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.Box CEO Aaron Levie on why AI agents can't keep a secret. "The reason why you probably won't see a complete compression of software where it's just a an agent and a database is because there's a lot of logic in the workflow and in the the company's specific... show more

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311,115 views • 1 year ago •via X (Twitter)

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Hot topic in vogue is what happens to systems of record in an era of 1000x more agents than humans. Aaron Levie has been the CEO of Box for 20 years and believes that systems of record become more important, not less, with agents: “Systems of record were built for a TAM of human employees. And that was sort of where the value maxed out at. And now maybe we'll have a hundred times more agents than people or a thousand times more agents than people. So I think on a relative basis, the agent layer will seize growth from zero to a trillion. But the system of record layer now has a thousand times more users on it that need to access data and need to execute a workflow and need to move things through various events. And importantly, you need as an enterprise to care about data integrity and security and the controls in your workflow and traffic copying the agents. So basically, this is where I am very diametrically opposed to the view that the system of record gets is squeezed into a database interaction layer because you actually need all of the core system of record properties for the agents to operate on.”

Hot topic in vogue is what happens to systems of record in an era of 1000x more agents than humans. Aaron Levie has been the CEO of Box for 20 years and believes that systems of record become more important, not less, with agents: “Systems of record were built for a TAM of human employees. And that was sort of where the value maxed out at. And now maybe we'll have a hundred times more agents than people or a thousand times more agents than people. So I think on a relative basis, the agent layer will seize growth from zero to a trillion. But the system of record layer now has a thousand times more users on it that need to access data and need to execute a workflow and need to move things through various events. And importantly, you need as an enterprise to care about data integrity and security and the controls in your workflow and traffic copying the agents. So basically, this is where I am very diametrically opposed to the view that the system of record gets is squeezed into a database interaction layer because you actually need all of the core system of record properties for the agents to operate on.”

Corinne Marie Riley

119,410 views • 8 months ago

"Agents really love files. They might need to tap into files you've curated or want to share with them — but you probably want a partitioned file system that only the agent has access to and can't touch other things." Aaron Levie spoke at Daytona's Compute Conference with Mark Suster on why agents need file systems and why getting that foundation right is one of the most important infrastructure decisions enterprises will make.

"Agents really love files. They might need to tap into files you've curated or want to share with them — but you probably want a partitioned file system that only the agent has access to and can't touch other things." Aaron Levie spoke at Daytona's Compute Conference with Mark Suster on why agents need file systems and why getting that foundation right is one of the most important infrastructure decisions enterprises will make.

Box

3,241,629 views • 4 months ago

Aaron Levie on why AI agents can't just be treated like normal user accounts: "I, as Aaron, don't really have any responsibility over anybody else's Box account in our organization. I can't see the Box account of any other employee that I work with. I'm not liable for anything that they do." "Agents don't have those properties. The person who creates the agent probably is going to, for the foreseeable future, take on a lot of the liability for what that agent does." "When you're in Claude Code or Cursor, the agent is you... It can do everything you can do." "That's the easy mode." "The hard mode is agents are running on their own and people check in with them occasionally." "How do you give them access to resources in the enterprise without dramatically increasing the security risk?" Aaron Levie on Latent.Space

Aaron Levie on why AI agents can't just be treated like normal user accounts: "I, as Aaron, don't really have any responsibility over anybody else's Box account in our organization. I can't see the Box account of any other employee that I work with. I'm not liable for anything that they do." "Agents don't have those properties. The person who creates the agent probably is going to, for the foreseeable future, take on a lot of the liability for what that agent does." "When you're in Claude Code or Cursor, the agent is you... It can do everything you can do." "That's the easy mode." "The hard mode is agents are running on their own and people check in with them occasionally." "How do you give them access to resources in the enterprise without dramatically increasing the security risk?" Aaron Levie on Latent.Space

a16z

40,579 views • 5 months ago

Google’s new Agent2Agent protocol is a key step toward a future of AI Agent interoperability. No single tool has all the data a user or business needs for most workflows, so we need Agents to be able to talk to each other. Salesforce will have AI Agents that understand the inner workings of CRM, Workday will have AI Agents that understand HR workflows, Box has AI Agents that understand content and documents, and so on. It’s easy to conceive of a world where we have tens of thousands of these tool Agents and then billions or trillions of customized Agents that are extensions of those. So openness becomes key. Workflows need data from multiple systems to complete the task — like a sales report that needs documents and CRM data or an HR task that needs HR policies and employee details — and that’s where A2A comes in. This protocol gives AI Agent providers a way of talking to each other and simplifying the way that Agents communicate, understand each other’s capabilities, and so on. It’s great to see more interoperability, along with MCP, emerging in the AI space right now, as this will define the future of most IT stacks.

