithmic level. How do you make this? How do you get around these noisy devices? But over time, that hybridization exists at the HPC level, where you have a general problem that you are then distributing across a wide variety of devices.

Daniel Newman: Yeah, I love what you say about the quantum computing as a whole, not thinking so much about computing processing. We’ve been in rooms with ion trapping machines. We’ve been in rooms of superconductor. It’s not flipping open a laptop.

Patrick Moorhead: Is this on?

Daniel Newman: Yeah. I say that because I think there’s still a lot of that misconception. One of the biggest opportunities still is to just break that mythology between the average person and what quantum can be, and of course, all this stuff happening that you mentioned in research and then eventually it comes. Like you said, the early classical computing machines were very different than what we see today in that same type of evolution. It’ll probably happen faster because of how fast technology is moving. But more than anything, Richard, I want to thank you for joining us. This topic is not over. We have many more conversations and I’m hoping they’ll be with you that we can bring here to talk about the progress. Because this is one of those things that every year, there’s progress being made, whether it’s more fidelity, more quantum supremacy. But I personally like more usability and realistic examples of software, of cryptography, things that are going to be done with quantum.

Richard Moulds: That’s why it’s in the cloud. That’s fundamentally why we felt it’s really, really important to deliver Braket as a cloud service within Braket because it’s all about leveraging the quantum computer in the context of the platform, in the context of all of that compute resources. Our large corporate customers, they live and breathe the AWS platform. They like the tool that exists. When we launched Braket, it was really important that we adhered to all of the security mandates, operational mandates, the integration with the various tools and services that are across the entire ecosystem of AWS, and customers love that. They want to be able to control access to these resources just like they want to control access to databases in EC2.

Daniel Newman: Well, Richard, thank you so much for joining us. We got to end there.

Richard Moulds: You’re welcome.

Daniel Newman: Really appreciate having you at AWS re:Invent, The Six Five on the road at this event. Great guest. Great conversation.

Richard Moulds: That was fun. It’s great.

Daniel Newman: There’s so many more of these to be had. If you’re out there and you’re watching, we have several more of these conversations. Go ahead and click that subscribe button. Join us for all the episodes here and all our other episodes. We love having you as part of the community, but for this one, time to say goodbye.

Patrick Moorhead: Thanks, everybody.

Daniel Newman: See you later.

The post The Six Five On the Road with Richard Moulds of AWS at re:Invent 2022 appeared first on Moor Insights & Strategy.

Their discussion covers:

• How AWS is supporting customers in the media & entertainment
• Post-production in the cloud
• The future of the media & entertainment industry within the cloud

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You can listen to the conversation here:

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Transcript:

Patrick Moorhead: Hi, this is Pat Moorhead and we are live at AWS re:Invent 2022. There’s so much action here. So much fun. Dan, I love being here. The show’s been great for me so far. How about you?

Daniel Newman: Yeah, you know I always love being here. The cloud, the technology, the energy, the people. We’re back baby. And the Six Five is “On the Road.” We are here at re:Invent 2022, in the wild.

Patrick Moorhead: I know.

Daniel Newman: If you can’t tell, this looks like a movie studio.

Patrick Moorhead: Pretty much.

Daniel Newman: But it’s just a news desk.

Patrick Moorhead: Yeah. Are you kind of leading us into the introduction of our guest?

Daniel Newman: Sometimes I try to find these really quippy connections to make it sound like we planned this stuff.

Patrick Moorhead: How about that? Well, let’s dive right in. Katrina, great to have you. How are you?

Katrina King: I’m great, thanks.

Patrick Moorhead: Good. We talked a little bit in the green room, stalking you on LinkedIn, looking at what you do, but you have been in the film industry for a long time. You were a producer before?

Katrina King: I used to be a producer, yes.

Patrick Moorhead: That’s amazing. So, super background, but what do you do today for AWS?

Katrina King: So, I lead strategy and business development for the film industry for AWS, specifically for content production.

Patrick Moorhead: That’s amazing. So, you’re in Hollywood a lot, Bollywood, I mean, all the woods?

Katrina King: All the woods.

Patrick Moorhead: Okay. All right. Maybe a little bit in Austin. I’m from Austin. We pretend to be an editing shop, and a couple movies are made downtown every year, but we try hard.

Katrina King: Sadly, I’ve never been to Austin.

Patrick Moorhead: Well, you should try it out. South by Southwest has a whole week of movies.

Katrina King: I know. I’ll try to make it up there.

Patrick Moorhead: Social, movies and music.

Daniel Newman: Yeah, isn’t that like South By and Con, aren’t they the same?

Patrick Moorhead: Oh yeah, they’re kind of the same.

Daniel Newman: We’re leveling.

Patrick Moorhead: Exactly.

Daniel Newman: We’re level. We both live in Austin, so we’re super proud of where we’re from. Great races, great music, good barbecue, but it’s not really a studio town.

Patrick Moorhead: It’s not.

Daniel Newman: All right, we’ll go ahead and agree with that. But we’re here at AWS re:Invent and obviously you coming over to Amazon to work in this business from being on that other side, would you say behind the camera, where you like to be? Must have been because you saw a pretty exciting opportunity. So production in the cloud, it’s kind of a big thing. It’s an emerging thing and obviously something that used to be really hard to do. So you took the role, talk about why you took the role, why you think this is a big opportunity. Where’s kind of this whole production in the cloud thing going?

