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In this video, Cohesity CEO Sanjay Poonan tells The Forecast about advice he got from Nvidia CEO Jensen Huang that led him on a journey to build a multi-billion-dollar, AI-powered data security company.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Jason Lopez: Cohesity is a cybersecurity company led by Sanjay Poonan. He told The Forecast how he came to Cohesity on advice from Nvidia, CEO, Jensen Huang.

Sanjay Poonan: He said something that really rang with me, which is, you've done the big company stuff, 20 billion, 10 to 20 billion at SAP, six to 12 billion at VMware. You haven't taken a small company and made it big, so you should try that. And for me it was a little bit of like, can we take something that was small and make it an iconic company? SAP and VMware were already iconic when I got there, and we of course made it even better. That's what I saw as the opportunity of Cohesity. It was a world-class company. It was about a $300-million company when I joined, with fantastic technology and an incredible founder. In fact, many of the customers told me it was the best tech they'd seen since VMware, so there was no tech risk there. We needed to scale the go-to market to get it to be a multi-billion and the leader in the space. We were number seven. So I saw a tremendous opportunity at the center of this notion of data security because of ransomware attacks and all the pressure on data to create a leader, and that's what we're off doing today.

[Related: Composable Data Centers That Power Enterprise AI]

The entire product is based on notions of machine learning and AI. Before generative AI, the founders were Google folks who built a lot of key capabilities of ML and AI into the product. A lot of these cybersecurity data detection of entropy and anomaly detection are AI algorithms. So that was from the get go, built to be a key part to the proposition of the product driven by Generative AI. We started working with Nvidia. Nvidia made one investment in this space. They looked at all the tech companies and picked us to do an equity investment because we cracked an important problem of how you could use retrieval augmented generation, also known as RAG, directly on secondary and backup data. We invented that. We in fact patented the idea. Nvidia loved it, and we were featured at GTC in both 2024 and 2025. Jenssen's words were Cohesity backs up the world's data and they're building their RAG application called Cohesity Gaia on top of the Nvidia platform. We're working with them and Google and Microsoft and Amazon who are also those four companies, I think pioneers in what the world of AI is going to look like. You can imagine a problem like this where hundreds of millions of PDF documents sit in our backup. You can easily query that, summarize it, get insights into sort of flying a flashing of torch light right into your data. That is going to be a tremendous advantage to not just keeping the data secure, but also getting insights into it.

[Related: Raising Ransomware Resistance]

Today, the number one problem is data resilience. People's data is under attack from nation state actors, cyber criminals, and we work with 13,000 customers. The largest of the largest 85% of the Fortune 100, 70% of Global 500, and it's the name brand and financial services, tech, telco, healthcare, you name it. The public sector are our customers. So their number one priority is keeping their data safe from ransomware attacks. And we've designed something that's got the fastest cyber recovery and enormous amount of advanced security. Now, what they want to be able to do is, let's take that a step further and using AI tools, get insight into that data, and we have a very good sort of two-pronged engine approach to how we approach our company's ambitions. Number one, we innovate to be the best product in the industry, and that's been always the pride of Cohesity, I hope, and we will continue to do that.

[Related: Protecting Against Ransomware at the Data Level]

Number two, we obsess about our customers. We get an anchor customer, particular vertical. We study their needs, we drive what they need. And then every customer, that vertical becomes automatically like that anchor customer. You pick the largest bank, make 'em successful, pick the public sector agency and the federal, the biggest telco, the biggest tech company, the biggest hospital. And when you do that, you can repeat that. That's the playbook I learned at SAP and at VMware and now you get to apply it at a smaller company. We've gone from 300 million now with this major acquisition we've done at Veritas. We're now on the path to being a 2 billion company and the biggest space, number one, but the enormous amount of innovation and we're pouring a lot of engineering including into our exciting new work we're doing on Nutanix, which I'm very excited about.

Men’s health is facing a silent crisis, due in part to cultural barriers that prevent men from seeking the mental health care they need. The result is a widening life expectancy gap as the number of suicides and drug overdoses grows.

In this episode, Richard Reeves shares why conversations about men’s health and social connection are critical to reducing these deaths of despair. Offering inclusive approaches to health care and well-being is essential to improving both personal and workforce well-being.

Guest: Richard Reeves, President, American Institute for Boys and Men

In a video interview, Nutanix’s Lee Caswell tells how IT customers need to meet changing needs as they embrace AI capabilities.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Jason Lopez: In between press briefings, Lee Caswell tells The Forecast about some of the biggest IT trends, challenges and innovations explored at the 2025 .NEXT event in Washington, D.C.