Google’s new Agent2Agent protocol is a key step toward a future of AI Agent interoperability. No single tool has all the data a user or business needs for most workflows, so we need Agents to be able to talk to each other. Salesforce will have AI Agents that understand the inner workings of CRM, Workday will have AI Agents that understand HR workflows, Box has AI Agents that understand content and documents, and so on. It’s easy to conceive of a world where we have tens of thousands of these tool Agents and then billions or trillions of customized Agents that are extensions of those. So openness becomes key. Workflows need data from multiple systems to complete the task — like a sales report that needs documents and CRM data or an HR task that needs HR policies and employee details — and that’s where A2A comes in. This protocol gives AI Agent providers a way of talking to each other and simplifying the way that Agents communicate, understand each other’s capabilities, and so on. It’s great to see more interoperability, along with MCP, emerging in the AI space right now, as this will define the future of most IT stacks.

Aaron Levie

139,591 views • 1 year ago

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.

Postman is going all-in on AI! Their AI Agent Builder is live (and it's pretty powerful!) Postman's API Network has 100,000+ APIs and they just released a suite of tools for building AI Agents that can automatically discover and connect to any of these APIs. Think about it for a second: You can now build an AI agent that searches the network, discovers an API, processes its documentation, connects to it, and evaluates whether everything worked at the end of the process. This is a huge deal. Every person I know uses the Postman client to work with APIs. Now, they will have access to the AI Agent Builder, a complete suite for designing, building, testing, and deploying agents. I've built a few simple agents myself, and it's tough. The ability to discover and use an API automatically is really exciting (and a huge time saver if it works as promised!) In collaboration with the Postman team, who is sponsoring this post, I recorded a video with my thoughts about the Postman AI Agent Builder.

Santiago

230,647 views • 1 year ago

Aaron Levie is one of the greats. He’s been the co-founder and CEO of Box for 20 years, of which public CEO for 10. And he shows no signs of slowing down, as one of the most active CEOs thinking about how AI agents will shape enterprise software. In our Greylock Partners Change Agents, Aaron and I discussed the question: happens when SaaS is no longer built for humans, but for a much larger scale of agents and humans orchestrating those agents? A few of my favorite topics from the episode: - Why 90%+ of enterprise data has gone unused to date and how agents finally make it accessible - Why SaaS categories won’t disappear, but will be rebuilt around agent orchestration layers - Why the system of record doesn't lose importance, but gains importance the enable agents - How abundance (not labor replacement) is the real shift agents bring to knowledge work - And the real moat in the agent era may be neither the model nor the interface but the workflow memory the agent accumulates over time Aaron has one of the clearest, most grounded takes on where agents for the enterprise can go and what happens to traditional enterprise SaaS. Full episode below:

Aaron Levie is one of the greats. He’s been the co-founder and CEO of Box for 20 years, of which public CEO for 10. And he shows no signs of slowing down, as one of the most active CEOs thinking about how AI agents will shape enterprise software. In our Greylock Partners Change Agents, Aaron and I discussed the question: happens when SaaS is no longer built for humans, but for a much larger scale of agents and humans orchestrating those agents? A few of my favorite topics from the episode: - Why 90%+ of enterprise data has gone unused to date and how agents finally make it accessible - Why SaaS categories won’t disappear, but will be rebuilt around agent orchestration layers - Why the system of record doesn't lose importance, but gains importance the enable agents - How abundance (not labor replacement) is the real shift agents bring to knowledge work - And the real moat in the agent era may be neither the model nor the interface but the workflow memory the agent accumulates over time Aaron has one of the clearest, most grounded takes on where agents for the enterprise can go and what happens to traditional enterprise SaaS. Full episode below:

Corinne Marie Riley

43,790 views • 8 months ago

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

Akshay 🚀

122,548 views • 2 months ago

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much *anything* because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

This agent relies on a marketplace of 2.8M+ specialized agents to complete a task. This is how I imagine the future of personal agents. Here is how this works: 1. Agentverse is a marketplace of agents 2. Anyone can build and publish an agent there 3. ASI:One is a personal agent with access to the marketplace 4. To solve a task, ASI:One finds the best agent for the job In other words: You have an agent that can solve pretty much anything because it knows how to outsource the work to a network of specialized agents. And you don't need to do any setup to get this working. The way ranking agents in the marketplace works is pretty cool: Fetch.ai, the company behind it, created an AgentRank algorithm that works much like Google's PageRank. Every time an agent calls another, it forms a connection. Over time, these connections build into a graph of who relies on whom. An agent that is frequently called by other reputable agents scores higher and surfaces higher in results. The end result here is that your agent will consistently use the best agents in the marketplace to accomplish a goal. You should give this a try: You don't need to pay or even register to try these agents. Thanks to the team for partnering with me on this post.