Katrina King: So, I took the role because there’s so much opportunity to change the way films are made now in the cloud. And so really over the past couple years we’ve been working to expand the art of the possible and it started with an analysis of what would it take, what were the technological gaps to be able to take an entire feature film and to post produce it in the cloud. And when we started analyzing it, we looked and we saw color and finishing as being the biggest impediment. So we started working on technology in order to be able to enable that because if you can’t finish a film in the cloud, then it doesn’t make sense to ingest your camera assets initially into the cloud because the next time they’re going to be used is during the color and finishing phase.

So by unblocking color and finishing, we actually are able to now enable the entire post-production process to take place in the cloud. And perfect example is the The Lord of the Rings: The Rings of Power. I don’t know if you came and saw the session a couple days ago, but they basically did about the vast majority of their post-production in the AWS Cloud. And so they were really the first major production to really try to do everything in the cloud, including the color and finishing phase.

Patrick Moorhead: Now you had said so for the neophytes, color and finishing, okay, me, why would that be a challenge? Why was it that last step? Is it the amount of data density to get up to the cloud? What is it about those two areas that’s unique and it was a challenge?

Katrina King: So color and finishing is the phase where you’re doing your final pixel. And so in order to be able to do that, you need to be able to see the full fidelity signal. And so that means you can’t compress the video, that means that you can’t encode the video. You need to be able to see that true pixel. And so traditionally, that’s been precluded by a number of factors including data rates and things like that because if you look at a 4K 4:4:4 12-bit signal, you’re looking at about seven gigabits a second. I mean, you would need dark fiber to be able to even do that. And so what we did is we actually took the AWS elemental broadcast tools that were designed to migrate broadcast playout to the cloud. And we took those tools, enhanced them with 12-bit and 4:4:4 chromosome sampling and bit depths.

And then we integrated them into applications they were never designed for specifically Baselight by FilmLight. And so what that basically does is allows us to take a reference grade monitor and connect it to an EC2 instance. We can lightly compress it using JPEG access over SMPTE Standard 2110-22. And then that allows us to be able to take that seven gigabit, compress it about 10 to one for a rough pass and all of a sudden you have a 700 megabit signal that you can actually handle.

Patrick Moorhead: Okay. Now that’s amazing. By the way, I’m going to pretend that I understood every acronym that you threw out there, but all of you video professionals out there, I know you know exactly what she’s talking about. I’m pretty sure our crew knows everything you’re saying right now, but it is fascinating. So you are compressing without losing the fidelity of the videos. Is that the net so easy to upload?

Katrina King: Okay. Yeah, so JPEG Access is an ultra-low latency Kodak that’s designed for broadcast playout. So it’s designed to move signals in and out of the cloud specifically for playout of broadcast. So it’s ultra-low latency. If it’s good enough for the Vegas bookmakers, it’s probably good enough for our artists. And so that allows us to encode it with virtually no latency. We’re seeing 0:1 frames of latency all the way from the region to the actual monitor itself. And additionally, it’s mathematically lossless, they say around three to one compression and visually lossless, which is a problematic term, but somewhere around 10 to one.

Daniel Newman: Got it. That’s really interesting. Makes me think of some of the early video conferencing Kodaks, twice the bandwidth, basically trying to get more bandwidth without using as much data at the same time and keeping people looking high def. This is a whole other level, of course, because now you’re talking about super high definition movie experiences. Because the thing about movies, especially… I know we’re kind of still in this interesting inflection point between do we watch movies at home, do we watch them on our mobile devices? Do we go into theaters, and clearly, we still like to go to theaters. That’s where this perfection really becomes a thing. I mean the TVs at home are great, the 4K, 8Ks, Amazon has a good one.

In the theaters, it has to be perfect. And I guess I’m just kind of thinking through, you’re talking to studios, you’re talking to the producers, you’re talking to directors and they’re coming to you and saying, “I see a lot of value in the cloud. It could make it simpler, make it faster.” And you want to get them out quicker. But the old way is safe. They know it works, they know it’s the no loss in fidelity. How is that transition happening? Are they buying in, are they nervous? How are you seeing that happen?

Katrina King: Well, I mean a lot of things changed in the last couple years and it was largely because for necessity, people had to start working from home and all of a sudden people were willing to take risks that they were never really willing to take before. A lot of innovation took place in the last couple years and that really kind of proved the model to people because now people don’t want to go back into the office. Companies are divesting themselves to brick-and-mortar facilities, they’re enabling their workforce to work remotely. And a lot of the innovation that took place over the past couple years in order to be able to enable that remote work is now becoming the new defacto norm.

Patrick Moorhead: That’s incredible. Anything tangible and big has some sort of a vision and you had talked about MovieLabs 2030 Vision. What is that and what is the future all about? Can you talk us through that?

Katrina King: Sure. So the MovieLab’s 2030 Vision. MovieLabs is a consortium of the Hollywood studios. And they got together and they wrote a white paper that basically said, what do we want the state of technology to look like in the year 2030? And so there’s 10 different principles. And they’re very cloud-centric. So principle one is assets are ingested straight to the cloud. Principle two applications come to the content. So that means running all of your different workloads on elastic compute instances and accessing that content that’s centrally stored. Principle three is propagation of assets as a published function. So that means that, for instance, when our assets are moved up to simple storage service, we have a series of lamb to functions that are triggered to be able to move that to a central store. And this is exemplified with what we built at IBC this year, which was a holistic studio in the cloud that MovieLabs recognized as having achieved five of the 10 principles.

