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…
Read more
You'll have to remove that Meta Horizons Workroom meeting from your calendar after Feb. 16, 2026. Oh you didn't have one scheduled? Apparently no one else did either.
Now if you wanted to have a meeting through your Meta Quest headset you could use apps from Microsoft, Zoom and Arthur to conduct a metaverse meeting.
Meta's latest retreat from the workplace comes as The New York Times reported the company laying off workers at its reality labs unit and said that it will stop selling Quest headsets and Horizon services to businesses. Meta exited its Workplace business in 2024 and transitioned customers to Zoom's Workvivo platform.
The company is focusing on its AI efforts as it cuts in its metaverse spending. Fun fact, Meta changed its entire brand to focus on the metaverse in 2021. At the time, Meta CEO Mark Zuckerberg said the "metaverse will eventually encompass work, entertainment, and everything in between."
Chief Distiller, Board Advisor
Constellation Research
Esteban Kolsky spent the first part of his professional career working on the customer service front line and in management roles, culminating in early implementations of first-generation CRM tools. In 2000, he became a research analyst at Gartner, focused on CRM, customer service, customer experience management, enterprise feedback management, and voice of the customer, among topics that later became the basis for the current field of CX. During his tenure at Gartner, Kolsky assisted vendors and Global 2000 organizations with their customer strategies.
He left Gartner to focus his career on strategic advisory services and influenced all the major software vendors, as well as many smaller vendors, in the CX industry and advised executive leadership teams across industries and countries…
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This post is a very short summary of the last three “Enterprise Technology Intelligence Briefing” books. In that monthly report, we track the enterprise technology topics that matter to executives and boards, and lately, it has been AI. Of course.
Much has been discussed, but succinctly, today's situation can be summarized in four points:
Generative AI is reaching (has reached, could be argued) its natural limitations. Both on economic viability and on the potential to address an ever-smaller number of use cases.
Agentic AI has promise, if only core governance issues can be resolved. The most critical ones are cybersecurity, privacy, autonomy, and availability.
The enterprise infrastructure in place today is not ideal. After decades of technical debt sprawl and patch-up jobs, digital integration is the most critical problem.
There is a bright future for AI in the enterprise, if only the multiple proper data models + technology infrastructure combinations can be quickly scaled and adapted to diverse use cases.
While the answer to this quandary will take far longer than 12 months to reach a conclusion -- this is after all a transformative event similar to the internet, cloud infrastructures, and digital transformation -- there are some things enterprises can do today to be prepared for the AI Transformation to come:
Digital readiness
Technology executives must understand and ensure that proper data flows across all stored data are available.
Determine which tools have the necessary permissions and access to the required resources, and how the enterprise can retain control, not the host vendors.
None of these matters if the outcomes are unsustainable. Monitor and enforce the economics of accessing, using, updating, and storing the data and processes.
AI optimization
There is not a single AI model or tool that can do it all. Explore Generative and Agentic AI as well as alternative models and solutions for redesigned use cases and processes.
Explore frontier technologies like edge computing to evaluate the likelihood of sustainable AI operations and reduce operational costs.
For cases where Agentic and Generative AI are the right solutions, optimize economics and outcomes by automating while ensuring proper human supervision.
Infrastructure maturation
Audit the technology stack. Update the documentation to focus on how to create a private platform; determine what else is necessary.
Understand current contracts and economics of all cloud providers: vendors, providers, and hyper-scalers. Create a master strategy for private and public clouds.
Document all places where privacy, access, rights, roles, and compliance are necessary, and centralize it into one common policy place. This is the base for the private platform.
Strategic thinking
As with all transformations, the technology is secondary to the strategy. Create one-year, three-year, five-year, and long-term plans for AI and digital needs.
Determine the available and necessary talent to deliver on those strategies. This is the most common overlooked aspect of strategy creation: how to deliver them.
In a recent study, actually, a few of them, the most striking finding was that executives don’t believe their fellow executives and their boards are sufficiently tech- and AI-savvy. Understand your enterprise situation.
