Amazon CEO Jassy's shareholder letter talks AWS' approach to generative AI

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Amazon CEO Andy Jassy said AWS is underway building "primitive services," or discrete building blocks, for generative AI and that approach will ensure customers bring more workloads to the cloud service.

Jassy’s shareholder letter landed as Amazon appointed Andrew Ng to its board of directors. Ng is managing general partner of AI Fund. He was also the founder of DeepLearning.AI, co-founder of Coursera and an adjunct professor at Stanford. Ng also has worked with Baidu and Google Brain.

In his 2023 shareholder letter, Jassy spend a good amount of space talking about generative AI and AWS services. Jassy walks through how primitive services were in Amazon's 2003 Vision document and how AWS' approach emerged from a partnership with Target in the early 2000s where Amazon was the back end to Target's web site.

"Pursuing primitives is not a guarantee of success. There are many you could build, and even more ways to combine them. But a good compass is to pick real customer problems you’re trying to solve," said Jassy, who noted that this approach to primitives guides everything from logistics to supply chain to stores to Prime delivery to AWS.

Jassy said AWS is designing a set of primitives focused on the layers of generative AI. The bottom layer is compute with Nvidia and Amazon's in-house processors. SageMaker, which is for customers building their own foundational models, is another service that's driving AI workloads. He noted Workday has cut inference latency by 80% with SageMaker.

The middle layer is where Bedrock will come in. Jassy said:

"What customers have learned at this early stage of GenAI is that there’s meaningful iteration required to build a production GenAI application with the requisite enterprise quality at the cost and latency needed. Customers don’t want only one model. They want access to various models and model sizes for different types of applications. Customers want a service that makes this experimenting and iterating simple, and this is what Bedrock does, which is why customers are so excited about it."

• AWS' Matt Wood on model choice, orchestration, Q and evaluating LLMs
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• AWS launches Amazon Q, makes its case to be your generative AI stack

Regarding the application layer, Jassy also outlined AWS approach. He cited services such as Amazon Q, Rufus, Alexa and other applications, but noted most applications will be built by third parties. AWS' spin on the application layer is worth noting. Jassy said:

"While we’re building a substantial number of GenAI applications ourselves, the vast majority will ultimately be built by other companies. However, what we’re building in AWS is not just a compelling app or foundation model. These AWS services, at all three layers of the stack, comprise a set of primitives that democratize this next seminal phase of AI, and will empower internal and external builders to transform virtually every customer experience that we know (and invent altogether new ones as well). We’re optimistic that much of this world-changing AI will be built on top of AWS."

Jassy also noted that AWS' move to help customers save money will pay off in the long run and deals are accelerating along with renewals and migrations.

Other takeaways from the Amazon shareholder letter:

Processes matter as Amazon has discovered in its robotics efforts in its fulfillment network. Jassy said:

"There are dozens of processes we seek to automate to improve safety, productivity, and cost. Some of the biggest opportunities require invention in domains such as storage automation, manipulation, sortation, mobility of large cages across long distances, and automatic identification of items. Many teams would skip right to the complex solution, baking in “just enough” of these disciplines to make a concerted solution work, but which doesn’t solve much more, can’t easily be evolved as new requirements emerge, and that can’t be reused for other initiatives needing many of the same components. However, when you think in primitives, like our Robotics team does, you prioritize the building blocks, picking important initiatives that can benefit from each of these primitives, but which build the tool chest to compose more freely (and quickly) for future and complex needs."

Amazon has built primitive services for everything from storage, trailer loading, pallet mobility and sortation along with AI models to optimize those parts.

Lowering the cost to serve. Jassy said Amazon has plenty of room to continue to lower costs for consumers and its margins. "We’ve challenged every closely held belief in our fulfillment network, and reevaluated every part of it, and found several areas where we believe we can lower costs even further while also delivering faster for customers," said Jassy. "Our inbound fulfillment architecture and resulting inventory placement are areas of focus in 2024, and we have optimism there’s more upside for us."

Meta launches latest chip for AI workloads

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Meta launched its next-generation training and inferencing processor as it optimizes models for its recommendation and ranking workloads.

The second version of the Meta Training and Inference Accelerator (MTIA) highlights how cloud hyperscale players are creating their own processors for large language model (LLM) training and inferencing.