Principle six is central authentication, and then principle 10 is real-time iteration and feedback. And so in order to achieve that one, all of our different workloads were interconnected using Moxion, which is a review and approval tool. And so we had our editorial, our pre-visualization, our color and finishing, all of these different workstations were fed directly into Moxion where the director could actually see over the shoulder of every single artist on their entire production. And so MovieLabs is very much the North Star. It makes my job easy because the studios basically said, “Here’s everything we want over the next 10 years.”

Daniel Newman: So to simplify that though. What are the key benefits? So I get it technologically, it’s advancing. Is it about faster production? Is it about that apps built in the cloud are going to be more effective than your legacy tools for editing? Not editors here, haven’t done a lot of CGI work myself, but are there measurable benefits or are they just prognosticating that everything’s going to be more flexible In the cloud?

Katrina King: There’s a number of different benefits. So it starts with content security. If the assets are all in cloud, then you don’t need to worry about assets moving around. There could be dozens if not hundreds of different companies collaborating on a film. And so those assets are bouncing around all over the world. If they’re staying in the cloud, then you’re able to apply layers of security to them that it’s significantly reduced the chances of content link. It also enables a global workforce. So artists all over the world can collaborate without having to relocate, without having to move. And so it just really creates process technological efficiencies that ultimately empower artists in a way that they weren’t empowered before.

Patrick Moorhead: No, that’s cool. No, I like that. I love that. So as we’ve seen in all parts of IT, the needs of the people who are out front, dare I call them cloud-native folks, and then you have some of the laggards who are just kind of getting going. And I even remember Andy Jassy was on CNBC and I think he was saying that, “Hey, we’re only 10% of the way there.” My question is, how are you anticipating the needs of both kinds of customers? And I know there’s strata of everybody in between. How are you meeting those different needs?

Katrina King: Well, you’re not suggesting the industry is resistant to change, are you?

Patrick Moorhead: Everybody is. No, I mean we’re conservative and businesses by nature are conservative, and in IT many times, you don’t get the award for being the superstar. You get the award for not screwing up. And that’s why there’s just so much conservativeness and gosh, when you get to movie making, I mean that is the core of the business. It’s kind of like a manufacturing with ERP. This is that important and vital.

Katrina King: If you can create process efficiencies, it ultimately allows artists to put more money in front of the camera instead of behind it. And nobody wants to take anything in front of the camera and put it behind the camera. I mean, that’s producing one on one. We’ve really proven the model now over the past couple years. It started with the mandated work from home and that really proved that some of these workloads can be efficiently done in the cloud.

And again, The Lord of the Rings: The Rings of Power really proved that you can do an entire show in the cloud.

Patrick Moorhead: It’s amazing.

Katrina King: They refused to shut down production and they just kept on going. By the next week, they just had everybody working remotely and they ended up innovating and proving the model. And in doing so, they ultimately created a new normal for artists where they don’t necessarily need to go into the office anymore. They don’t necessarily need to relocate for a production. And so I think that we’ve seen an acceleration of this trend over the past couple years that probably organically would’ve taken another five. But now we’re really there.

Patrick Moorhead: Right.

Daniel Newman: It’s interesting to see things get sped up and, of course, we did see it with the pandemic. I’m kind of interested in when that first movie is going to be made, not an animator or anything that’s going to be like no one was physically together, yet the whole thing looks and feels… Because I’m sure we’re getting closer to that, the way we are able to insert into the graphic environments and stuff like that where people aren’t always in the physical scenes in the same ways, but what do you think about the process forward? So of course, you’ve got the innovators, you’ve got those that have been less reluctant, they’ve moved forward, they’ve gone ahead, but you’ve got tons of movie makers, tons of content, and the creator universe is growing. How do you speed up adoption? What is the AWS strategy to get more buy-in, get more studios, to get more creators to see the value in what you’re building?

Katrina King: So, we started by unblocking the key workloads that precluded holistic production in the cloud. And so now we’ve got the tech stack necessarily to be able to do this. And so our strategy is to start with smaller productions, prove the model, expand, expand, expand. So start off with smaller films that have less demanding requirements and slowly work up until we can see the biggest blockbusters in the world. And I know that that’s contrary to what I just said about Lord of the Rings, but you’re absolutely right, there is a reluctance to change in the industry. And so we start off small and we really make sure that we’re ironing out all the wrinkles in the entire process. And then we expand and expand and increase the requirements.

Patrick Moorhead: As analysts, we think everything’s easy. We wave a magic wand and this is the way it’s going to be, but what I do know is that markets move based on two things and one is risk and the other is opportunity. And I think if a studio, it would seem to me if a studio showed up and they were just radically profitable and a lot of that was the behind the camera, a portion of it, or they could take money that they didn’t have to invest in behind the camera could do in front of the camera, that it would show like, “Hey, what’s the magic of this studio? Or what’s the magic of this creator?” And I think once people start talking that says, listen, the cloud is vital, the AWS Creative Cloud, I mean, it seems like that would be something to get the ball rolling where the magic is, the cloud capabilities. Does that pass your muster at all?

