Principal Analyst and Founder
Constellation Research
R “Ray” Wang is the CEO of Silicon Valley-based Constellation Research Inc. He co-hosts DisrupTV, a weekly enterprise tech and leadership webcast that averages 50,000 views per episode and blogs at www.raywang.org. His ground-breaking best-selling book on digital transformation, Disrupting Digital Business, was published by Harvard Business Review Press in 2015. Ray's new book about Digital Giants and the future of business, titled, Everybody Wants to Rule The World was released in July 2021. Wang is well-quoted and frequently interviewed by media outlets such as the Wall Street Journal, Fox Business, CNBC, Yahoo Finance, Cheddar, and Bloomberg.
Short Bio
R “Ray” Wang (pronounced WAHNG) is the Founder, Chairman, and Principal Analyst of Silicon Valley-based Constellation Research Inc. He…...
Paging all CXOs 📣 If you use the Constellation ShortList portfolio to narrow your search for leading enterprise technologies, don't miss this interview!👇
In 2024, we selected Hewlett Packard Enterprise Greenlake as one of the leading hashtag#transformation target platforms. R "Ray" Wang sits down with Fidelma Russo, CTO of HPE, to talk through why CXOs should strongly consider using HPE Greenlake to reach their digital transformation goals.
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. ...
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.
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.
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. ...
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.
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.
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."
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. ...
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.
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)."
Vice President and Principal Analyst
Constellation Research
Chirag Mehta is Vice President and Principal Analyst focusing on cybersecurity, next-gen application development, and product-led growth.
With over 25 years of experience, he has built, shipped, marketed, and sold successful enterprise SaaS products and solutions across startups, mid-size, and large companies. As a product leader overseeing engineering, product management, and design, he has consistently driven revenue growth and product innovation. He also held key leadership roles in product marketing, corporate strategy, ecosystem partnerships, and business development, leveraging his expertise to make a significant impact on various aspects of product success.
His holistic research approach on cybersecurity is grounded in the reality that as sophisticated AI-led attacks become…...
Two questions have haunted me for two decades: first, can we really address security without addressing networks? Second, are observability and security like oil and water? With customers adopting SASE (Secure Access Service Edge) solutions, we have begun to see a convergence of networks and security. Now, with Splunk's acquisition, Cisco has answered my second question.
Cisco officially closed Splunk’s acqusition a few days ago. Last week, the leadership from Cisco and Splunk communicated their joint vision in an executive roundtable that I attended. The roundtable featured Liz Centoni, Cisco EVP and Chief Customer Experience Officer, Tom Casey, SVP of Splunk Products & Technology, and Jeetu Patel, Cisco EVP and GM of Security and Collaboration. They together offered a glimpse into how they see the future of Cisco and Splunk working together to better serve their customers. Here's a breakdown of the key points, my brief analysis, and recommendations for customers.
Leaders' Vision: A Unique Combination of Security, Observability, and AI
The core message from Cisco and Splunk was clear: data, observability, and AI are the cornerstones of modern security. Jeetu Patel emphasized,
"If you want to be a world-class security company, you have to be a world-class AI company, and if you want to be a world-class AI company, you have to be a world-class data company."
What excites both companies is the unique combination their offerings bring. Cisco's strength in networking and security complements Splunk's expertise in data platform and observability. As Tom Casey highlighted, "There's a lot of complements between the two areas."
This unique combination, according to Jeetu, "doesn't exist in the market today." A fully integrated security and observability platform with AI at its core has the potential to revolutionize how organizations approach security.
Jeetu outlined three key objectives: enhancing efficacy through generative AI, improving user experience, and optimizing economics. In his closing remarks, he reiterated the companies' commitment to customer-centric innovation, “We will always start from the customer first and work backwards. We love our customers keeping us honest and making sure that we can actually drive to the outcomes.”
My Insights
Cisco's acquisition of Splunk presents a unique opportunity to address customers' end-to-end cybersecurity needs, leveraging the power of AI and data analytics. Although integrations require time and effort, when executed effectively, they have the potential to solve complex challenges and enhance operational efficiency.