Lee Caswell: We're really interested in that customers get value out of AI. And this is one of the things where a lot of customers today are a little bit stalled trying to figure out, well, hey, I don't want to spend too much. I don't want to spend on the wrong thing. How do I get started, right? And how do I also make sure that the AI that I bring to market has the same enterprise level resilience, the same day-to-day operations, the same security and privacy that I bring for all of my traditional apps. How do I make AI just the next enterprise app? And so we're helping customers basically with choice. So the idea is, what are the fast-changing parts? GPUs are changing pretty fast, so we're going to give you the access to the GPUs or CPUs with acceleration, for example. LLMs are changing fast. Last year, we gave you almost an app store, if you will, into LLMs from our partners Hugging Face and NVIDIA. This year, now we're supporting agentic workloads, which is an iterative cycle where it's not enough to just get an answer. Now, I want to iterate on that answer and give you more relevant results, maybe through guardrails, maybe through re-ranking, maybe through embedding. And so we're giving you all of those access points so you can get started today and have a production-ready enterprise experience.

Jason Lopez: Caswell talked about how enterprises are onboarding AI and managing data privacy.

Lee Caswell: Well, I think first off, we're helping customers understand the fast pace of the AI market. So most customers today realize that large language models, or they don't have to be that large, it could be SLMs as well, will be developed either in the public cloud or by some of the largest customers because of the incredible capital expense of training a model. So now customers are looking and saying, well, all right, I'm probably going to get access to a model, but how do I make sure now that my data remains private? It could be, for example, that your model becomes more sensitive than the data itself because the model now starts showing you're inferencing your ideas, your insights into the data that you have. And so that idea of saying, it's going to be important to access large language models and change them out. I want to have access to that. We provide that capability. And then I also want to have this ability to go and have more responsible results and be able to go and manage the infrastructure so that it's actually an enterprise-level experience. That's how we're helping customers basically get started with our Nutanix AI, enterprise AI products.

Jason Lopez: He tells how Nutanix software helps enterprises build and run a future-ready IT operation.

Lee Caswell: Yeah, it turns out, right, you know, the quality of our engineering has been really something that's just been delightful to see as we're bringing all the elements necessary to bring AI into the enterprise. And so that could be, for example, like the storage, right? You have to ingest that data. You've got to run the model. You've got to archive the data. How do you do all of that together? We provide an offer for that. In addition, by the way, many of these models are containerized. And so as you bring containers and Kubernetes, for many customers, these are relatively new concepts into the on-prem world. How do you leverage what you know already today? And here we acquired a company called D2IQ, and we have a terrific set of engineering leadership resources there who are now taking that, what was a production shipping product, and now adding Nutanix data services. It's an incredibly compelling offer and part of the complement of products you need to make AI successful.

In this video interview, Liqid CTO Sumit Puri explains why dynamic IT infrastructure that taps into pools of GPUs and scale-up memory can quickly and efficiently run virtual machines, containers and AI workloads on-premises and at the edge.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Transcript:

Jason Lopez: Liquid is a software-defined infrastructure company that enables more efficient use of high-cost, high-power resources like GPUs in data centers. Instead of installing GPUs directly into each server, Liquid creates centralized GPU pools and dynamically allocates them to servers based on workload demands. This composable approach maximizes GPU utilization, reducing waste, and improving performance.

Sumit Puri: We saw this vision of GPUs being important in the data center many years back, and then all of a sudden this thing called ChatGPT burst upon the scene and made this front and center in everyone's mind. And so we've been focusing on pooling and sharing these resources for a long time, and now all of a sudden AI is forcing itself into the mainstream, especially in the areas that we focus on, things like the enterprise, and now all of it's coming kind of market for us in a very, very good way. And so we ended up building the product, and the market ended up coming our way.

[Related: Nutanix CEO Stokes Surge in IT Ecosystem Partnerships]

Jason Lopez: Liquid addresses the growing demand for AI inference workloads, especially for enterprises that want to keep their data on-prem instead of moving to the cloud.

Sumit Puri: What Liquid focuses on is very much on building power-efficient, cost-efficient solutions for inference. And it's interesting to think whether it's on-prem or it's in the cloud, a lot of the data, which is what a lot of this AI is driven on, it lives on-prem. Eighty-three percent of the data is actually on-prem. And so one of two things must happen. We either must move the data into the cloud, or we must bring the GPUs on-prem. We think there's a lot of customers who are not willing to wholesale move their data to public cloud, and so therefore we want to build efficient solutions to allow them to process their data on-prem.