If you are wondering how my organization can do all this over the next 12 months, the good news is that the next 12 months (2026, basically at the time of this writing) is about preparing for the long haul, not achieving all these outcomes. Audits, documentation, strategy alignment, and long-term planning are to be built on top of these blocks, and the enterprise will begin to lay out in the coming year. And maybe by then, geopolitical and economic uncertainties holding back the enterprise from investing heavily will become clearer, revealing their direction and outcome.
The north star for the enterprise's focus on AI in 2026 is adopting a private platform approach to the cloud, the internet, data, AI, and all things technology and processes. A combination of private- and public-cloud components that will enable the enterprise to leverage cloud providers and hyperscalers while retaining control and governance, the private platform is the core model on which AI (and Quantum, Robotics, and other technological evolutions in the next few decades) will be based.
The biggest concern? Creating the right strategy and ensuring Executive Tech Know-how is up to date. Secondary? Resources and talent.
Want to go deeper into these topics? Will continue doing so in this blog and in the ETIB reports throughout the year. Talk to us, we can help you.
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
AWS European Sovereign Cloud is generally available and the cloud provider is planning to expand from Germany to Belgium, the Netherlands and Portugal.
AWS is focusing on sovereign cloud as various countries adopt data residency regulations. AWS European Sovereign Cloud is physically and logically separate from other AWS regions yet offers all the services as other clouds.
According to AWS, EU customers will have complete control over the location and movement of data. These customers will also have low latency via a local cloud.
The plan going forward is to connect AWS European Sovereign Cloud to AWS Local Zones to give customers options to deploy workloads with sovereignty and operational independence with all of AWS services. AWS Local Zones enable enterprise to store data in a geographic location for data residency requirements.
AWS noted that AWS European Sovereign Cloud has a dedicated governance structure and is run by EU citizens. Launch partners include Accenture, adesso, Adobe, Arvato Systems, Atos, Capgemini, Dedalus, Deloitte, Genesys, Kyndryl, Mistral AI, msg group, Nvidia, SAP, SoftwareOne and others.
Constellation Research analyst Holger Mueller caught with Mustafa Isik, Chief Technologist Sovereignty at AWS. Here's a look at the takeaways on AWS' sovereign cloud plans in Europe and beyond.
AWS European Sovereign Cloud. Isik said AWS has been in Europe for years, but the focus is now on taking infrastructure that has solved problems for customers for years and rebuilding it in the EU. AWS European Sovereign Cloud is based in Brandenberg and the location is the equivalent of the North Virginia location in North America. Isik said AWS will expand its sovereign cloud footprint across Europe in Belgium, the Netherlands and Portugal. "With AWS European Sovereign Cloud, you get a full blown AWS region," said Isik.
A sovereign data and cloud and local talent. Isik noted that AWS has built AWS European Sovereign Cloud with local talent. "We have hired many new colleagues and enabled them. These are system engineers, administrators, software developers, and across the board in terms of IT jobs," said Isik. "These colleagues that we have hired within the EU, are EU residents, and can only assume their operational role while they're physically present in a member state of the EU. These people are the only ones who will be operating the infrastructure and the services of the European Sovereign Cloud.
AI in Europe. AWS European Sovereign Cloud will have all of the AI and machine learning services found in any AWS cloud including Amazon SageMaker, Amazon Bedrock and the full suite of infrastructure offerings. "It comes with AI services from day one and that's a powerful proposition that customers from highly regulated industries and public sector have been waiting for," said Isik. He added that AWS European Sovereign Cloud has its own legal structures for handling data and customers can opt out of data used for inference, tuning and other use cases.
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
CEOs are now driving AI strategy at enterprises and half of them think their jobs are on the line if they don't get it right.
72% of CEIS say they are the main decision-makers on AI.
Half of CEOs think their job depends on getting AI right94% will continue to invest in AI even if it doesn't deliver returns.
And 90% of CEOs think AI agents could produce measurable returns in 2026.
Leaders confidence in AI is higher in India and Greater China than in the West.
BCG said in a report:
"About 90% of CEOs believe that by 2028, AI will redefine what success looks like within their industry. Companies will move beyond simply deploying AI in everyday tasks to reshaping critical workflows and, for many, inventing entirely new business models."