Intel launched its Gaudi 3 accelerator on Tuesday to better compete with AMD and Nvidia. Google Cloud outlined new tensor processor units and Axion, an ARM-based custom chip. AWS has Trainium and Inferentia processors and Microsoft is building out its own AI chips. The upshot is rivals to Nvidia as well as huge customers such as Meta are looking to bring costs down. Why enterprises will want Nvidia competition soon

• Nvidia Huang lays out big picture: Blackwell GPU platform, NVLink Switch Chip, software, genAI, simulation, ecosystem
• Will generative AI make enterprise data centers cool again?
• AWS presses custom silicon edge with Graviton4, Trainium2 and Inferentia2
• Intel's AI everywhere strategy rides on AI PCs, edge, Xeon CPUs for model training, Gaudi3 in 2024
• AMD Q4 solid

MTIA.v2 more than doubles compute and memory bandwidth compared to its predecessor released last year. MTIA is only one part of Meta's plan to build its own infrastructure. Meta also updated its PyTorch software stack to account for the updated MTIA processors.

In a blog post, Meta noted:

"MTIA has been deployed in our data centers and is now serving models in production. We are already seeing the positive results of this program as it’s allowing us to dedicate and invest in more compute power for our more intensive AI workloads.

The results so far show that this MTIA chip can handle both low complexity and high complexity ranking and recommendation models which are key components of Meta’s products.  Because we control the whole stack, we can achieve greater efficiency compared to commercially available GPUs (graphics processing units)."

Like other cloud providers such as Google Cloud and AWS, Meta will still purchase Nvidia GPUs and accelerators in bulk, but custom silicon efforts highlight how AI model training and inference workloads will aim to balance cost, speed and efficiency. Not every model needs to be trained by the best processors available.

Here's a look at the MTIA processor comparisons followed by the software stack Meta has deployed.

Google Cloud Next: The role of genAI agents, enterprise use cases

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Google Cloud pitched an agent-oriented vision for generative AI at Google Cloud Next and highlighted a bevy of emerging use cases going from pilot to production.

"We are now building generative AI agents," said Google Cloud CEO Thomas Kurian. "Agents are intelligent entities that take action to help you achieve specific goals."

These actions can range from helping a shopper find a dress, picking health benefits, nursing shift handoffs, bolstering security defenses or building applications. Google Cloud's agents during the keynote were built with its Gemini large language model, but presumably other LLMs were possible via the company's Model Garden.

• Google Cloud Next 2024: Google Cloud aims to be data, AI platform of choice
• Google Unveils Ambitious AI-Driven Security Strategy at Google Cloud Next'24

Google Cloud continues to "offer widely used first party, third party and open-source models," said Kurian. "Vertex AI can be used to tune, augment, manage and monitor these models."

In many ways, Kurian's riff about agents is Google Cloud's answer to Microsoft's Copilot stack and AWS' Q. What Google Cloud did was tie agents to business outcomes and processes that could be automated. "These agents would connect with other agents as well as humans," said Kurian.

Kurian added that genAI agents powered by Gemini models will be the connective tissue between all of Google Cloud's services.

Constellation Research analyst Holger Mueller summed up Google Cloud's approach with agents:

"In the AI race Google provides the right mix of assistants/agents (not the inflationary number of co-pilots like Microsoft) while providing the Über AI with Gemini Cloud Assist (which has the same ambitions like Amazon's AWS' Q). And all of that on the best hardware infrastructure from chips to intra-data center networking and public networking. Google Cloud is powered by Gemini, the most advanced LLM out there, and offers grounding services with Google Search. All in all Google keeps it lead of 3-4 years when it comes to custom algorithms on custom silicon."

Here's a tour of use cases by the type of agents being deployed on Google Cloud.

Customer agents. For enterprises, customer agents are viewed as extra sales and service people. These agents are able to listen carefully, understand your needs and recommend products and services.

Mercedes Benz highlighted multiple customer agent experiences in car as well as for customizing models to buy. "The sales assistant helps customers to seamlessly interact with Mercedes when booking a test drive or navigating through offerings," said Mercedes Benz CEO Ola Källenius.

Enterprises cited by Google Cloud appeared to be gravitating toward genAI as a service engine. Discover Financial uses genAI to search and summarize procedures during calls and IHG Hotels & Resorts is building a travel planning tool for guests.

In addition, Target is optimizing offers and curbside pickup on its app and site. Best Buy is also building an agent to troubleshoot product issues and manage order deliveries. Paramount+ is also using genAI to personalize viewing recommendations.