Santiago

18,715 views • 2 months ago

Agents shouldn't see your secrets. Today, most deployments hand the agent a real API key and hope for the best. That's why we're launching Infisical Agent Proxy. You give an agent a fake API key. The agent uses that key and the proxy swaps it for the real one at the network boundary before forwarding it outbound to its destination. An agent can't leak a secret it never sees. Build and deploy AI agents securely with Infisical Agent Proxy.

Agents shouldn't see your secrets. Today, most deployments hand the agent a real API key and hope for the best. That's why we're launching Infisical Agent Proxy. You give an agent a fake API key. The agent uses that key and the proxy swaps it for the real one at the network boundary before forwarding it outbound to its destination. An agent can't leak a secret it never sees. Build and deploy AI agents securely with Infisical Agent Proxy.

Infisical

28,086 views • 1 month ago

Agents will eventually outnumber humans by orders of magnitude. Aaron Levie on the infrastructure that will be needed to manage them: "Whether you think the number is 10x or 100x... we're going to have some order of magnitude more agents than people." "There's going to be just incredibly spectacularly crazy security incidents that will happen with agents, because you'll prompt-inject an agent and find your way through the CRM system and pull out data you shouldn't have access to." "How do you make sure you have the right security, the permissions, the access controls, the data governance?" "We actually don't yet exactly know in many cases how we're going to regulate some of these agents." "No matter what, there's going to need to be a layer that manages the data they have access to and the workflows they're involved in." "This is the new infrastructure opportunity in the era of agents." Aaron Levie on Latent.Space

Agents will eventually outnumber humans by orders of magnitude. Aaron Levie on the infrastructure that will be needed to manage them: "Whether you think the number is 10x or 100x... we're going to have some order of magnitude more agents than people." "There's going to be just incredibly spectacularly crazy security incidents that will happen with agents, because you'll prompt-inject an agent and find your way through the CRM system and pull out data you shouldn't have access to." "How do you make sure you have the right security, the permissions, the access controls, the data governance?" "We actually don't yet exactly know in many cases how we're going to regulate some of these agents." "No matter what, there's going to need to be a layer that manages the data they have access to and the workflows they're involved in." "This is the new infrastructure opportunity in the era of agents." Aaron Levie on Latent.Space

a16z

64,423 views • 5 months ago

"The data that we've been working with as people now becomes extremely useful in a world of agents. You're having this rebirth of the file system." Aaron Levie on Navigators with Paul Klein IV on why unstructured data, the 90% of enterprise data that has always been the hardest to automate, is now the biggest unlock for AI agents. And why the real AI conversation is a context conversation.

"The data that we've been working with as people now becomes extremely useful in a world of agents. You're having this rebirth of the file system." Aaron Levie on Navigators with Paul Klein IV on why unstructured data, the 90% of enterprise data that has always been the hardest to automate, is now the biggest unlock for AI agents. And why the real AI conversation is a context conversation.

Box

66,225 views • 1 month ago

Elon Musk explains the deeper reason why SpaceX succeeding in AI matters for humanity’s future "It's important that we have an AI that cares about humanity, that fosters humanity and helps take us to other planets and other star systems This is why I think it's essential that SpaceX succeed not just with AI hardware, but also with AI software. I think an AI that is SpaceX's baby will be a very good AI SpaceX is a collection some of the very best humans on Earth, both in capability and in morality and goodness. We want to have an AI that is born of that capability, that morality and that goodness. I think that's very, very important So I'd like to encourage people to help out with the AI hardware on the ground, the AI software, and of course with the AI satellites Solar is going to be a very important part of that. The Terafab will be a very important part of that”

Elon Musk explains the deeper reason why SpaceX succeeding in AI matters for humanity’s future "It's important that we have an AI that cares about humanity, that fosters humanity and helps take us to other planets and other star systems This is why I think it's essential that SpaceX succeed not just with AI hardware, but also with AI software. I think an AI that is SpaceX's baby will be a very good AI SpaceX is a collection some of the very best humans on Earth, both in capability and in morality and goodness. We want to have an AI that is born of that capability, that morality and that goodness. I think that's very, very important So I'd like to encourage people to help out with the AI hardware on the ground, the AI software, and of course with the AI satellites Solar is going to be a very important part of that. The Terafab will be a very important part of that”

