This list celebrates changemakers creating meaningful impact through leadership, innovation, fresh perspectives, transformative mindsets, and lessons that resonate far beyond the workplace.
Editor in Chief of Constellation Insights
Constellation Research
About Larry Dignan:
Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning.
He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.
<br>Constellation Insights does the following:
Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and…
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Akamai has added Nvidia GPUs to its distributed cloud network adding a service optimized for processing video content at the edge.
The cloud service, announced at the National Association of Broadcasters' (NAB) conference, is powered by Nvidia RTX 4000 Ada Generation GPUs.
Akamai has been steadily building out its distributed cloud infrastructure for multiple use cases including AI and machine learning workloads that require low latency near data.
According to Akamai, the Nvidia RTX 4000 instances can process video frames per second 25x faster than CPU encoding and transcoding. Although the Akamai Nvidia-powered instances are initially aimed at the media industry, the company is also betting on other use cases.
These use cases include:
Virtual reality and augmented reality content.
Generative AI and machine learning workloads for training and inference at the edge.
Data analytics and scientific computing.
Gaming and graphics rendering.
High-performance computing.
Akamai said it will continue to add GPU instances optimized by industries.
Editor in Chief of Constellation Insights
Constellation Research
About Larry Dignan:
Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning.
He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.
<br>Constellation Insights does the following:
Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and…
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Foundational model debates--large language models, small language models, orchestration, enterprise data and choices--are surfacing in ongoing enterprise buyer discussions. The challenge: You may need a crystal ball and architecture savvy to avoid previous mistakes such as lock-in.
AWS highlighted its thoughts on open model choices and leveraging Bedrock combined with Q to be the generative AI platform of choice. AWS has the Titan model, but emphasizes model choices and is positioned to be the Switzerland of LLMs.
Constellation Research's latest BT150 CXO call revolved around small models vs. large ones. Vendors such as ServiceNow have championed smaller use-case specific models for efficiency and costs.
Anthropic CEO Dario Amodei said enterprises won't be in a position in the future of choosing a small custom model or a large general one. The correct fit will be a large custom model. LLMs will be customized for biology, finance and other industries.
Archetype AI raised $13 million in seed funding and launched its Newton physical world foundational model. Newton is built to understand the physical world and data signals from a range of environmental sensors. Newton highlights how LLMs are being aimed at green field opportunities.
For good measure, Microsoft will likely talk about building customized Copilots and model choices at Build in May. A few sessions on Azure AI Studio have highlighted orchestration.
Now that's a lot to talk about considering how enterprises need to plow ahead with generative AI, leverage proprietary data and pick a still-in-progress model orchestration layer without being boxed in. The dream is that enterprises will be able to swap models as they improve. The reality is that swapping models may be challenging without the right architecture.
Will enterprise software vendors use proprietary models to lock you in? Possibly. There is nothing in enterprise vendor history that would indicate they won't try to lock you in.
The crystal ball says that models are likely to be commoditized at some point. There will be a time where good enough is fine as enterprises toggle between cost, speed and accuracy. Models will be like compute instances where enterprises can simply swap them as needed.
It's too early to say that LLMs will go commodity, but there's no reason to think they won't. Should that commoditization occur platforms that can create, manage and orchestrate models will win. However, there is a boom market for models for the foreseeable future.
AWS Vice President of AI Matt Wood noted that foundation models today are "disproportionately important because things are moving so quickly." Wood said: "It's important early on with these technologies to have that choice, because nobody knows how these models are going to be used and where their sweet spot is."
Wood said that LLMs will be sustainable because they're going to be trained in terms of cost, speed and power efficiency. These models will then be stacked to create advantage.
Will these various models become a commodity?
"I think foundational models are very unlikely to get commoditized because I think that there's just there is so much utility for generative AI. There's so much opportunity," said Wood, who noted that LLMs that initially boil the AI ocean are being split into prices and sizes. "You're starting to see divergence in terms of price per capability. We're talking about task models; I can see industry focus models; I can see vertically focused models; models for RAG. There's just so much utility and that's just the baseline for where we're at today."
He added:
"I doubt these models are going to become commoditized because we haven't yet built a set of criteria that helps customers evaluate models, which is well understood and broadly distributed. If you're choosing a compute instance, you can look at the amount of memory, the number of CPUs, the number of cores and networking. You can make some determination of how that will be useful to you."
In the meantime, your architecture needs to ensure that you aren't boxed in as models leapfrog each other in capabilities. Rapid advances in LLMs mean that you’ll need to hedge your bets.