Google Cloud customer agents can be tailored by conversation flow, languages and subject matter and then know when to hand off to a human agent.

Employee agents. The returns on employee agents are relatively straightforward: Remove repetitive tasks so employees can be more productive. Employee agents can also streamline chores such as health benefits enrollment.

Most of the employee agent examples were tethered to Gemini models running through Google Cloud Workspace, but via Vertex AI extensions models can connect to any external or internal API. Uber CEO Dara Khosrowshahi said employee agents were being built to aid support teams, summarize user communications and reduce marketing agency spending.

How Uber's tech stack, datasets drive AI, experience, growth

Other use cases included Dasa, a Brazil-based medical diagnostic company, using agents to surface relevant findings in test results; Etsy optimizing ad models; and Pepperdine University, which is using Gemini to provide captions and notes across multiple languages.

Gemini-powered agents in Workspace are also being used to analyze RFPs, contracts and other corporate documents. This analysis of large documents and paperwork automation was a key use case across companies such as HCA Healthcare and Bristol Myers Squibb.

Home Depot is leveraging Gemini for its Sidekick application that manages inventory. See: How Home Depot blends art and science of customer experience

Creative agents. Like employee agents, creative agents have been tied to Workspace in the Google Cloud ecosystem. However, I saw an AWS demo where a marketer or ad agency team can create mood boards, pick models and accelerate content concepts to minutes from days or weeks.

For Google Cloud, creative agents are all about using Gemini to create slides, images and text. Carrefour is using Vertex AI to create dynamic campaigns across social networks quickly.

Procter & Gamble is using Google Cloud's Imagen model to develop images and creative assets. Canva is using Vertex AI to power its Magic Design for Video editing tools.

WPP is using Gemini 1.5 Pro to power its media activation tools.

The returns of creative agents can be powerful in that enterprises can avoid media waste and its associated costs across a campaign. In addition, storyboards can be created and tweaked quickly.

Related: Middle managers and genAI | Why you'll need a chief AI officer | Enterprise generative AI use cases, applications about to surge | CEOs aim genAI at efficiency, automation, says Fortune/Deloitte survey

Data agents. A common use case is using generative AI to search, analyze and summarize document, video and audio repositories to surface insights. A good data agent is one that can answer questions and then tell us what questions we should be asking.

Suresh Kumar, CTO of Walmart, said it is using data agents to comb BigQuery and surface insights for personalization, supply chain signals and improve product listings.

Data agents are being deployed for drug discovery and medical treatments. Mayo Clinic is using data agents to search for more than 50 petabytes of clinical data.

In addition, delivery carriers and airlines are using data agents to optimize shipments and routes.

Data agents can be deployed for data preparation, discovery, analysis, governance and to create data pipelines. These agents can also provide notifications when KPIs are being met or in jeopardy.

Constellation Research analyst Doug Henschen said the data agent argument is strong.

"The vision for Data Agents is pretty compelling, with a key point made by Google Cloud being that multi-modal opportunities lie ahead. Multi-modal GenAI-powered data agents will unlock combinations of structured and unstructured data including video, audio, images and code and correlations with structured data. One scenario that Alphabet CEO Sundar Pichai shared was that of an insurance company adjuster that might combine video, images and text to automate a claims process. With BigQuery at the center, Google Cloud foresees data agents applying multiple engine to data, whether SQL, Spark, search or whatever to solve business problems."

BT150 CXO zeitgeist: Data lakehouses, large models vs. small, genAI hype vs. reality

Code agents. Goldman Sachs CEO David Solomon said that genAI ability to boost developer productivity was promising. "There's evidence that generative AI tools for assisted coding can boost developer efficiency and we're excited about that," said Solomon, who said genAI is being used to analyze content and market signals and boost client engagement.

Goldman Sachs rival JPMorgan Chase also sees a boom in developer productivity with genAI code assistance. JPMorgan Chase CEO Dimon: AI projects pay for themselves, private cloud buildout critical

Wayfair CTO Fiona Tan said the retailer is standardizing Google Code Assist and improvements via Gemini 1.5 Pro. Google Cloud is also leveraging Gemini Code Assist and has increased productivity by 30%.

Security agents. Anyone following the ongoing battle with Palo Alto Networks, CrowdStrike and Zscaler knows generative AI has a big role in security. Google Cloud said that Palo Alto Networks will build on top of Google Cloud AI.