Cisco and Splunk are culturally different companies; Cisco being a mature networking player known for its robust partner ecosystem, while Splunk holds boasts a broader developer reach with its security and observability offerings. Harnessing each other's strengths, they can foster a thriving cybersecurity ecosystem that paves a path for companies to build compelling solutions on their platform. A complementary acquisition typically benefits customers more than an overlapping one.
Traditionally, observability and security were seen as distinct areas. Yet, their merging offers Cisco and Splunk an unprecedented opportunity to tackle enduring cybersecurity issues, aggravated by data silos. In a time when organizations struggle with a scarcity of cybersecurity expertise, Cisco's ambition to democratize cybersecurity via AI reflects prevailing industry patterns we observe, placing emphasis on enhanced tool adoption and fortified security posture.
Recommendations for CxOs
As you navigate the convergence of networking and security, and formulate your AI strategy, reasses your current cybersecurity landscape. Look for opportunities to streamline your tools to drive increased adoption. Remember, increased adoption is superior to the allure of extravagant features.
Advocate for improved integration between Cisco and Splunk by articulating specific business outcomes you aim to achieve. Communicate your expectations to both companies' leadership regarding the enhancements you anticipate in the coming weeks and months. Consider attending their conferences this summer, Cisco Live and Splunk .conf24, to gain insights into their respective product roadmaps and provide your valuable feedback.
As you craft your security strategy and execution plan, check out our "11 Top Cybersecurity Trends of 2024 and Beyond." (If you're a vendor and don't have access to the report please contact me for a courtesy copy.) Drawing insights from numerous conversations with security, technology, and business leaders as well as extensive market research, this cybersecurity trends report offers a holistic view into the broader cybersecurity landscape. It also offers tangible recommendations for CxOs who are frantically navigating the cybersecurity maze to design and operationalize their cybersecurity strategy, with the objective to improve their defenses against increasingly sophisticated attacks.
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. ...
Enterprises need to focus on data lakehouse strategies in 2024 to properly take advantage of generative AI; model architecture will be critical to managing large and small models; fine tuning is more difficult than you'd think; and CXOs were weary of database vendors glomming on to genAI hype.
Those were some of the takeaways from Constellation Research's April 5 BT150 CXO meetup.
These gatherings, held under Chatham House rules, are a venue to share information and emerging trends.
Here's a look at the topics from our February meetup.
Fine tuning isn't as easy as you'd think. While fine tuning and customizing a foundational model should be easier than training a large language model from scratch, the process is more involved. The tooling isn't mature enough yet for fine tuning at scale and enterprises are evaluating where to host data.
Get that data lakehouse. Enterprises are coming around to the reality that they need to have a data strategy before even thinking about AI. Considerations include:
Ability to move data to models in real time.
Need to combine enterprise data with third party data.
Costs.
Need for real-time data ingestion.
Build your own enterprise data lakehouse.
Benefits of data lakehouse strategy is that the business will also see business intelligence and analytics benefits and the promise of big data.
Take 2024 to nail the data lakehouse strategy to prepare your company for AI.
Foundational model strategy. CXOs and Constellation Research analysts expect industry and role specific models to emerge. In addition, enterprises will need to have model strategies that incorporate approaches that use language that applies to industries and companies specifically. Enterprises will need to think through model architectures to manage models for finance, HR, manufacturing, and other roles.
Kill switches. There was a good debate on our call about the need for an AI kill switch. On one side, models will be hacked and when that happens, you'll need to be able to pull the plug and recover. The argument against the kill switch concept is that other parts of the enterprise don't automatically shut down.
CXOs were exhausted by transactional database vendors that are glomming on to the generative AI hype. These vendors are mostly concerned about you moving your data away from them. Wait until you see a feasible generative AI solution from database vendors before falling for the hype.
Small models vs. large ones. LLMs could be seen by enterprises as boiling the ocean and many CXOs and vendors are talking about smaller models that are specific to a task or process. The reality is that models will require a hybrid approach. Some models will be large, some small and some will be run locally too.