[Related: Swarms of AI Agents Powering Businesses and Daily Life]

Jason Lopez: There are advantages of pooling and sharing GPUs instead of deploying them directly in each server.

Sumit Puri: There's three primary benefits why somebody takes this journey of pooling and sharing the GPUs. One is around performance. I need that server to have a lot of GPUs because I need it to run very fast. We're not limited by two or four or eight in a box. We can compose 30 GPUs to a server and give you the fastest servers on the planet. That's one reason. The other is cost. If I have to deploy 30 GPUs, do I want to buy four servers, put eight GPUs in every server, buy a bunch of networking? Or do I want to buy a single server, deploy 30 GPUs, reduce my cost, reduce my power, and have a more efficient way of deploying these resources? And the third reason is agility. It's very hard to predict, will my workload need one? Will it need two? Will it need four? Will it need eight? Do I use an H100? Do I use an A100? Do I use an L40S? There's too many choices, and so we try to take the guesswork out of it. Let's put a centralized pool of whatever device type we need and pick the right tool for the right job at the right time.

[Related: Search for VMware Alternatives That Meet Existing and Future Needs]

Jason Lopez: The AI infrastructure landscape is evolving from training to inference, especially for companies that are not building foundational models.

Sumit Puri: The first chapter of this entire AI journey was very much focused on training and building these foundational models. And the way that we see that going forward, there's probably only going to be 10 companies on the planet that can afford to build these massively large 100,000, 200,000 GPU clusters to build the foundational model. The other 100,000 customers that are out there, they're going to take these models, open-source models like Llama, they're going to bring them on-prem, they're going to fine-tune that model, they're going to do RAG, they're going to do inference, and that part of the journey now is just starting. We think by the end of the decade, inference actually is going to be a larger piece of the overall AI pie than is something like the training portion of it.

Jason Lopez: Liquid makes AI inference more accessible and efficient for enterprises, especially with Kubernetes and model deployments.

Sumit Puri: So NVIDIA has a big push for something called NIMS, NVIDIA Inference Microservices, which is basically a container. And what they've said, it's very, very difficult for people to get all the layers of the stack perfectly right to deploy these models, and so we're going to containerize these models, and that is the way that enterprises are going to go off and deploy this. We have a plugin for our solution where what we do is we suck the container in, we probe the container, we figure out what type of GPU and the quantity of GPU in the backend, and we connect that GPU resource to the specific server in the Kubernetes cluster, then we deploy that container on that machine, so you have this perfect matching of hardware to container, and we automated the entire process. We're at a point now where we have one-click deployment of inference. You say, give me Llama7b go, and we will automate the entire process on the backend, and within two minutes, give you a model that you can speak with. When you're done with it, you hit the delete button, we'll rip those GPUs off, put them back into a centralized pool so that next container, that next model that you're looking to deploy has resources to use. We think that's how you get there, is you have to make it easy for enterprises to deploy these things. They don't have the large armies of data scientists to do this on their own, so the more that we can automate this, the easier it is for those companies to consume, the more that we can make it more efficient, and the way that we think about efficiency is tokens per dollar and tokens per watt, because that's what the enterprises are limited by, they're limited by power and money. If you're a hyperscaler and you have unlimited power, unlimited money, we can't help you. But if you're an enterprise, that is the metric you need to think about. Tokens per dollar, tokens per watt, automation, ease of use, those are the things that are needed to get AI to scale.

[Related: Bracing Data Centers for Wave of AI Workloads]

Jason Lopez: The company supports virtualization and dynamic GPU allocation in enterprise environments.

Sumit Puri: We are a platform for a variety of different applications. One of the applications we are very well suited for is virtualization. If we think about VMs for a second, it's very difficult for enterprises to predict which VM they're going to deploy at what time and what resources that VM might need. We deploy infrastructure for three to five years and making that long-term prediction is very difficult. We've partnered with Nutanix where we can put a centralized pool of GPUs inside of a Nutanix cluster and depending on the requirements of a specific VM, we can match the GPUs on the fly dynamically, hot-plugging GPUs into servers to meet the requirements of the VMs on Nutanix.