Google Cloud said security agents are designed to incorporate data and intelligence to serve up insights and incident response faster. The win is that generative AI can create a multiplier effect for cybersecurity analysts by analyzing large samples of malicious code.

Charles Schwab and Pfizer were cited as a Google Cloud security customers. The goal of a security agent is to identify and address threats, summarize and explain findings and recommend next steps and remediation playbooks quickly. Ultimately, security agents will automate responses.

Constellation Research analyst Chirag Mehta analyzed Google Cloud's security strategy in a research note. He said:

"As a Google Cloud prospect or customer, take a comprehensive inventory of your current security tools landscape, encompassing Google Cloud and its partner ecosystem. Engage with Google Cloud and security tool vendors to discuss their roadmaps for Google Cloud, with a specific focus on how they plan to leverage AI to address your unique requirements. Additionally, consider exploring tools that offer multi-cloud support, regardless of your primary cloud provider, to future proof your security infrastructure."

Intel launches Gaudi 3 accelerator with availability in Q2

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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Intel said its Gaudi 3 AI accelerator will be available in the second quarter with systems from Dell Technologies, HPE, Lenovo and Supermicro on tap. Intel, along with AMD, is hoping to give Nvidia some competition. 

The chipmaker's Gaudi 3 launch, announced at the Intel Vision conference, is the linchpin of Intel's plans to garner AI training and inference workloads and take share from Nvidia.

According to Intel, Gaudi 3 has 50% average better inference and 40% better average power efficiency than Nvidia H100 with lower costs. It's worth noting that the Nvidia has outlined its Blackwell GPUs and accelerators that leapfrog H100 performance.

Nevertheless, model training will be a balancing act between speed and compute costs. Enterprises will use a bevy of options for AI workloads including Nvidia, AMD and Intel as well as in-house offerings from AWS with Trainium and Inferentia and Google Cloud TPUs.

• Nvidia Huang lays out big picture: Blackwell GPU platform, NVLink Switch Chip, software, genAI, simulation, ecosystem
• Will generative AI make enterprise data centers cool again?
• AWS presses custom silicon edge with Graviton4, Trainium2 and Inferentia2
• Intel's AI everywhere strategy rides on AI PCs, edge, Xeon CPUs for model training, Gaudi3 in 2024
• AMD Q4 solid

Key points about Gaudi 3:

• Intel Gaudi 3 is manufactured on 5 nm process and uses its engines in parallel for deep learning compute and scale.
• Gaudi 3 has a compute engine of 64 AI-custom and programmable tensor processor cores and eight matrix multiplication engines.
• Memory boost for generative AI processing.
• 24 GB Ethernet ports integrated into Gaudi 3 for networking speed.
• PyTorch framework integration and optimized Hugging Face models.
• Gaudi 3 PCIe add-in cards.

To go along with the Gaudi 3 launch, Intel said it will create an open platform for enterprise AI along with SAP, RedHat, VMware and other companies. It is also working with the Ultra Ethernet Consortium and will launch a series of network interface cards and AI connectivity chiplets.

MongoDB Atlas expands Google Cloud Vertex AI integration, eyes vertical use cases

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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MongoDB expanded integrations with Google Cloud's Vertex AI, BigQuery, Google Distributed Cloud and Google Cloud Manufacturing Data Engine.

The expanded collaboration between MongoDB and Google Cloud boils down to a common theme: Enterprises need more seamless ways to build generative AI applications with their proprietary data.

MongoDB's news lands as Google Cloud kicked off its Google Cloud Next conference in Las Vegas with a barrage of new product features and announcements with Vertex AI and BigQuery as the headliners.

Key items in the MongoDB and Google Cloud expanded partnership include:

• Google Cloud Vertex AI will have an extension for MongoDB Atlas and Spark integration with BigQuery.
• MongoDB Atlas will be integrated into the Google Cloud Manufacturing Engine, which is focused on the manufacturing vertical.
• MongoDB joins Google Cloud's Industry Value Network, which is designed to expand industry-focused AI. MongoDB and Google Cloud are also working on industry integrations for retail.
• MongoDB Atlas Search Nodes are generally available on Google Cloud.
• And MongoDB Enterprise Advanced on Google Distributed Cloud is aimed at regulated industries that need to comply with data privacy requirements.

For MongoDB, a Google Cloud partner of the year, the partnership with the No. 3 cloud provider gives it more reach into key industries. Enterprises are increasingly looking to multiple language models to fine tune for industry-specific applications while keeping first-party data secure.