Model suites will always win? While CXOs will take a best-of-breed approach to models due to conditions and hardware limitations, but ultimately the suite approach is likely to win. Specialist models will be better for some tasks, but economies of scale over time favor generalists and suites. The feedback loop of more data and context is likely to favor large models. Beware of small model chatter from vendors without a comprehensive AI strategy or access to a large language model.
Generative experiences with avatars. One CXO was piloting a series of avatars to personalize generative experiences by language and use case. This avatar meets genAI approach appears to be positive for the host and participant. The CXO noted that starting with a framework, governance and privacy controls is a key enabler for generative AI use cases.
AI will create interesting dynamics in the labor pool. Analytics and data science roles are likely to be impacted despite what recent surveys have indicated. A college student with the free time to experiment with prompts can replicate the experience of someone doing predictive analytics and data science for decades. Simply put, the entire skill model for enterprises is going to change.
High performance computing will change due to generative AI. HPC is going to have to evolve since it is in the middle of the generative AI revolution. Nvidia's Blackwell launch featured a series of GPU clusters that will likely compete with supercomputers. Generative AI workloads will fundamentally change compute.
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. ...
The debate over generative AI and its impact on the workforce is just heating up since the technology hasn't scaled at most enterprises. One of the biggest questions to ponder is whether genAI's impact will be muted by demographics.
It's easy to conclude that generative AI is going to take jobs from humans. But there's another argument that genAI will be needed just to maintain and improve productivity levels because there will be fewer workers. There’s a demographic donut hole in the workforce that may be partially ameliorated by genAI.
During Paychex's third quarter earnings call, CEO John Gibson highlighted how small and mid-sized businesses were struggling to find employees. Gibson also noted that the pace of retirements from Baby Boomers is only going to pick up. Meanwhile, generation X doesn't have the numbers to fill the institutional knowledge gap.
Gibson said Paychex is using AI to drive insights on retention as well as integrations with Indeed. Paychex's partnership with Visier will offer compensation insights.
He added:
"The simple fact is we have a generational change happening in the labor force. Participation rates remain below pre-pandemic levels and it's going to be very difficult given the rate of retirements that we're seeing in Baby Boomers to really see that change. And what you see in the prime age workers were actually at record highs. The problem is not enough prime age people to fill all the opportunities."
"We need to do more to allow businesses to invest in productivity and drive productivity enhancements and that's not going to replace workers. That's going to enable them to get the work done with less workers than are going to exist in the marketplace. I think this is a systemic problem."
Gibson said there's a productivity gap that'll occur as younger workers replace older ones. The only nuance here is that older workers may not all leave the workforce on schedule.
Pew Research found that 19% of Americans ages 65 and older were employed in 2023, nearly double the share from 35 years ago. The typical worker age 65 or older earns $22 an hour.
Workers ages 75 and older are the fastest-growing age group in the workforce. Today, 9% of workers that age is employed. Blackrock CEO Larry Fink should be excited about that development given he sounded the alarm bells about retirement funding and noted that one fix to Social Security would be working longer. Living to age 80 isn't terribly uncommon today.
Bottom line: The generative AI hit to the workforce is inevitable, but there's a more nuanced position to take amid the doom and gloom. One thing is certain: Generative AI is going to be a public policy issue.
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. ...
Wipro names Pallia CEO to replace DelaporteWipro has named Srini Pallia CEO effective immediately replacing Thierry Delaporte, who stepped down to pursue other interests.
Pallia (right) most recently was CEO for Wipro's Americas 1 unit and has been with the company for more than three decades. Pallia was responsible for Wipro Americas' vision, strategy and industries and was president of Wipro's consumer business and head of business applications services. Delaporte will remain with Wipro through the end of May for the transition to Pallia.
Wipro's Americas 1 unit includes healthcare and medical devices, consumer goods and life sciences, retail, transportation and services, communications, media and information services, technology products and platforms in the US and Latin America. Wipro's Americas 2 division is focused on financial services, manufacturing, technology and energy and utilities in the US and Canada.