Katrina King: It does. It’s really funny. I was talking to somebody yesterday and I was trying to explain all of the different ways that you can save money in the cloud and things that you wouldn’t even think about. So for instance, you’re producing a show and you have an editor, and you’re shooting in Toronto. So you fly your editor to Toronto, you put them up in a hotel, so you have to pay their travel, you have to pay their per DM, you have to pay for their accommodations. You can take all of that and put that right in front of the camera by having that editor stay at home and work remotely. So, there’s all of these little efficiencies that can be created using the cloud in order to really enable that global workforce. And at the end of the day, to really allow artists to focus on the art.

Daniel Newman: I feel like it’s orders of magnitude. As I just kind of do the math, there’s got to be some pretty significant ROI or improvements in terms of efficiency. Now again, that a hundred million-dollar studio project, you can, like you said, take it from behind and put more in front. Alternatively, you can also put more in your… Well, I’m saying make high quality content. We’ve seen with CGI what you can do. You don’t need the same sets and effects that you used to physically need now because of what can be done. Is there a business model here that you’re seeing? I mean, are studios kind of also going? Because the risk is different, box office numbers are different now. The monetizations are different with streaming and how long people are going to go pay for tickets versus… And I guess my point is this, high quality, high production that can be now done at much lower cost, that has to be a part of what they’re asking you to help them build.

Katrina King: Absolutely. And I mean virtual production is a great example. I mean, films used to shoot two, three pages a day. I’ve heard tell of production shooting 20 pages a day on virtual production volumes. And so leveraging things like our G5 instances to be able to do pre-visualization, checking those assets into Perforce, for instance, running on the AWS Cloud, that can be brought down to a virtual production volume. And then you can create all of these… You can end up shooting twice as fast and really focus on the production itself. Everything is changing so dramatically right now, not only with the way that the cloud is being leveraged, but even the production processes themselves using virtual production, pre-visualization, all of these sorts of technologies.

Patrick Moorhead: This conversation has gone in a different direction. It’s funny, I was thinking that you were going to drive the snowplow-

Daniel Newman: Snell.

Patrick Moorhead: The one semi that you drive, it’s so much data that you drive-

Katrina King: Snowmobile.

Patrick Moorhead: Snowmobile. I was envisioning, okay, this is the way, and then you pull out this magic coder process. I know it’s more complex than that, but you pull that out because that is the major piece. Now I’m asking myself, “Where does Local Zones fit in?” Because I know I think the first Local Zone was in Los Angeles. If you’ve found the magic way to encode all this stuff, I mean latency doesn’t become an issue. Is that correct?

Katrina King: True. So two-part answer. With regards to the Snow family, I think we’re past shipping things around now. We have partners like PacketFabric and Sohonet that allow us to be able to enable stages with high amounts of bandwidth. So rather than having to ship a device across the world now, you can just release 10 gigabits a second for run of show on a sound stage, and you can ingest all your assets straight to the cloud. With regards to Local Zones, Local Zones enable single-digit millisecond latency for artists experience. And they’re strategically placed in places like LA where you have a lot of median entertainment workloads. And that allows artists to have virtually the same experience that they would have with a cheese grater sitting under the desk.

Patrick Moorhead: Okay. Sure. Lovely.

Daniel Newman: Obviously know what the cheese grater is.

Patrick Moorhead: That’s great.

Katrina King: That’s trash can now, actually.

Patrick Moorhead: Yes.

Daniel Newman: Well, in the end, the technology is going to revolutionize this business for sure. And as a movie connoisseur, Patrick knows me, I like to quote movies all the time.

Patrick Moorhead: We’re sending memes back and forth just based on movies and shows.

Daniel Newman: We legitimately do business with memes.

Patrick Moorhead: Ridiculous. Yes.

Daniel Newman: Some of our favorite shows like Succession, we just love it always. Billy’s always at fault to come from a movie, but it is really a way that the world connects through entertainment. I mean, you have that background. It’s interesting to see this intersection. It used to be about big avid booths and lots of hardware and technology and very specialized equipment. And now you’re seeing just like every other industry… Pretty soon, we already know the creator. They just use a mobile phone and a little gyro and they’re creating. And sometimes you’re really impressed. But I’m saying the volume, the veracity, the intensity of this industry is going to get big. And the fact is everybody kind of becomes a creator. And what you’re building now is new studios will come out of the woodwork disruptively just like we saw with new production through Netflix.

Patrick Moorhead: That’s right.

Daniel Newman: And it used to be only studios and all of a sudden Netflix, now they’re making content and now everybody followed suit.

Katrina King: I like to call it the democratization of post-production. I mean really if you look traditionally, the amount of investment that you would have to make-

Daniel Newman: She said that faster.

Katrina King: In order to build out a post-facility, you need to invest in so much hardware. And that ends up creating barriers to entry for new entrants into the marketplace. And by leveraging the cloud, you can use the resources that you need when you need them, then you can spin them down, stopping for them. And it allows smaller players to enter and really start competing with the bigger players in the marketplace.

Patrick Moorhead: Katrina, this has been a great conversation. It took a lot of twists and turns, which I love and the audience loves because it just makes great content and great stories. So, thank you so much for coming on the Six Five. We really appreciate that.

Katrina King: Thanks for having me.

Patrick Moorhead: Absolutely.

Daniel Newman: All right everybody, there you have it. We’re here at re:Invent 2022. This is the Six Five “On the Road” for Patrick, for myself. We appreciate you tuning in. Hit that subscribe button, watch all those other episodes from here. And of course, all our episodes because they’re all good.

Patrick Moorhead: They pretty much are.

Daniel Newman: Yeah, we are. And have a great week. We’ll see you all there. Bye now.