Constellation Research analyst Holger Mueller noted:
"We are only at the beginning of the foundation model era. The layering of public LLMs that are up to speed on real world developments and logically merged with industry knowledge, SaaS packaging, and functional enterprise domain specific models are going to be crucial for gen AI success. Bridging the gap from real world aware and fitting an enterprise makes genAI workable and effective."
Editor in Chief of Constellation Insights
Constellation Research
About Larry Dignan:
Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning.
He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.
<br>Constellation Insights does the following:
Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and…
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Anthropic CEO Dario Amodei said large language model personality is starting to matter, argued costs to train models will come down and agents that act autonomously will need more scale and reliability.
Those were some of the takeaways from Amodei, who spoke at Google Cloud Next.
Model personalities will start to matter. Amodei covered the launch of Claude 3 and said a lot of effort was put into making the large language model personable. He said:
"One thing that we worked particularly hard on was the personality of the model. We've had this kind of chat paradigm of models for a while, but how engaging the model was hasn't had as much attention as reasoning capabilities. How much does it sound like a human? How warm and natural is it to talk to? We had an entire team devoted to making sure that Claude 3 is engaging."
Models need families. Amodei said the strategy for Claude 3 was to create a family of models. "Opus is the largest one. Sonnet is the smaller one, but faster and cheaper. Haiku is very fast and very cheap," he said. "Enterprises have different needs. Opus is very good at performing difficult tasks where you have to do exact calculations and those calculations have to be accurate. Sonnet is the workhorse model in the middle. I'm excited about Haiku because it outperforms almost all of its intelligence class while being fast and cheap."
Costs of training and inference. Amodei said costs for training and inference are coming down and will continue to fall, but more will be spent on training models. He said:
"I think the cost of training a particular model is going to go down very drastically but the models are so economically valuable that the amount of money that's spent on training is going to continue growing exponentially. We'll eat up all the efficiency gains at least at the higher end of models. Within Anthropic we measure things in units we call effective compute. I think that is going to go up 10x per year. That can't last forever, and no one knows for sure how long it'll last, but that's where we are right now."
How LLMs will develop over next few years. Amodei said model intelligence will come from pure scale. Future reliability and ability to handle specialized tasks will come from more scale and multi-modality with images, video and audio inputs. There will also be interactions with the physical world, maybe even robotics.
Hallucinations will also be a key challenge. "We have substantial teams to reduce the amount of hallucination at the present in models," said Amodei.
"The final thing I expect to see in the next year or two is agents models acting in the world," he added. "We've seen lots of instantiations of agents so far, but we haven't seen anything yet."
Enterprise use cases. Amodei said as models get smarter and trained for longer, they become much better at coding tasks. Healthcare and biomedicine will also be key use cases as well as finance and legal uses. "These use cases often involve reading long documents which Claude 3 has gotten better at relative to previous models," said Amodei.
Corporate use cases appear to be split evenly between creating internal tools to make employees more productive and customer facing uses. Consumer-facing companies will enable users to do more sophisticated tasks by coupling APIs.
Amodei said the cost of models for these use cases will become less of an issue since they'll be right sized for the task at hand.
The importance of prompt engineering. Amodei said enterprises should spend time with prompt engineers to test models and make sure they work as expected.
He said:
"We are still trying to figure out how our own models work. A large language model is very complicated object. When we deploy it, there's no way for us to figure out everything that it's capable of ahead of time. One of the most important things we do is just providing good prompt engineering support. It sounds simple, but 30 minutes with the prompt engineer can often make an application work when it wasn't before, or get better at handling errors.
I always recommend to an enterprise customer just meet with one of our prompt engineers for half an hour. It might completely transform your use case. There's a big difference between demos and actual deployment."
Safety and reliability. Anthropic recently published a paper on jailbreaking models. Amodei also said partnerships with Google Cloud revolve around security and reliability. Enterprises need both reliability and security to scale deployments of generative AI.
Amodei said short term concerns for models revolve around bias and misleading answers when important decisions need to be made in industries like finance, insurance, credit and legal. Overall, Amodei said his concern is how models will become increasingly powerful. He said:
"I think it's going to be possible for folks to misuse models. I worry about misuse of biology. I worry about cyberattacks. We have something called a responsible scaling plan, that's designed to detect those threats which honestly are not really very present today. We're only starting to see the beginning of them. So, every time we release a new model, we run we run them through this. We run tests to see if we are getting any closer to the world where we would be worried about these risks being present in models. And so far, the answer has always been no, but they're a little bit better at these tasks than they were before. Someday, the answer will be yes. And then we have a prescribed set of safety procedures that we'll take on the model. When that is the case, the other side of the risks is as models become more autonomous."