MongoDB and Google Cloud have been partners since 2018 and have thousands of joint customers.

More MongoDB:

• MongoDB Q1, fiscal year outlook light, but eyes stable workload gains

• MongoDB steps up generative AI rollout across platform
  MongoDB launches Atlas Vector Search, Atlas Stream Processing to enable AI, LLM workloads

Google Cloud Next 2024: Google Cloud aims to be data, AI platform of choice

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

Read more

Google Cloud outlined a series of services and enhancements across its platform in a bid to make it easier for enterprises to bring their data to generative AI models, build applications and deploy them at scale. Google Cloud's data analytics services will unify under the BigQuery umbrella and Vertex AI becomes the venue to tune, orchestrate and deploy models.

Ultimately, Google Cloud is bidding to be the AI optimized stack of choice that will enable companies to deploy a series of agents that can automate workflows and carry out tasks. And by the way, Google Cloud is offering model choices, but is embedding Gemini everywhere. 

Google Cloud CEO Thomas Kurian (right) said:

"We're building AI to be an open, vertically optimized stack. This stack consists of advances with our AI supercomputer, which is now used by over 90% of AI unicorns. There are advances to improve the efficiency and scale of training and serving and a wide portfolio of different kinds of system optimizations, allowing us to provide developers and organizations with the market leading cost performance for training and inferencing models."

Kurian added that Google Cloud Next 2024 will include more than 1,000 new products and features across its platform. "We continue with our strategy to help organizations drive digital transformation using our cloud platform and AI," said Kurian.

• Google Unveils Ambitious AI-Driven Security Strategy at Google Cloud Next'24

• Google Cloud Next: The role of genAI agents, enterprise use cases

Here's the Google Cloud vision in two slides.

Leading to...

The announcements filling out this vision are plentiful, but here are the big launches.

• At the infrastructure layer, Google Cloud outlined the latest GPU/TPI support, PyTorch enhancements, Axion, Google's first custom designed Arm processor, and confidential computing enhancements. Distributed Cloud will be aimed at sovereignty workloads and AI anywhere use cases. 
• Google Axion delivers up to 50% better performance and 60% better energy efficiency compared to x86 based instances. 
• Google Cloud said that Nvidia's latest Grace Blackwell GPUs will be served up as instances in early 2025. The company also outlined A3 Mega, a generally available instance tht has twice the bandwidth per GPU compared to A3 instances. 
• On the model choice front, Google Cloud general availability for Gemini 1.0 and Gemini 1.5 Pro in public preview as well as Grounding on Google Search that will provide fresh information that's grounded. Google Cloud also announced Imagen 2.0 Editing in GA and private preview of text to live image, LangChain on Vertex AI and Vertex AI Prompt Management and Assistance. 
• Gemini is being added to BigQuery, Databases, Vector indexing and Looker to name a few. Gemini is also being added to Google Cloud's security offerings. 
• Google Workspace will get Google Vids, a way to collaborate and tell stories at work via Gemini, Vertex AI and Workspace integration and an add-on SKU for Gemini Meet/Chat that will be $10 per month per user.
• For databases, Gemini will power an AI database assistant across Google's offerings. AlloyDB will also add an extension for faster vector search and AlloyDB for LLMs will be able to retrieve information with natural language. Vector support and integration with LangChain will be deployed across Google databases. 
• On data analytics, BigQuery will become a unified platform for data to AI to support multimodal data and workloads with Gemini and a series of AI integrations. Specifically, BigQuery upgrades include a metastore for a unified data foundation, unified governance and cataloging and the addition of Apache Spark and Apache Kafka integration. Google Cloud is putting all of its data analytics services together on BigQuery.

• With security, Google is using Gemini for SecOps and Threat Intelligence as well as adding an enterprise browser for Chrome.
• Developers will get Gemini Code Assist, Cloud Assist for cloud operations and insights, and enhanced integrations with partners such as Datadog, DataStax, Elasticsearch, HashiCorp, SingleStore and Redis.
• Customer references cited by Google Cloud for AI adoption include Bayer, Best Buy, Discover Financial and TD Bank to name a few. Also see what Equifax and Wayfair have done with Google Cloud. 

The race with AWS and Microsoft Azure

Google Cloud is No. 3 among the hyperscale cloud vendors, but Kurian said the company has a real play for deploying AI models and its data platform. "Customers want three things. They want a platform that allows them to build deploy AI models at scale. They want that platform to be differentiated. They want an organization that owns its own models and is able to vertically optimize the models. And they want integrated AI across the portfolio," said Kurian.