The post The Six Five On the Road with Katrina King of AWS at re:Invent 2022 appeared first on Moor Insights & Strategy.

Their discussion covers:

• Announcement of SageMaker & Omics
• Progress on Amazon Monitron
• Key trends driving machine learning & artificial intelligence

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You can listen to the conversation here:

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Transcript:

Patrick Moorhead: Hi, this is Pat Moorhead and we are live at AWS re:Invent 2022 and it is rocking Daniel and I. We are talking about our most favorite thing when it comes to tech and that is the cloud. Dan, how are you?

Daniel Newman: I’m doing great. It’s always great to have the Six Five on the road here at AWS. It’s such a popular conversation, the topic, cloud, the enterprise, digital transformation. It’s all happening here. By the way, just trying to get down here, you’re bumping shoulders, you’re running in because I think everybody in IT is here.

Patrick Moorhead: Pretty much. I mean, I always gauge all shows here by CES crowds and this is a monster show and you know when you can barely walk through the hallway, something is going on and a huge attraction. One of the biggest attractors in IT and digital transformation is taking data and doing meaningful things with it through AI and machine learning. We happen to have a repeat guest here on the Six Five. How are you doing, Bratin?

Bratin Saha: I’m good. Thank you for having me. It’s always nice talking to you.

Patrick Moorhead: Yeah, I mean last time we talked, we had this great conversation about democratizing AI, making it simple for people, putting it in the hands of, I don’t know, people like Daniel and I even, right, who don’t necessarily get down to the metal on a GPU or a Trainium or an Inferentia, but abstracting it so even the citizen developer can get access to it. But we are here, it’s another event. Big announcements.

Daniel Newman: Yeah. We had the chance to talk to Bratin actually at re:MARS. So this is a second time sitting down. You’re now a Six Five alumni and you have a big mandate at AWS and I would love for you just to quickly talk a little bit about the work you do for the company, just to set the context out there because we’re going to get into everything Pat just said, but you are highly qualified.

Bratin Saha: Well, I hope so. Thank you for the kind words. So at AWS, I run all of our AI and machine learning businesses, the AI services that’s at the top of the stacks, SageMaker, that’s the middle of the stack, and then the deep learning armies and engines that’s at the bottom of the stack. We built one of the fastest growing businesses in the history of AWS. More customers doing machine learning on AWS than anywhere else. So it’s been a fun ride so far.

Patrick Moorhead: What have you done with your life? I mean, I’m like what he’s done with his career, I’ve done with, I mean-

Daniel Newman: Are you really going to make me assess this and-

Patrick Moorhead: I don’t know. I mean we’re live.

Daniel Newman: I’ve written seven books.

Patrick Moorhead: Okay, I got you. You’re good. But you don’t run the largest AIML cloud service on the planet.

Daniel Newman: That’s why he’s on our show. He’s the guest.

Patrick Moorhead: Okay, let’s go. Sorry, keep going, Dan.

Daniel Newman: Oh no, absolutely. So let’s start with the launches. I mean look, we like to have a little fun, little banter here, but this is really important stuff and the problems you’re working on solving are big problems that pretty much every company, every government entity, universities, everyone’s kind of looking to AWS at least as part of the solution. So what’s your big exciting moments here that you’re announcing this week at re:Invent?

Bratin Saha: Yeah, so when we look at machine learning, one of the things we look at is what are the key trends that drive machine learning innovation? If you are a customer, what should we be paying attention to? What should we be leveraging? Two of them, we talked about at re:MARS, ML industrialization, ML democratization. I think they remain important. We are going to continue to push them.

Then there is this new class of models that are coming up called foundation models and we use them internally at Amazon. Just to give you an idea, if you went back to say 2017, the state of the art models had 20 million parameters. 2019 they had about 300 million parameters. Now they have more than 500 billion parameters, right? So that’s 1600 times just in three years. That’s super, super, super. What these have done is they have opened up amazing capabilities. You’re now looking at a world where potentially in the near future, you could have AI and machine learning be an assistant for creative tasks, okay?

And so, we have these generative models now on AWS and SageMaker, stability.ai that creates the most popular foundation models. Now, they have chosen AWS as the cloud. They’re using SageMaker and there’s just an enormous amount of innovation that’ll come out of that. I’m really excited by the capabilities that we are going to open up for our customers there.

Then there is this variety of data. We have talked about the data, but what I’m talking about is the variety of data, multi-modal data. One of the things that we launched is SageMaker’s geospatial machine learning capabilities. What we had up until now was machine learning was good at answering the who and the what, but you couldn’t really answer the where and the when, okay?

With this geospatial launch, you can now answer the where and the when. And so we have customers like BMW, who are actually using it to say, “Where should I be having electric charging stations?” We have customers like Zario that are using it for agriculture like, “How should I be planting? Where should I be planting?” And so on and so forth.

Then the other thing that I’m really excited about that’s really taking off now in terms of maturity is machine learning powered use cases, so document processing, contact center Monitron, which is industrial equipment. If you think about what we are doing with contact center automation, today, what happens is when a customer is calling in to a call center, then contact center supervisors listen in on a fraction of the calls to see is the customer satisfied, do they need more information, but they can’t listen in on every call. What we have done now is we have enhanced Amazon Transcribe, which is used for contact center automation and we’re using speech recognition models to detect customer sentiment. So, we can detect things like raised voices or someone saying repeatedly, “I want to talk to a manager,” or someone saying, “I want to cancel my subscription.” That’s a clear indication of a frustrated customer. When that happens, you can send a real time alert to the contact center supervisor.