Constellation Research analyst Esteban Kolsky noted in his latest boardroom missive that AI success has become an initiative that has moved up the C-suite. He said:
"AI is a transformation initiative for the enterprise for the next 20 years and is itself being transformed from the basic “GenAI using public models†pilots to a more complete organizational redesign strategy. This transformation will take more than a decade to materialize, with quantifiable steps along the way. Its contextual significance is represented in the boundaries and connections boards and executives must place on AI starting now: It is about the outcomes, it is about
leveraging the private platform infrastructure IT is building, and it is about optimizing (and redesigning) the processes that are used to run the business via a control plane."
What could go wrong with CEOs making the AI calls? A few ideas:
First, CEOs in this BCG report look a bit overconfident. CEOs have stronger conviction on AI than their technology executives.
Technology executives that raise concerns may be benched by CEOs and lead to vulnerabilities.
Agentic AI is still immature and requires technology expertise and architecture to work well.
CEOs may be driven by short-term results and forget to play the long game.
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
Wikimedia, the foundation behind Wikipedia, said the company has added Microsoft, Mistral AI, Perplexity and others as Wikimedia Enterprise partners to join Amazon, Google and Meta. The Wikimedia Enterprise traction highlights how human-driven content carries a premium.
The additional customers of Wikimedia Enterprise highlight the importance of Wikipedia for training large language models. Wikimedia Enterprise provides the datasets of Wikipedia and its sister projects as well as enterprise APIs that include snapshots, on-demand pulls and streaming updates.
Wikimedia Enterprise along with donations and other fundraising supports its human contributors as well as technology infrastructure. The non-profit disclosed the Wikimedia Enterprise customer additions in a blog post outlining its 25th birthday.
Human-driven content has also driven the success of Reddit, which has LLM partnerships with Google and OpenAI. Reddit has become the No. 4 largest web property.
On Reddit's third quarter earnings call, CEO Steven Huffman said the company's LLM relationships are "healthy and collaborative" and mutually improve each other’s products.
Jennifer Wong, Reddit Chief Operating Officer, summed up the importance of human content. "I think Reddit's corpus of information is clearly incredibly valuable and helpful to LLMs because it's human conversation that's fresh, it's authentic. It's just distinctive. There's nothing like it. And we know that LLMs appreciate Reddit's conversation," said Wong.
She added that Reddit is focused on developing its business on its own property. LLMs are a secondary product. "What the marketers are getting is real value from the Reddit platform itself in terms of converting that engagement to real business outcomes for them. It just so happens that, that environment of that conversation is also appreciated by LLMs," said Wong.
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
MongoDB said it is integrating its Voyage 4 embedding and reranking models into its platform infrastructure in a move that will improve accuracy, deliver better context and help developers scale applications.
With the moves, announced at MongoDB.local San Francisco, MongoDB is looking to grab a bigger piece of the AI applications pie. MongoDB is also creating a unified data intelligence layer for production AI in a move that will enhance its enterprise footprint.
MongoDB's Ben Cefalo, SVP, Head of Core Products and Atlas Foundational Services, said the company has worked with customers to understand where things break as AI moves from prototype to production. "Those conversations rarely start with AI models. They start with really practical questions like, how do you get our data ready? How do we keep things performant as we scale? How do we ensure accuracy of results? How do we avoid gluing together five different systems," said Cefalo. "What's interesting is that customers increasingly do not think of MongoDB as just a database. They reframe the database as a foundation for their AI stack. They are increasingly thinking of MongoDB as their strategic data platform."
Cefalo added that the main goals of developers are to scale and operate applications at scale with minimized risk of hallucinations without the need to move data. MongoDB is unifying databases, vector stories and model APIs on one platform.
Here's what MongoDB announced:
New embedding models from Voyage AI, which was acquired a year ago and serves as MongoDB's embedding and retrieval model suite. MongoDB said its Voyage 4 models are generally available including the general purpose voyage-4 embedding model, voyage-4-large for retrieval accuracy, voyage-4-lite for optimized latency and cost and the open weight voyage-4-nano for on-device applications.
Voyage-multimodal-3.5 is generally available to extract context from video, images and text. The model vectorizes multimodal data to capture semantic meaning from various documents.