Kurian also touted model choices, which is something AWS recently spoke about with Bedrock. Kurian said grounding of models will also be critical. He said:

"We're introducing grounding with Google search. Not just grounding on your own enterprise data, and then evaluating. We provide that platform that offers a set of services that works with all the models. People are able to choose the platform and then choose the latest model or the best model for their needs. Many organizations now recognize that they need to take an enterprise AI platform, not pick a model. Models are changing week to week, month to month. They need a common foundational platform to do that."

Ultimately, Kurian is betting that Google Cloud and gain ground with a series of AI agents that can carry out tasks, understand processes and context and orchestrate workflows. He said customers are using a combination of Google Cloud building blocks "not just to do individual tasks but to orchestrate process flow."

Ultimately, cloud providers are looking to evolve to become the model orchestration layer for AI.

• AWS, Microsoft Azure, Google Cloud battle about to get chippy
• Google Cloud CEO Thomas Kurian on DisrupTV: Generative AI will revamp businesses, industries
• AWS, Microsoft Azure, Google Cloud themes: Optimization, generative AI and the long game
• Google Cloud rolls out Gemini models to Vertex AI customers, Gemini 1.5 in private preview
• Google Cloud's healthcare push accelerates with Vertex AI, MedLM, Healthcare Data Engine
   

Google Cloud agents everywhere

The linchpin of Google Cloud's agent vision revolves around Vertex AI, which will include an Agent Builder and Model Builder to go along with a wide selection of models.

Agent Builder will feature no code, low code and full code varieties to orchestrate, ground and augment models, take action and process documents.

In a blog post, Kurian outlined the importance of agents to the generative AI landscape. He said agents can understand multi-modal information and learn overtime to handle transactions and business processes over time. Best Buy, Etsy, The Home Depot and ING Bank are uutilizing agents. 

Kurian and Google Cloud executives said these agents can be deployed in multiple contexts and venues including contact center, security, healthcare, retail and media to name a few. The consolidation of data analytics under BigQuery in a unified platform will hand off to Vertex AI.

Google Cloud will also layer in a series of MLOps services to bring generative AI from pilots to production including prompt management to create a feedback loop to continually improve and revise prompts.

Constellation Research's take

Constellation Research CEO Ray Wang said the following announcements stuck out on Day 1 of Google Cloud Next:

• Gemini is now across software development, application life cycle, security, data analytics, BI, and databases.
• Model selection. Customers want to bring their own models and being able to choose from Gemini, Lensa, Gemma, and Athropic is what customers want.  
• Google Cloud is providing choice in chips from TPUs, to Nvidia GPU's to CPUs in the data center. 
• Security models allow for air gap capabilities meaning it works great for government. Threat intelligence was beefed up.
• Google Workspace is getting some new features from Vids to AI meetings that will take notes in 69 languages.

Constellation Research analyst Doug Henschen covered many of the announcements, here's his take on the Google Cloud Next news:

Gemini integration into BigQuery, Looker and GCP's databases. Henschen said:

"It’s significant both in terms of the depth and breadth of GenAI capabilities promised both within each product and across the entire portfolio of services. Focusing on BigQuery, the breadth of Gemini assistance is a differentiator, spanning from ingestion, data preparation, cleansing, and low-code data pipeline building to query recommendation, query cost and performance optimization, semantic search, and Python and SQL code generation."  

What Gemini brings to the data platform. Henschen said:

"GenAI capabilities are showing up in a lot of databases, but Google is going deeper and doing it more comprehensively across its portfolio. The capabilities are still in preview, mind you, but Google has also updated the underlying model since last year’s Duet announcements. Google says Gemini will deliver higher accuracy and better performance at a lower cost. As with most GenAI features, the promise is making sophisticated tasks easier for untrained users while improving the productivity of more experienced users."

Google Cloud's AI strategy to date. Henschen said:

"Google's data and AI strategy is about delivering a comprehensive platform with well-integrated capabilities so you can do it all without having to move data around or cobble together disparate services. BigQuery, in particular, has become the focal point, with tight integration with Vertex AI, for  AI, ML and GenAI, and Google Looker, for analytics. Another differentiator is  multi-cloud support through BigQuery Omni access to other clouds as well as enterprise applications, such as Salesforce, with a new Zero-ETL capability."