Patrick Moorhead: Okay. So we have talked, I’m going to ask a question of, I mean there’s so much content coming out here. First of all, for the neophytes in AI and ML out there, what are the benefits of a larger model?

Bratin Saha: So you can think of what a larger model does is it has more learning capacity. I’m kind of simplifying the thing because ultimately, you want a model is important and the data is important, the amount of data that you’re training it, right? Now, all things being equal, when you have a larger model, it has more learning capacity. What that means is I’m able to learn more from data, I’m able to identify more patterns and I’m able to make better predictions.

Patrick Moorhead: Now, I appreciate that. For, let’s say Monitron, now Monitron’s been out for two years now?

Bratin Saha: Couple of years ago.

Patrick Moorhead: Couple of years now. Do large models help things like Monitron?

Bratin Saha: Large models in general are applicable to a variety of places. Monitron could be one of them. The special thing about Monitron is it’s its own solution. You need no machine learning expertise for it. It comes with its own sensors, gateway and all that. So you plug the sensors to your equipment, they stream your equipment’s temperature and vibrations and then machine learning models detect problems with your machines. And so we have customers like Coke Industries using it, Baxter using it. Digital transformation is a cliche, but I think it’s one of those things that’ll really transform industry of manufacturing.

Patrick Moorhead: Yeah. By the way, not even just from having smarter factories and smarter manufacturing, smarter transportation, but completely changing business models because, when you know and you can predict what’s going to happen before it happens, you can get into doing some of the things that you might sell as a good, selling as a service, right?

Bratin Saha: A lot of just in time things can become…

Patrick Moorhead: In fact, you might design it to be different if you’re selling the widget versus offering the widget as a service. You might make it last longer. You might make it last less time because you know exactly when that thing needs to be replaced or serviced as opposed to a person coming out every month, checking it out, and by the way, half the time it’s going to be wrong. I’m just imagining every servo motor, every thing that goes around and makes a noise inside of a distribution center or a manufacturing plant. I feel like that’s game changing.

We see a little bit of that transformation as a service in odd things like oxygen. There’s a company that I interviewed two or three years ago that used to supply oxygen bottles to a manufacturing site and now it’s as a service and putting sensors everywhere to make that happen and their profit margins have gone up, their customers are incredibly happy because all they want is oxygen. Some of these things aren’t necessarily sexy. I think they’re sexy because it’s moving forward, making more money and tech doing cool stuff, but I like Monitron because it’s so transformational in industries that haven’t transformed yet.

Bratin Saha: Yeah. It makes a lot of things easier. Like you said, I don’t just have to guess what is going on. I can really be predictive about things. The other thing we have is Amazon Textract that we use for document processing. Now, a lot of customers have been telling us we would like help with mortgage processing because a mortgage loan package can have 500 pages and it can take 45 days to close. Almost half of that time, 20 days, is just extracting information from these pages. And so, what now analyzed lending does is you give it a form and it extracts all of the relevant information and not just that. It can find pages that require review by human underwriter. So if you have a mortgage form and it’s missing a signature, it can flag that page for an underwriter.

Daniel Newman: It’s literally unstructured data becoming structured data in real time.

Bratin Saha: Exactly. It’s a great analogy actually because I almost think of it as people talk about no ETL and all that. This is basically taking unstructured data and converting it into information. It’s kind of doing what you’re saying and this whole notion of machine learning powered use cases, and we have all talked about how it’s going to transform industries. Now, that’s really happening. Pennymac used to spend hours every day processing documents. Now, they can process 3000 page PDFs in less than five minutes.

Patrick Moorhead: That’s amazing. Another great example is in our healthcare system in the United States, we ship most all of those papers to a different country to be processed. That is crazy, takes a long time, and also as the frustration of a customer thinking they paid and they didn’t or there’s a transcription error and it makes a mistake on even some sort of an outcome. So, there’s this time delay and it costs money. In a perfect world, it would all be digital, but because every state, every county, every country, every region has different rules for it, it could be 50 to a hundred years before we move off of paper.

Bratin Saha: And they have different formats and the same templates don’t work. With AI, all of this goes away because you’re just semantically getting information out.

Patrick Moorhead: Well, and the smarter it is. So for instance, we’ve had robots in manufacturing, gosh, I worked in a factory 35 years ago and we had robots. But the big difference though is the cost and time to set up and the tolerance errors were nil. But with today’s machine learning and the ability that the machine acts more like a person and if that window is slightly off, it knows to-

Bratin Saha: Adjust.

Patrick Moorhead: … to adjust. That is brand new and the ability to even get into a 5G connected, instead of you can actually move the devices and the robots around. That’s very similar to, in an odd way, is this Textract and what it’s doing because it’s taking this old brownfield type of environment and modernizing it, not by completely just A, you need to throw everything out and start over, but living in the reality of where we are today and making it better. That is pretty awesome.

Bratin Saha: Yeah. Every form has handwritten stuff. Templates don’t work on that. That’s why you need AI. All of these have corner cases that don’t work. That is where bringing in AI, making the corner cases work, makes the solution [inaudible 00:14:21], and that is what makes it possible for widespread deployment. I think that notion of it’s no longer a brittle thing that only works in one scenario, it’s something that generalizes and actually works in the real world, I think is transformational.