Intel IT is modernizing our enterprise architecture (EA) to accelerate Intel’s business transformation. Our goal is to simplify processes, more tightly manage IT assets, and drive EA innovation through AI. Initiatives include establishing federated governance, formalizing evaluation of emerging technologies, increasing EA resiliency, infusing various forms of AI into our EA workflows, and using capability-based planning to prioritize and optimize capability evolution. Our new approach to EA will reduce technical debt, improve business agility, and support Intel’s strategic growth imperative.

As cannabis use in the United States surges, the science behind it struggles to keep pace. In this episode, Dr. Ziva Cooper, Director of the UCLA Center for Cannabis and Cannabinoids, breaks down the spike in usage for both medicinal and recreational purposes, how products and consumption methods continue to evolve and the health conditions for which cannabis is FDA-approved.

Tune in to learn more about the urgency for evidence-based guidance on the therapeutic effects, dosing and safety requirements for clinicians, employers and consumers alike.

Guest: Ziva Cooper, Director of the UCLA Center for Cannabis and Cannabinoids

A relentless drive for innovation, a diligent work ethic and a uniquely American focus on individualism have shaped domestic food culture — one that prioritizes convenience, customization and “snackification” over tradition and shared meals.

In this episode, Sophie Egan, author and leader at the intersection of food, health and climate, explores how these distinctly American values established a food culture rife with marketing and misinformation that creates challenges for maintaining healthy habits. Tune in for actionable insights on how employers can leverage their influence to build healthier, more sustainable food environments for today’s workforce.

Guest: Sophie Egan, Author of How to Be a Conscious Eater and Devoured: How What We Eat Defines Who We Are

Intel is entering a new era, with Intel Product and Intel Foundry as separate business entities. Through activities such as accelerated adoption of AI capabilities, data transformation, and ongoing modernization of factory and data center alike, Intel IT plays a pivotal role as a resilient change agent. We are aligning IT investments and initiatives with Intel’s strategic business objectives and are building resilience into our IT systems, processes, and organizational culture to support enterprise-wide resilience.

This year’s Intel IT Annual Performance Report chronicles Intel IT’s recent efforts and achievements within four categories: enabling the business to thrive, driving operational excellence, delivering an amazing employee experience, and shaping a resilient workforce.

The report includes an opening letter from our CIO, Motti Finklestein, and numerous success stories. Here is just a sampling:

We have developed an enterprise-grade, scalable GenAI platform and a dashboard that enables us to track the lifecycle and business value of all GenAI projects across Intel. 30,000 monthly users have already created more than 7,000 personal assistants, and our 400+ GenAI use cases have generated an estimated USD 376 million in business value.

We are nearing completion of modernizing our enterprise resource planning (ERP) platform. So far, we have over 56 million master and transactional data records loaded, and during our first and second releases, we achieved 100% and 90% data load success, respectively. We are now poised to start scaling our data strategy across the enterprise.

We are transforming our data centers by using sustainable technology like advanced cooling techniques and fuel cell technology; deploying software-defined networking; and evolving our enterprise architecture (EA) processes and practices. The EA effort alone has eliminated more than 300 in-house applications, removed 1,400 commercial software products from the environment, and generated USD 13 million in cost avoidance.

Factory modernization initiatives include implementing a 5G private network and extending our industrial Internet of Things (IIoT). We have also deployed over a dozen robots equipped with multiple integrated IoT sensors; these robots can autonomously roam Intel facilities to perform facility inspections and read values from instrumentation.

We are transforming the employee experience through cloud-based client device management, improving the Wi-Fi experience using client analytics, and benchmarking the PC experience to pinpoint applications that negatively affect battery life. We have cut the number of “How do I…” support tickets by 30% by deploying a GenAI-based chatbot.

We are creating a strong workforce that can keep innovating despite disruption and rapid change. Our new People Council is staffed by leaders who focus on developing and retaining top talent, talent trends, and strategic workforce planning. Our employee training programs center on five top skills for the future: AI, SAP, cloud, business relationship management, and data.

Read the entire APR for the details of these stories and more.

Upon receiving a cancer diagnosis, many patients quickly ask about their ability to continue working through treatment. Work can be vital for the healing process as it offers both a sense of control and connection, and perhaps more importantly, continued alignment with one’s purpose and identity.

In this episode, Gina Jacobson, Program Director, Publicis Groupe, shares her personal story with cancer and how her experience helped inspire the Working with Cancer pledge. Offering guidance and best practices to support employees impacted by cancer, the pledge helps employers design recovery-forward workplaces.