MongoDB's Atlas Embedding and Reranking API expose Voyage AI models natively within Atlas.
Auto Embeddings for MongoDB Vector Search, which automatically generates and stores embeddings using Voyage AI whenever data is inserted, queried or changed. The feature eliminates the need for separate embedding pipelines and external model services. Auto embedding is in public preview for MongoDB drivers in various frameworks, available in MongoDB Community and coming to MongoDB Atlas soon.
An intelligent assistant for MongoDB Compass and Atlas Data Explorer is now generally available.
Separately, MongoDB expanded its MongoDB for Startups program, which is designed to enable startups to scale on its platform. Initial launch partners include Temporal and Fireworks AI.
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
Vice President and Principal Analyst
Constellation Research
Martin Schneider has had a unique career that has spanned both analyst and marketing practitioner roles, focused on high technology and related industries. The unifying factor has always been both a keen analysis of go-to-market trends, while also having achieved success as a marketing leader.
Schneider started his career as a journalist covering B2B technologies, and quickly transitioned into a leading analyst covering application software for the 451 Group in NYC, where he specialized in CRM, marketing automation, and business intelligence/analytics technologies. After analyzing various go-to-market strategies of dozens of technology vendors, Schneider made the move to the vendor side, where he led successful go-to-market teams for several startups and established tech providers,…
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Vice President and Principal Analyst
Constellation Research
Michael Ni is Vice President and Principal Analyst at Constellation Research, covering the evolving Data-to-Decisions landscape—where CDOs, CIOs, and CPOs must modernize data infrastructure, integrate AI into decision-making, and scale automation to improve business outcomes.
Ni’s research examines how enterprises operationalize AI, automate decision-making, and integrate data management and analytics into core business processes. He focuses on the challenges of scaling AI-driven decision systems, aligning data strategy with business goals, and the growing role of data and decisioning “products” in enterprise ecosystems.
With 25+ years as a product and GTM executive across enterprise software, AI platforms, and analytics-driven technologies, Ni brings a practitioner’s perspective to…
Read more
Chief Distiller, Board Advisor
Constellation Research
Esteban Kolsky spent the first part of his professional career working on the customer service front line and in management roles, culminating in early implementations of first-generation CRM tools. In 2000, he became a research analyst at Gartner, focused on CRM, customer service, customer experience management, enterprise feedback management, and voice of the customer, among topics that later became the basis for the current field of CX. During his tenure at Gartner, Kolsky assisted vendors and Global 2000 organizations with their customer strategies.
He left Gartner to focus his career on strategic advisory services and influenced all the major software vendors, as well as many smaller vendors, in the CX industry and advised executive leadership teams across industries and countries…
Read more
CRTV kicks off 2026 with Episode 121, focusing on the reality of enterprise AI, decision velocity, and the hype vs. value of humanoid robots at CES. In this episode:
AI, M&A, and Precision Data
Martin Schneider and Larry Dignan break down recent AI-driven M&A and why the real competitive edge is shifting toward secure, precision data and job-to-be-done–focused agents rather than generic AI.
Decision Velocity
Larry talks with Mike Ni, Constellation’s analyst for Data to Decisions, about:
What decision velocity really means
Moving from POCs to proof of value
Why context engineering is replacing prompt engineering as the AI pressure point
How culture, architecture, and governance must evolve for decision-centric enterprises
Humanoid Robots: Hype vs. Reality
Esteban Kolsky joins to explain why humanoid robots are mostly a bad business idea:
Inefficient, expensive, and usually optimized for a single function
Why function-specific robots (UPS, Walmart, Amazon-style warehouses) deliver real ROI
How CES and the “humanoid bubble” are accelerating the reckoning
00:56 – News & AI Trends with Martin and Larry
09:50 – Decision Velocity with Mike Ni
15:56 – Humanoid Robots & Robotics ROI with Esteban Kolsky
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
Vice President and Principal Analyst
Constellation Research
Michael Ni is Vice President and Principal Analyst at Constellation Research, covering the evolving Data-to-Decisions landscape—where CDOs, CIOs, and CPOs must modernize data infrastructure, integrate AI into decision-making, and scale automation to improve business outcomes.