Daniel Newman: There’s so many instances of where you’ve got all this amazing technology and all this compute and processing and this killer UX and then in the end, it ends up printing out a 500 page mortgage that ends up being at the end of this thing. So you can do the whole process on an app on your phone and it can actually read your credit. But in the end, like I said, there’s still some analog manual process that just destroys-

Patrick Moorhead: Flood…

Daniel Newman: … the whole experience. By the way, destroys their sustainability, because all that printing and paper is totally unnecessary. I mean you got industries like real estate mortgage, you got industries like automotive where they still print all the forms every time. I mean, they still have the dot matrix printers in automotive dealers because that’s-

Bratin Saha: Healthcare, if you look at Health,  Health has used Textract for automating the claims processing. They have been able to automate almost 90%. Anthem has been using Textract for automating. So like you were saying, there’s the automation aspect, the improved productivity aspect, the less paper aspect, the “I can go invest in other things,” aspect. So it’s really transformational.

Daniel Newman: So let’s talk about a smarter application because I got something, you guys were talking kind of about these kind of dull and boring applications, but I think someone alluded to the word supply chain at some point in this conversation. I really do wonder with someone like yourself that thinks about this all day long, we’ve had a massive problem fixing it. I think some of these are structural, some of them are policy. But where does something like AIML help in what we’ve been through in the last two years? Where do you start to see, I’m sure you guys are involved in this, solving problems to get stuff where it needs to go faster?

Bratin Saha: I think there’s a lot of machine learning and AI we can apply to do things like congestion management. A lot of this has been congestion, things just being congested and moving things flow, better prediction of demand supply, where does it need to go to relieve bottlenecks? So I think we can do a lot of that application to just make the thing flow a lot smoother.

Patrick Moorhead: Oh, you did make a major announcement here at the show called Amazon Supply Chain. So using AIML, so that’s-

Daniel Newman: I was keying him up.

Patrick Moorhead: Oh well, okay.

Daniel Newman: No, you did good. You did it, you did it, you did it.

Patrick Moorhead: Which is just smart. I mean ETL out of SAP or Oracle and pull it in there and start having fun. I did want to talk about Omics. Am I saying it correctly?

Bratin Saha: Yeah.

Patrick Moorhead: I mean that seemed to be a giant. By the way, we’re running out of brand names. That’s why these names start to get funkier and funkier. But tell us about Omics. What does it do? What problem is it solving?

Bratin Saha: So fundamentally what we are trying to do with Omics is making it easier to do analyzing genetic information. Now, there’s genomes, which is genomics, but then there’s also other forms of analysis you can do like proteomics and other stuff. Fundamentally, what a lot of our healthcare customers want to do, especially in the field of precision medicine, is they want to be able to look at genetic information, correlate it with your health information, and then use that for personalized treatments or other forms of therapeutic stuff that is personalized to your needs.

Patrick Moorhead: That is absolutely the future.

Bratin Saha: Of medicine.

Patrick Moorhead: That is an absolute global game changer.

Bratin Saha: Yes. It promises to make things a lot more effective and it’ll probably make it a lot more efficient. So what Amazon Omics does, you can think of it as genomics processing as really kind of three things going on. One is you have a genomic sequencer that spits out all of the genomes. Then you are taking the genomes and you’re comparing it with what we call reference genomes.

So let’s say I’m taking a person’s genome and I’m comparing it with the reference and I’m seeing where the two of them differ because then I know, “Okay, there was a mutation here.” So those are called variants. So once I’ve found all the variants, all the mutations, then I try to correlate it. I say, “Okay, there is a mutation in this position that might have resulted in this thing going on.” So what Amazon Omics does is it makes it really easy for you to go from genomes down to these variants and then we have Amazon health leg that you can use for storing electronic health records and you can use for storing X-rays and so on. So now you can go in and you can do an analysis and say, “Okay, here is the genetic information, here is the electronic health records. How do I correlate that to analyze the two and get to personalized treatments?”

Patrick Moorhead: Okay, dumb question, but I have to ask, I’m assuming it’s AI and ML, is it truly a big data problem?

Bratin Saha: The Omics one or-

Patrick Moorhead: Right, Omics?

Bratin Saha: Yeah. These genome files can be petabytes.

Patrick Moorhead: Okay, thank you. I didn’t know that, but thank you.

Bratin Saha: They can be petabytes. So you’re looking at the human genome as 3 billion bases, but typically we’ll over sample it, and so you can have billions of bases and that’s petabytes of data and then you’re analyzing that and then trying to see where are those variants.

Daniel Newman: It’s super interesting. I’m kind of sitting here trying to visualize putting, I’m like building it, I’m trying to put it all into a container and moving around. I’m kidding. But the direction of Amazon, by the way, and you leading in the role you are is clearly going more towards healthcare. Obviously-

Bratin Saha: I don’t know if I would call it going towards. It’s not quite going towards healthcare. This is helping our healthcare customers, providing them the compute, the AIML, the storage capabilities so they can innovate faster, right?

Daniel Newman: But I mean the company’s made a lot of investments. The portfolio of Amazon is diversifying. They’ve made some big buys in everything from primary care. I mean, so the company sees healthcare as a big future and obviously AWS, let’s face it, I think you built AWS a little bit to solve, company’s got a history of building new businesses to solve other business problems that it had. AWS was a bit born out of solving its own problems.