When models become agents and more autonomous, they can take actions without humans overseeing them. "I think there will be very substantial risks in this area, and we'll have to have policies. We'll have to mitigate them," said Amodei, who noted that enterprises will ask about those concerns as much as they do data privacy and hallucinations today.
Large custom models. Amodei said enterprises won't be in a position in the future of choosing a small custom model or a large general one. The correct fit will be a large custom model. LLMs will be customized for biology, finance and other industries.
What needs to happen beyond LLMs to create agents that take actions on your behalf? Amodei said "it's kind of an unexplored frontier." He said:
"One of my guesses is that if you want an agent to act in the world it requires the model to engage in a series of actions. You talk to a chat bot, it only answers and maybe there's a little follow-up. With agents you might need to take a bunch of actions, see what happens in the world or with a human and then take more actions. You need to do a long sequence of things and the error rate on each of the individual things has to be pretty low. There are probably thousands of actions that go into that. Models need to get more reliable because the individual steps need to have very low error rates. Part of that will come from scale. We need another generation or two of scale before the agents will really work."
Editor in Chief of Constellation Insights
Constellation Research
About Larry Dignan:
Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning.
He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.
<br>Constellation Insights does the following:
Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and…
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Microsoft said it is raising the prices for its Dynamics 365 enterprise resource planning and customer relationship management applications.
The company said that Dynamics 365 hasn't seen a price increase in more than 5 years. The price changes go into effect Oct. 1 and range from an additional $10 to $15 more a month per user for most apps, but $30 more for a select apps.
Microsoft's Dynamics 365 price increases apply to cloud and on-premise versions. US government list prices will increase 10% Oct. 1, 2024 and then see a smaller increase Oct. 1, 2025 to be on par with commercial pricing. These price increases don't appear to affect small business customers.
Copilot capabilities delivered in Dynamics 365 are in the core SKUs. Copilot for Service, Copilot for Sales (both GA'd), and Copilot for Finance (in preview) require separate licenses. In other words, if Copilot is part of Dynamics 365 it does not get charged as extra. There are product SKUs called CoPilot for Sales, CoPilot for Service and CoPilot for Finance that are compatible with multiple CRM systems including Salesforce, so those products are the per seat per month Copilots
Here's a look at the changes.
Product
Price before October 1, 2024
Price as of October 1, 2024
Microsoft Dynamics 365 Sales Enterprise
$95
$105
Microsoft Dynamics 365 Sales Device
$145
$160
Microsoft Dynamics 365 Sales Premium
$135
$150
Microsoft Microsoft Relationship Sales3
$162
$177
Microsoft Dynamics 365 Customer Service Enterprise
Vice President and Principal Analyst
Constellation Research
About Andy Thurai
Andy Thurai is an accomplished IT executive, strategist, advisor, enterprise architect and evangelist with more than 25 years of experience in executive, technical, and architectural leadership positions at companies such as IBM, Intel, BMC, Nortel, and Oracle. Andy has written more than 100 articles on emerging technology topics for publications such as Forbes, The New Stack, AI World, VentureBeat, DevOps.com, GigaOm and Wired.
Andy’s fields of interest and expertise include AIOps, ITOps, Observability, Artificial Intelligence, Machine Learning, Cloud, Edge, and other enterprise software. His strength is selling technology to the CxO audience with a value proposition rather than the usual technology sales pitch.
Find more details and samples of Andy’s work on his…
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Last week, executives from Cisco and Splunk, including Liz Centoni, Jeetu Patel, and Tom Casey, held a 45-minute round table where the combined entity outlined their plans for Cisco’s observability future. General opportunities and high-level customer observability pain points were communicated in that discussion. Yet, customers still seek high-level action plans and specific execution details from the merger. While generic customer pain points to observability and security were discussed, the market sought more information about how these major observability platforms would come together. The full video of this roundtable can be seen here -> https://www.youtube.com/watch?v=PsZ2z66i6JI
Tom Casey from Splunk has taken over the product ownership of the Cisco #Observability solution strategy. This move aims to reduce leadership alignment friction by not having competing priorities across #O11y divisions to drive a unified platform. The collaboration between Cisco and Splunk has the potential to provide visibility from the network to the application level, which other observability vendors lack. However, the details of how this will be accomplished are not yet clear.