Google Cloud's analytics strategy relative to rivals. Henschen said:

"It’s not just about tacking on GenAI features. The big push across all the hyperscale clouds it to offer a single platform for data that seamlessly supports all your AI, GenAI, analytics and wider application development and operational needs. That’s also Microsoft’s push with Fabric and AWS’s push with its extensive portfolio of services. I give Google credit for bringing together a well-integrated platform centered around BigQuery and Vertex. AWS, for its part, has been doing more integration across its vast portfolio of services in recent years. Microsoft Fabric is very new and unproven at this point, but it’s also messaging about doing it all with one platform, putting an emphasis on familiarity and ease of use."   

What Google should do next. Henschen said:

"I’d say Google just needs to stick to its strategy, which has been very consistent. Google BigQuery and Vertex AI are strong, well-integrated services that are only getting stronger.  Google Cloud is playing catch-up a bit on the transactional side with AlloyDB, which is a much more recent introduction that goes up against Amazon Aurora. I’m sure Google will keep on improving that product while also retaining the open partnerships it has had with leading independents, such as MongoDB. Google leads with its strengths, but openness to third-party vendors and model providers  has been another consistent and important part of the Google Cloud strategy."   

JPMorgan Chase CEO Dimon: AI projects pay for themselves, private cloud buildout critical

Larry Dignan

Editor in Chief of Constellation Insights
Constellation Research

Larry Dignan is Editor in Chief of Constellation Insights at Constellation Research, where he leads editorial coverage focused on enterprise technology, digital transformation, and emerging trends shaping the future of business. He oversees research-driven news, analysis, interviews, and event coverage designed to help technology buyers and vendors navigate complex markets with clarity and context. ...

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JPMorgan Chase CEO Jamie Dimon issued his annual shareholder letter and provided an incremental update on the company's artificial intelligence efforts as well as private cloud buildout.

In the letter, Dimon covered the expected interest rate outlook and geopolitical uncertainty, but also spent a good bit of space on AI, generative AI and transitioning to the cloud, which enables JPMorgan Chase to roll out services faster.

JPMorgan Chase: Digital transformation, AI and data strategy sets up generative AI (download PDF) | JPMorgan Chase: Why we're the biggest tech spender in banking

Here are some of the technical highlights that update JPMorgan Chase's AI strategy as outlined in Constellation Research's case study (download PDF).

• AI's big picture. Dimon said: "We are completely convinced the consequences will be extraordinary and possibly as transformational as some of the major technological inventions of the past several hundred years: Think the printing press, the steam engine, electricity, computing and the Internet, among others."
• JPMorgan Chase has 2,000 AI and machine learning experts and data scientists.
• The company has more than 400 use cases in production. "We're also exploring the potential that generative AI (GenAI) can unlock across a range of domains, most notably in software engineering, customer service and operations, as well as in general employee productivity," said Dimon, who added that generative AI will help the company "reimagine entire business workflows."  Also: BT150 CXO zeitgeist: Data lakehouses, large models vs. small, genAI hype vs. reality
• JPMorgan Chase will continue to invest in AI and "many of these projects pay for themselves." Dimon added that AI has the potential to augment most jobs, reduce roles and create new ones.
• To enable new AI capabilities, JPMorgan Chase has to migrate its data estate to the public cloud. "These new data platforms offer high-performance compute power, which will unlock our ability to use our data in ways that are hard to contemplate today," said Dimon.
• AI is being incorporated into JPMorgan Chase's risk and control frameworks to counter threats.
• Multicloud is critical to avoid lock-in. Dimon said JPMorgan Chase's cloud plans will include multiple clouds--private and public.
• JPMorgan Chase is building 4 new private cloud data centers for $2 billion.
• Most workloads and data will be in public and private clouds. "To date, about 50% of our applications run a large part of their processing in the public or private cloud. Approximately 70% of our data is now running in the public or private cloud. By the end of 2024, we aim to have 70% of applications and 75% of data moved to the public or private cloud," said Dimon. "The new data centers are around 30% more efficient than our existing legacy data centers. Going to the public cloud can provide 30% additional efficiency if done correctly (efficiency improves when your data and applications have been modified, or “refactored,” to enable new cloud services)."

Related: Middle managers and genAI | Why you'll need a chief AI officer | Enterprise generative AI use cases, applications about to surge | CEOs aim genAI at efficiency, automation, says Fortune/Deloitte survey