Ni’s research examines how enterprises operationalize AI, automate decision-making, and integrate data management and analytics into core business processes. He focuses on the challenges of scaling AI-driven decision systems, aligning data strategy with business goals, and the growing role of data and decisioning “products” in enterprise ecosystems.
With 25+ years as a product and GTM executive across enterprise software, AI platforms, and analytics-driven technologies, Ni brings a practitioner’s perspective to…
Read more
As we move into 2026, the question for every executive is: How do we actually measure the success of our technical processes?
It’s not about the depth of your models; it’s about Decision Velocity. In this interview with Constellation analysts Larry Dignan and Michael Ni, we explore why this metric is the key to closing the gap between a pilot program and a successful enterprise-wide implementation.
The Decision Velocity Framework:
Accuracy: Ensuring the reliability of automated returns.
Efficiency: Reducing human-in-the-loop dependencies and removing unnecessary steps.
Contextual Impact: Tailoring metrics to the specific process, whether it's decreasing human-handled call volumes or boosting marketing campaign returns.
Vice President and Principal Analyst
Constellation Research
Michael Ni is Vice President and Principal Analyst at Constellation Research, covering the evolving Data-to-Decisions landscape—where CDOs, CIOs, and CPOs must modernize data infrastructure, integrate AI into decision-making, and scale automation to improve business outcomes.
Ni’s research examines how enterprises operationalize AI, automate decision-making, and integrate data management and analytics into core business processes. He focuses on the challenges of scaling AI-driven decision systems, aligning data strategy with business goals, and the growing role of data and decisioning “products” in enterprise ecosystems.
With 25+ years as a product and GTM executive across enterprise software, AI platforms, and analytics-driven technologies, Ni brings a practitioner’s perspective to…
Read more
Enterprise AI is shifting from insight to execution, and integration is becoming mission-critical.
Constellation analyst Mike Ni and IBM's Scott Brokaw discuss why yesterday’s human-centric, storage-first data architectures are failing AI agents and how unifying structured and unstructured data with strong governance, lineage, and access control unlocks real business impact.
Learn why data & AI leaders must simplify their tooling, adopt a horizontal integration control plane, and design for deterministic, reliable execution at scale so AI can safely close the loop between data, decisions, and actions.
Chief Distiller, Board Advisor
Constellation Research
Esteban Kolsky spent the first part of his professional career working on the customer service front line and in management roles, culminating in early implementations of first-generation CRM tools. In 2000, he became a research analyst at Gartner, focused on CRM, customer service, customer experience management, enterprise feedback management, and voice of the customer, among topics that later became the basis for the current field of CX. During his tenure at Gartner, Kolsky assisted vendors and Global 2000 organizations with their customer strategies.
He left Gartner to focus his career on strategic advisory services and influenced all the major software vendors, as well as many smaller vendors, in the CX industry and advised executive leadership teams across industries and countries…
Read more
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
Is the Humanoid Robot Trend a Total Bubble?
Constellation analysts Larry Dignan and Esteban Kolsky examine the "parade of robots" at CES 2026 to separate genuine innovation from marketing hype. While Jensen Huang and others may be walking out with dozens of humanoids, Esteban argues that the focus on the human form is a "horrible idea" driven more by Hollywood lore and investor optics than by actual utility.
In this interview, they discuss:
Why the human form is inefficient: Why replicating 17,000 receptors in a robotic hand is a "dexterity trap."
The "Ugly" Winners: Why UPS and Walmart are winning by using specialized, non-humanoid robots for logistics and "dark warehouses."
The Cost of "Dancing": Why marketing stunts like dancing quadrupeds are fun to watch but offer zero enterprise value.
The Future of AI & Robotics: The inevitable merger of these two fields and how to ensure your investment returns money instead of just headlines.
Key Quote: "Your goal is to create something that's useful or something that gets you money. Humanoids get you money, but they're not going to be very useful." — Esteban Kolsky
Stay ahead of the curve: Subscribe for more deep dives into the Constellation pipeline as we track the innovations shaping the enterprise in 2026 and beyond!