Splunk has been working hard to integrate its recent acquisitions, including SignalFx, Omnition, Rigor, Flowmill, Plumbr, and VictorOps, into its Observability platform. With Cisco acquiring them, Splunk’s initial direction of keeping SignalFx as a Splunk observability cloud while maintaining the Cloud logs as Splunk Platform (as it was difficult to change the architecture completely to merge them all together) might change. We still don’t know which platform the incoming observability products, such as AppD, Thousand Eyes, and FSO (Full Stack Observability), will move into or merge with. They also diverted investments or decommissioned some acquisitions, including VictorOps and Incident Intelligence to make things simpler (Though engineering and support teams maintain those solutions, product, and strategy teams were eliminated thereby indicating the future of these products may be short-lived).
Given Cisco's history and past experience of integrating observability products, such as AppD and ThousandEyes, and Cisco’s own organic observability platform FSO, and the time Cisco took to streamline operations, field teams, pricing, and create a combined solution, Constellation expects that this new collaboration will take even longer to come to fruition. Many existing Splunk and AppD customers have expressed concerns about how this collaboration will unfold. For example, they are worried about getting the right recommendations from the field/solution teams given many overlapping solutions. Customers are very nervous about the combined Cisco observability solution pricing structure going forward, and whether they will pay a double dip fee to Cisco, which has not been fully disclosed yet. The combination of multiple platforms, add-ons, suites, packaging, overlapping features, and licensing models may confuse the customers, and field teams until the unified pricing structure and full-stack unified platform take shape. These include DEM (Synthetic Monitoring & RUM), APM (Distributed Tracing), metric stores, tracing stores, session replay capabilities, Infrastructure monitoring, and log capabilities overlap along with Splunk having their own powerful query language (SPL) which Cisco’s observability solutions lack. Cisco should proactively take the time to clearly explain these outcomes to customers and properly execute on it with specific defined milestones.
Furthermore, both companies claim that the acquisition is to catch up with AI demands. Yet, neither of them is a leader in infusing AI into their Observability or AIOps solutions. There are other competing vendors ahead of Cisco/Splunk with their generally available AI use cases, which Cisco/Splunk both need to catch up with. For instance, Splunk AI assistant (formerly SPL Co-Pilot), introduced in .conf23, is still in preview mode and constitutes a very basic use case of using a natural language interface to produce SPL (Splunk query language) used in observability data searches. Cisco's AI does not perform any observability-related tasks yet. It will be interesting to see how many AI use cases they can support quickly to catch up with the market.
Since a significant portion of Splunk's revenue comes from their ARR, this could help Cisco launch into the ARR model, which they have been trying to expand for the last few years.
Constellation POV
Based on our conversations with existing Splunk and Cisco customers, and Splunk ex-employees, Constellation believes that the integration faces many challenges. Constellation expects that the combined entity will take at least two years to complete post-merger integration in a manner that users will see the benefits.
Although the Cisco/Splunk team has said all the right things so far, execution will be critical, and it could be painful and slow, which may cost some large accounts that are already experimenting with competing solutions. Constellation believes that the overall merger will bring benefits to customers and partners, but be prepared for a much longer than expected post-merger integration, given the different architectures, consumption models, data collected, culture, and technical debt accrued over the years.
At first glance, the idea of combining Security with Observability seems to be a good one, and it aligns well with Splunk's ongoing mission before the acquisition. Bottom line – while this high-level strategy sounds promising, it needs more details to be fully understood and value realized.
Vice President & Principal Analyst
Constellation Research
About Liz Miller:
Liz Miller is Vice President and Principal Analyst at Constellation, focused on the org-wide team sport known as customer experience. While covering CX as an enterprise strategy, Miller spends time zeroing in on the functional demands of Marketing and Service and the evolving role of the Chief Marketing Officer, the rise of the Chief Experience Officer, the evolution of customer engagement and the rising requirement for a new security posture that accounts for the threat to brand trust in this age of AI. With over 30 years of marketing, Miller offers strategic guidance on the leadership, business transformation and technology requirements to deliver on today’s CX strategies. She has worked with global marketing organizations to transform…
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When Adobe acquired Frame.io, it was chalked up as just another Creative Cloud solution that was so niche and specialized only people with expensive cameras and the agencies that hire them would reap the rewards. But in the wake of the announcement in 2021, I blogged a hot take:
“Imagine what happens when Adobe pulls the best of the best from BOTH Workfront AND Frame.io to reimagine what collaboration for creativity and experience really works like. Only time will tell how far collaboration will connect the two sides of the Adobe coin…If anything can bridge that gap in a meaningful way, it just might be collaboration and workflows.”