Guest: Gina Jacobson, Program Director, Publicis Groupe

In this Tech Barometer podcast, disruptive technology investor and analyst Jeremiah Owyang explains the rise of AI agents and a future shaped by a multiplying AI-first mindset.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Jeremiah Owyang: The biggest industry right now in AI is the AI agent space. It's destined to grow 10 times every five years. Right now it's estimated at 5 billion, which will go to over 65 billion, and then it’s estimated to be 500 billion just within the next several years. This is the hot market growing within the AI space right now. People use the internet. You physically go out and find different websites to get information. You fill out tasks and you order flights, you order e-commerce, you get things done even inside of your enterprise. You have to fill out expense reports and timecards. All of these things are wasteful and not great experiences in the future. Your AI agents are going to go out and do those tasks for you. The information will be reassembled in the way that you want when you want in a multimodal way, whether it's text, video, ar, vr, whatever time of day that you want, and in the amount of information that you want. It's no longer up to the web designer. It's no longer up to the website or the app designer. It's up to you.

[Related: 4 Trends Defining the Future of Enterprise AI]

Jason Lopez: What you heard are excerpts from a keynote speech by Jeremiah All Yang, general partner at Blitzscaling Ventures. He also leads an event called the LAMA Lounge and AI community of hundreds of AI company founders. We recently interviewed Jeremiah to tell us more about agents.

Jeremiah Owyang: AI agents are dependent upon large language models.

Jason Lopez: In just a moment, Jeremiah explains the importance of LLMs to agents. But let's start here. An agent is software code that allows a device to understand its environment process information then acts autonomously to take actions to achieve specific goals. AI agents can do more than common AI assistance that respond to specific user prompts. An AI agent can self task, it can learn over time and even recruit other agents to help. They operate across multiple apps, often working silently in the background even while we sleep. A smart home thermostat or robot vacuum or autonomous vehicle, or just a few examples of AI agents in action. In many ways, AI agents are less like tools and more like digital organisms adapting and evolving in a world built from code.

Jeremiah Owyang: As they become more advanced, they operate in sequence, in combination with each other. In concert, which is called the agent crews or fleets or groups or swarms. In many cases, they are like low level human employees.

[Related: Managing Enterprise AI Sprawl]

Jason Lopez: So how does an agent know what to do?

Jeremiah Owyang: They get it from large language models. They're dependent, so it's not if they're a replacement of large language models, they're actually executing the tasks. So think of large language models as your brain with all that knowledge and the things to do, but the actual clicking and typing and doing the actual physical tasks like your limbs, your appendages, your body are the AI agents, and you need both in combination to be an effective quote creature.

Jason Lopez: From the point of view of an enterprise, you might be wondering if IT infrastructure needs to change as companies shift from just using LLMs to deploying AI agents. The answer would be yes. An IT team needs to prepare for how agents will interact with the company's systems. This means choosing the right tools or partners and deciding on what permissions and access an agent should have for the tasks it could do.

Jeremiah Owyang: It could fill out your expense report after looking at your credit card. It could create meetings or meeting summaries. It could build your monthly reporting deck and grab data from disparate places and aggregate it into one location. It could be like a virtual colleague. It could be just the two of us on a call. We could have three, four, or five assistants that are not just recording, but actually talking, collaborating, taking notes, taking actions, and generating diagrams in real time.

[Related: Enterprise AI Reality Check: Implementing Practical Solutions]

Jason Lopez: Many companies are already using AI assistance for customer service, handling basic questions using company data, and Jeremiah says, companies are using Gen AI in all sorts of interesting ways. He said 10% of Pfizer's marketing content is being generated by ai. This can help workers be faster and more productive.

Jeremiah Owyang: Now, imagine if that worker who just wants to focus on making good decisions or the relationships with customers or relationships with internal stakeholders could allow the AI agent to do all that busy work for them. Now at your company and many companies who has an executive assistant, typically it's only provided to those at the top of the hierarchical pyramid. Now, imagine a world where every worker has one assistant wait, two assistants, wait, wait, wait, 10 assistants, 10 assistants that help with data or meetings or scheduling. Just all of those things. Imagine the level of productivity that can increase for those knowledge workers, and I think we're on the cusp of that because if you interface with just an AI agent, it means you just don't need that many enterprise apps anymore.

[Related: A New Generation of Data Centers Spreads Use of Enterprise AI]

Jason Lopez: This isn't to say that an AI agent is just a simple addition to a team. Some companies are exploring what it would be like to rely on AI led teams.