The US accounted for 56% of Wipro's fiscal 2023 revenue.
In a statement, Pallia said he was "excited to build on the strong foundation established by Thierry and lead Wipro on its next growth trajectory."
Delaporte led Wipro through a transformational phase in his four years at the helm. The company has launched its ai360 strategy, which revolves around embedding AI throughout its services and offerings.
For the nine months ended Dec. 31, Wipro reported revenue of $8.12 billion with profit of $993 million. Revenue was modestly higher relative to a year ago. For fiscal 2023, Wipro reported revenue of $11 billion, up 14% from 2022, with profits of $1.38 billion.
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. ...
Archetype AI has raised $13 million in seed funding and launched Newton, a foundational model that is built to understand the physical world via data signals from accelerometers, gyroscopes, radars, cameras, microphones, thermometers and other environmental sensors.
Newton aims to take physical data and combine them with natural language to provide insights about the physical world. Architype AI's funding round was led by Venrock and included Amazon Industrial Innovation Fund, Hitachi Ventures, Buckley Ventures and Plug and Play Ventures.
Archetype AI's Newton highlights how foundational models continue to evolve at a rapid clip. While large language models have focused on language and image patterns, there is plenty of room for more niche use cases. Archetype AI describes Newton as "a first-of-its-kind physical AI foundational model that is capable of perceiving, understanding and reasoning about the world."
Ivan Poupyrev, CEO and co-founder of Archetype AI, said the company's mission is to solve the biggest problems which are "physical, not digital." "Our goal is to encode the entire physical world so we can derive meaning from the signals all around us and create new solutions to problems that we previously couldn’t understand," he said.
Newton is designed to scale across any kind of sensor. In theory, Newton could bring insights to the Internet of things as well as trillions of sensors in multiple industries. Archetype AI is an example of a foundational model company that can work through multiple verticals and use cases.
Speaking at an AWS analyst meetup, Matt Wood, VP of AI at AWS, was asked about whether foundational models would be commoditized quickly. After all, the LLM layer is likely to be abstracted with models being swapped as easily as cloud instances. Wood said foundational models are unlikely to be commoditized. Instead, these models will become more specialized.
Wood said:
"There is so much utility for generative AI. You're starting to see divergence in terms of price per capability, but I think that we're talking about task models, industry-focused models, vertically focused models and more. There's so much utility that I doubt these models are going to become commoditized."
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. ...
DataStax said it will acquire Logspace, which is the company behind Langflow, an open-source framework for retrieval-augmented generation (RAG) applications. Logspace's Langflow team will continue to run independently with a focus on development and community.
Langflow features visual tools to iterate on data flows and build LangChain RAG applications and deploy them in a click.
Constellation Research analyst Andy Thurai noted that DataStax needed to upgrade its platform for building RAG-based applications. "DataStax had its own version of software to build RAG-enabled applications called RAGStack, which combined LangChain, LLaMaIndex, and more," said Thurai. "RAGSTack was as the best open-source option for implementing RAG, but it was difficult to use."
Thurai added Langflow will give DataStax the ability to create conversation flows with large language models, chatbots and virtual agents without extensive coding.
Doug Henschen, analyst at Constellation Research, said DataStax's purchase of Logspace keeps pace with where the industry is going. He said:
"For DataStax, this acquisition clearly improves DataStax’s ability to support RAG. Other DB services, like MongoDB and DataStax competitor Azure Cosmos DB, have also added support for vector embedding, vector search and RAG, but it’s early days for everybody. Another competitor adding vector embedding capabilities is Amazon with DynamoDB, so they’re clearly heading in the same direction. It takes time to add such capabilities and gain adoption, so the LangFlow acquisition is all about acceleration while AI interest is peaking."
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 
Pallia (right) most recently was CEO for Wipro's Americas 1 unit and has been with the company for more than three decades. Pallia was responsible for Wipro Americas' vision, strategy and industries and was president of Wipro's consumer business and head of business applications services. Delaporte will remain with Wipro through the end of May for the transition to Pallia.