I WAS RIGHT. IT IS HAPPENING!
That gloat felt good. Now back to the news at hand.
Adobe’s Frame.io V4 takes collaboration to the next level, focused on the work creative professionals must synch, share, comment on and coordinate to create new experiences. From will.i.am creating a new music video to a brand marketer creating a new story driven transmedia campaign, V4 has both the asset and the process covered. Much like the other updates and modernizations across Creative Cloud, the reimagination of Frame.io has me feeling the rage only true jealousy can bring on.
Let me explain: Many moons ago, I worked on a rebrand for a cosmetic product that required an extensive shoot involving multiple models with unique-yet-natural looks to satisfy a year-long campaign involving photo and video assets. The shoot was booked with an agency, a videographer, a casting agent and a photographer in Cape Town, South Africa…I was in Campbell, California. Let the creative chaos games begin. Briefs were shared, mood boards, story boards and concept briefs passed around for what felt like lifetimes.
As these types of creative jobs go…the shoot happened when I was sound asleep thanks to time zones so when I got the test shots 48 HOURS later, I had to send that “delicate” email of “The brief clearly outlined casting and I approved the first round of models. All the test shots you sent back are of totally different models in completely different scenes and nowhere near what was outlined on the boards?”
Days would be lost in the name of collaboration. Chaos was the norm in the name of asset and file sharing. Budget was lost to misinterpretation.
This new version of Frame.io enables that entire chaotic scenario to become a streamlined workflow centered around an easy to view and review interface, common centralized asset storage and intentionally uncomplicated processes to consolidate the work of creation. I’m secure enough to admit that how elegantly Frame.io reframes the chaos makes me more than a little jealous. It takes hold of the process from casting through to file transfer and sharing, delivers a single pane for commenting and collaborating and intentionally works to accelerate the process with alerts and aggregated comment drawers for smooth signoffs and approvals.
Version 4 also comes with a new single metadata framework that underpins everything allowing all assets, data and collaborators come together in a single, unified platform. Now every piece of the process can exist as metadata on an asset or file. Loved working with an actor you met in casting…that lives on that video. Want to only view dailies by scene or actor…yup…that’s metadata that can live with an asset and be easily searched. Frame.io extends the power of a metadata framework with Collections that aggregates and segments by that metadata.
Let’s follow the bouncing ball of my gloating once more and close your eyes to imagine just how powerful search becomes as this metadata framework extends beyond Frame.io into, for argument’s sake, a Digital Asset Management (DAM) solution like Adobe Assets or a workflow and work management solution like Workfront?
Don’t worry…you won’t have to worry all too long as Frame.io’s integration with Workfront is expected to be released later this year, enabling a new unified review and approval workflow between cross-functional teams. For marketers, agencies and brand leaders, we are talking about visibility and work that connects CAMERA to CAMPAIGN! That’s where this is heading!
Frame.io V4 beta is rolling out in stages for Free and Pro customers across web, iPhone and iPad across 2024 with Team and Enterprise customers expected to get the V4 update later in the year. In a video blog announcing V4, Frame.io’s Founder, Emery Wells, also shared a simplification of the pricing model for the new version.
This is the fourth iteration of Frame.io since the product launched in 2015 and the biggest update the company has ever introduced, reimagining the platform from the ground up but remaining grounded in their customers asks and innovations. Clearly, this whole “expand workflows so the processes of casting, scouting, and dailies review” makes me mutter like an old lady under my breath with that “BACK IN MY DAY” lament. But it really can’t be overstated just how much this work needs this overhaul. We need to reimagine the work and workflows of creatives and creators with tools that don’t just start and stop with outputs and assets but truly connects the totality of this work we call creation.
Image generated by Adobe Firefly (and my sick prompt skillz)
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…
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Former Vice President and Principal Analyst
Constellation Research
Doug Henschen is former Vice President and Principal Analyst where he focused on data-driven decision making. Henschen’s Data-to-Decisions research examines how organizations employ data analysis to reimagine their business models and gain a deeper understanding of their customers. Henschen's research acknowledges the fact that innovative applications of data analysis requires a multi-disciplinary approach starting with information and orchestration technologies, continuing through business intelligence, data-visualization, and analytics, and moving into NoSQL and big-data analysis, third-party data enrichment, and decision-management technologies.