Bratin Saha: 90% of our roadmap is really based on what our customers tell us to do. Especially in the case of Omics, we have been working with a lot of our healthcare customers and they say, us building this infrastructure for compute, now think of this petabyte storage. You need a storage infrastructure to do it efficiently. You need a query infrastructure to query those petabyte size files. And so what they’re saying is, “There’s a lot of infrastructure mark that we have to handle. Why don’t you guys do all that undifferentiated infrastructure work so we can focus truly on the healthcare work?” So we aren’t really doing any of the healthcare discoveries here. We aren’t really doing any of the healthcare domain specific stuff. We are simply building the infrastructure so that our customers can get away from building infrastructure and focusing on the healthcare solutions.

Patrick Moorhead: The last question that I think we have time for here is, by the way, I’m really enjoying this discussion.

Bratin Saha: Thanks.

Patrick Moorhead: This is great. What are the different ways that your customers can build applications for AI and ML?

Bratin Saha: So we see our customers approaching machine learning in one of three ways. There’s some sort of customers who say, “You know what? I’m going to build my own machine learning infrastructure, my own machine learning models and their own apps.” For them, we just give them optimized hardware and optimized software, and then they build the whole stuff. Then there are most customers who say, “I don’t want to get into the infrastructure. I just want to build the machine learning models.” And so for them, we have SageMaker where we build the infrastructure, they build the machine learning models. Then we have some customers who say, “You know what? I don’t even want to be building those machine learning models. Give me APIs that I can use and I’ll use them in my applications, document processing, personalization and all that.” And so for those customers who don’t want to build models, who don’t want to get into machine learning, we have this higher level API AI services. So we cater to all of these customers and we meet them where they are.

Patrick Moorhead: I mean, as analysts, we have to put everything in a box and then put a name on it and then many times we don’t even use the same names. But at least we have narrowed in on IaaS, PaaS, and SaaS. I like your three layer cake. In fact, I wrote about it years ago, and I like the consistency that you’re bringing to the table. I think Daniel and I both believe that simplicity is really going to be the thing that makes the change. So more PaaS and more SaaS to simplify it. SageMaker is dramatically improving every element of the step from Dataproc, prepping all the data, garbage in and garbage out, all the way to running the models on the smallest little machine on the edge. So-

Bratin Saha: AstraZeneca, they migrated to SageMaker and they were able to reduce the lead time to do machine learning from three months to one day. Three months to one day.

Patrick Moorhead: That’s just mind boggling.

Bratin Saha: Mind boggling.

Patrick Moorhead: I mean, there’s very few things in life that have an order of magnitude shift like that. So-

Bratin Saha: But that’s to your point, which is there are various levels of simplification and us simplifying the infrastructure part with SageMaker or the infrastructure thing with Trainium and things or even the AI layers-

Patrick Moorhead: Little editorial, and sorry to hog this here, but I think really what’s missing is a lot of enterprise apps that are, nothing’s off the shelf, but let’s call it pick your favorite enterprise SaaS app, really needs to get on board quicker to be able to enable it. Because there’s some enterprises, they’re not going to go there, right? They’ve SaaS-ified and born in the cloud. I mean, they’re almost all SaaS, small businesses, right? Headed to SaaS. I think that is one of the bigger breakthroughs that as an industry, we need to roll that forward. I’m going to look forward to what you can do there.

Bratin Saha: Sure. Happy to. Oh, go ahead.

Daniel Newman: No, I was just going to say, I just really appreciated the opportunity. I was thinking as you were saying that, in the end, it’s going to be a lot of people just want to use NLP to ask you to query and then a query finds the data, creates the model, starts outputting data and visualize. I’m saying that is kind of the future. There’s going to be a small subset of PhD types that have been doing this for decades. A lot of it’s going to be me going, “Where should I be sourcing these materials from? I should be able to,” and it’s going to go out and the software’s going to write itself.

Bratin Saha: And in fact, if you look at a lot of the latest generative stuff-

Patrick Moorhead: My favorite kind of software.

Bratin Saha: … then prompts and you’re just asking questions and the thing is generating it. Now with Code Whisperer, it can generate code like a developer can do. So all the things that you guys are talking about is coming to fruition.

Patrick Moorhead: By the way, I don’t love all your names, but I love Code Whisperer.

Bratin Saha: Thank you.

Patrick Moorhead: Can I just tell you that? Anyways, more editorial. Who’s asking the questions here?

Daniel Newman: I don’t know.

Patrick Moorhead: Just a guess.

Daniel Newman: I’m going to interview you. But Bratin, thank you so much for joining the Six Five here-

Bratin Saha: Thank you. It’s always a pleasure, always a pleasure.

Daniel Newman: … on the road at AWS re:Invent. It’s great to have you. You are an alumnus. You will be coming back more often.

Bratin Saha: Happy to.

Daniel Newman: We’re going to make sure to have you on the show.

Bratin Saha: Happy to.

Daniel Newman: Everyone out there, there’s a lot to learn here. You may have to watch this one twice, maybe three times. But we really do appreciate you tuning in. If you like what you heard, hit that subscribe button. Join us for all of our sessions here for Six Five on the Road at AWS re:Invent. We have a lot of great guests, a lot more great content. But for this one, it’s time to say goodbye, Pat.

Patrick Moorhead: We’re out of here.

Daniel Newman: We’ll see you later.

The post The Six Five On the Road with Bratin Saha from AWS at re:Invent 2022 appeared first on Moor Insights & Strategy.