Jeremiah Owyang: So it's really critical that you as a worker lean into AI and lead that within your company and for your career, or you might have to be updating your LinkedIn profile. So I think it's critical that we lean into this technology.

Jason Lopez: We've reported before as we head into the age of ai, there's a skills gap. Companies like Nutanix have AI teams that can build AI into products or create apps that boost business productivity, but using AI as a skill that will be needed across different business units to enhance employee competency. Many companies onboard AI capabilities like Microsoft Copilot or Glean or services like writer.com. Many workers take upon themselves to acquire knowledge and certifications for AI expertise. They turn to YouTube or platforms like Coursera or continuing ed from colleges and universities.

Jeremiah Owyang: Every worker is responsible for their continued growth. Now, for executives, there's a new role. There's a chief AI officer leading the charge within the organization to use these tools. Most of the innovation is happening with the young startups, many of them who spun out a big tech or spun out of big companies and they say, I'm going to move much faster.

[Related: Business Steps for Using Agentic AI]

Jason Lopez: Jeremiah says, small teams are capable of building a global enterprise AI software suite in one year with rapid adoption.

Jeremiah Owyang: Crew AI only has 16 employees, and they're already one of the dominant enterprise agent leaders because they're using AI for everything.

Jason Lopez: Crew AI is a San Francisco based firm, which makes development tools to build AI agents that do tasks in apps like CRM or ERP systems.

Jeremiah Owyang: They are what is called an AI-first mentality. So let's talk about an AI first mentality. This is a common mental framework within Silicon Valley, amongst the AI leaders. If you have a problem in your life, you first see if there's an AI that exists off the shelf, whether it's an app you download or an enterprise app that's an existing enterprise software. If it doesn't exist, then you try to build it and step three, if it doesn't, you can't build it. Then you hire somebody to build it. So you follow that lineage to think about, how do I move fast? And it's always about leveraging AI first. So that's the AI-first mindset.

[Related: AI and Cloud Native Skills Reshape Careers]

Jason Lopez: Building on the rise of enterprise AI platforms and the growing availability of ready-made templates. The next logical step is even more transformative. As tools become more accessible in development, more streamlined, the creation of AI agents is starting to shift as well, possibly even to the agents themselves.

Jeremiah Owyang: We are probably going to see AI agents create AI agents. There's already no-code AI, so developers can be even more efficient and can create even faster. I anticipate that developers and software engineers, they're still needed. We need them more so they can just produce more. I don't see them going out of a job. That's our take in this

Jason Lopez: Space. Still, the development of AI agents in the enterprise lags behind the customer side. A lot of that has to do with security.

Jeremiah Owyang: One rogue AI system within an enterprise, potentially from a nation that is not friendly with your nation, could upend your company. It could grab data and send it back to the home country. So I think there are appropriate concerns around ensuring that the AI is safe for the enterprise and the organization eventually, which would help with the customer relationships. So I think that's why we see that lag.

[Related: CIOs Sharpen Skills for the AI Era]

Jason Lopez: Jeremiah points out that while AI agents can be like coworkers, they're not human, but powerful tools, tools that aren't neutral because they mirror the intentions of people.

Jeremiah Owyang: The old adage in Silicon Valley that you and I know quite well is that if the product is free, then you are the product. If you're paying a premium, whether it's enterprise or consumer for the AI agent, then it is more likely to be aligned for your benefit. If not, then it is more likely to be towards the benefit of the builder. And I think the old business models in Silicon Valley apply here. They're tools, and we have used Fire for cooking, which helped us to expand our brains with more nutrients by having clean food. We've used steel to build amazing vehicles and buildings, but we've also used fire and steel for weapons, and that choice is very much a human thing where we choose how to use these tools and technologies. The difference here with AI though is that it is a thinking machine and it's trained off what humans will do, and it starts to think or simulate thinking on its own, and that's something we haven't seen before in every possible way you look at this, it's an embodiment of the human condition, and that is a wild thing to think about, that we're creating a new species.

Jason Lopez: Jeremiah Al Yang is the founder of Lama Lounge AI events, and he's a partner at Blitzscaling Ventures. You can find out more about his events at lu.ma/lama lounge. This is the Tech Barometer podcast. I'm Jason Lopez. Thanks for listening. Tech Barometer is a production of the forecast where you can find more stories about artificial intelligence as well as cloud and enterprise computing. Look for those at the forecast by nutanix.com, all one word, theforecastbynutanix.com.