Insight-driven business models are of interest to the entire C-suite, but most particularly chief executive officers, chief digital officers…
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Vice President and Principal Analyst
Constellation Research
Holger Mueller is VP and Principal Analyst for Constellation Research for the fundamental enablers of the cloud, IaaS, PaaS and next generation Applications, with forays up the tech stack into BigData and Analytics, HR Tech, and sometimes SaaS. Holger provides strategy and counsel to key clients, including Chief Information Officers, Chief Technology Officers, Chief Product Officers, Chief HR Officers, investment analysts, venture capitalists, sell-side firms, and technology buyers.<br>
Coverage Areas:
Future of Work
Tech Optimization & Innovation<br>
Background:
Before joining Constellation Research, Mueller was VP of Products for NorthgateArinso, a KKR company. There, he led the transformation of products to the cloud and laid the foundation for new Business…
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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…
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Vice President and Principal Analyst
Constellation Research
About Andy Thurai
Andy Thurai is an accomplished IT executive, strategist, advisor, enterprise architect and evangelist with more than 25 years of experience in executive, technical, and architectural leadership positions at companies such as IBM, Intel, BMC, Nortel, and Oracle. Andy has written more than 100 articles on emerging technology topics for publications such as Forbes, The New Stack, AI World, VentureBeat, DevOps.com, GigaOm and Wired.
Andy’s fields of interest and expertise include AIOps, ITOps, Observability, Artificial Intelligence, Machine Learning, Cloud, Edge, and other enterprise software. His strength is selling technology to the CxO audience with a value proposition rather than the usual technology sales pitch.
Find more details and samples of Andy’s work on his…
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"You can't go one minute without hearing about hashtag#AI."
We got the Constellation crew together to hear overarching themes of hashtag#GoogleCloudNext across every coverage area: hashtag#cybersecurity, hashtag#cloud applications, hashtag#data to decisions, hashtag#observability, and hashtag#generativeAI.
Here are a few observations from Google's announcements and hashtag#market positioning:
☁ Google's AI hashtag#technology is making cybersecurity more accessible (i.e. copilots, agents, etc.)
☁ Google Cloud has a 2-3 year lead on its competition by putting custom silicon on custom chips (hashtag#TPUs)
☁ Google offers one AI-ready data platform (including AI, ML, and GenAI) that combines structured and unstructured data.
☁ Google offers a super infrastructure to train all sizes of hashtag#LLMs, customers can fine-tune and customize existing LLMs for a few hundred dollars.
☁ Google offers one of the only open AI stacks from one vendor.
A few takeaways for our hashtag#executive audience:
Customers should already be considering how AI technology and hashtag#cloud platforms can drive hashtag#business outcomes in their hashtag#enterprise. hashtag#CXOs must think beyond traditional data silos and invest in platforms supporting a continuum of structured and unstructured data. And finally, re: Google Cloud Next - Google offers an easier way to build models at a cheaper price.
Watch the full interview below with Holger Mueller, Doug Henschen, Andy ThurAI, Chirag Mehta, and R "Ray" Wang.
Editor in Chief of Constellation Insights
Constellation Research
About Larry Dignan:
Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning.
He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.
<br>Constellation Insights does the following:
Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and…
Read more
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."
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."
Editor in Chief of Constellation Insights
Constellation Research
About Larry Dignan:
Dignan was most recently Celonis Media’s Editor-in-Chief, where he sat at the intersection of media and marketing. He is the former Editor-in-Chief of ZDNet and has covered the technology industry and transformation trends for more than two decades, publishing articles in CNET, Knowledge@Wharton, Wall Street Week, Interactive Week, The New York Times, and Financial Planning.
He is also an Adjunct Professor at Temple University and a member of the Advisory Board for The Fox Business School's Institute of Business and Information Technology.
<br>Constellation Insights does the following:
Cover the buy side and sell side of enterprise tech with news, analysis, profiles, interviews, and event coverage of vendors, as well as Constellation Research's community and…
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Meta launched its next-generation training and inferencing processor as it optimizes models for its recommendation and ranking workloads.
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
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.
"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.
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…
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The following eight interviews are between Constellation Research founder and analyst R "Ray" Wang and customers attending the 2024 #GoogleCloudNext conference in Las Vegas, Nevada. They discuss the Google keynotes, main takeaways, future business implications, and more.
Those were some of the takeaways from Amodei, who spoke at Google Cloud Next.