X Freeze

29,610 views • 22 days ago

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

New Course: ACP: Agent Communication Protocol Learn to build agents that communicate and collaborate across different frameworks using ACP in this short course built with IBM Research's BeeAI, and taught by Sandi Besen, AI Research Engineer & Ecosystem Lead at IBM, and Nicholas Renotte, Head of AI Developer Advocacy at IBM. Building a multi-agent system with agents built or used by different teams and organizations can become challenging. You may need to write custom integrations each time a team updates their agent design or changes their choice of agentic orchestration framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing how agents communicate, using a unified RESTful interface that works across frameworks. In this protocol, you host an agent inside an ACP server, which handles requests from an ACP client and passes them to the appropriate agent. Using a standardized client-server interface allows multiple teams to reuse agents across projects. It also makes it easier to switch between frameworks, replace an agent with a new version, or update a multi-agent system without refactoring the entire system. In this course, you’ll learn to connect agents through ACP. You’ll understand the lifecycle of an ACP Agent and how it compares to other protocols, such as MCP (Model Context Protocol) and A2A (Agent-to-Agent). You’ll build ACP-compliant agents and implement both sequential and hierarchical workflows of multiple agents collaborating using ACP. Through hands-on exercises, you’ll build: - A RAG agent with CrewAI and wrap it inside an ACP server. - An ACP Client to make calls to the ACP server you created. - A sequential workflow that chains an ACP server, created with Smolagents, to the RAG agent. - A hierarchical workflow using a router agent that transforms user queries into tasks, delegated to agents available through ACP servers. - An agent that uses MCP to access tools and ACP to communicate with other agents. You’ll finish up by importing your ACP agents into the BeeAI platform, an open-source registry for discovering and sharing agents. ACP enables collaboration between agents across teams and organizations. By the end of this course, you’ll be able to build ACP agents and workflows that communicate and collaborate regardless of framework. Please sign up here:

Andrew Ng

105,343 views • 1 year ago

The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.

The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.

The Startup Ideas Podcast (SIP) 🧃

13,598 views • 2 months ago

Is the rise of FDEs a short-term phenomenon or a continuous feature of the AI ecosystem? “I have a bit of a hot take: I think it's the future for the short term. Right now there's a concentration of people in San Francisco who really know how to deploy agents, eval agents, and be AI first, and that knowledge just isn't out there yet. Eventually it will be. At that point you won't need teams to set up AI agents for every enterprise, and it'll become more of a job function, similar to software engineering.” Do you agree? Do you agree with the dissemination of knowledge FDEs will be needed less Matan Grinberg Nikesh Arora Shyam Sankar

Is the rise of FDEs a short-term phenomenon or a continuous feature of the AI ecosystem? “I have a bit of a hot take: I think it's the future for the short term. Right now there's a concentration of people in San Francisco who really know how to deploy agents, eval agents, and be AI first, and that knowledge just isn't out there yet. Eventually it will be. At that point you won't need teams to set up AI agents for every enterprise, and it'll become more of a job function, similar to software engineering.” Do you agree? Do you agree with the dissemination of knowledge FDEs will be needed less Matan Grinberg Nikesh Arora Shyam Sankar

Harry Stebbings

19,473 views • 1 month ago

A one-man trading hedge fund now costs about $100 a month in AI subscriptions. Five PhDs on a Citadel desk cost $270,000 a month, and that is the cheap end. That buys one tested idea a month. Man Group's AI writes hundreds of new signals a week, on a floor where the humans used to manage twenty in a quarter. Fifty AI agents do the reading and the writing. One of them exists only to destroy what the others build, and that one is the reason any of it works. Every job on that payroll is now an agent you can run yourself: > AI research agents reading filings, transcripts, options flow and on-chain data overnight, fifty personas at once, none of them allowed to see what the crowd is pricing > coding agent that turns one sentence of plain English into entry rules, exit rules, sizing and risk limits, then debugs itself until it runs > backtest agent that replays five years of that idea in about 12 seconds > breaker agent, an AI whose only job is killing the thing, at double the trading costs and in the ten worst markets that asset has ever seen > critic agent that reads your journal and names the mistake you keep making Nobody on that list asks for a bonus in January. $200 million is already run this way on one platform where 52,000 people mostly sit and watch the machine work. The full map of what is built and what is missing is in the article below. Bookmark & read it If you're not yet using AI in your trading and investing.

A one-man trading hedge fund now costs about $100 a month in AI subscriptions. Five PhDs on a Citadel desk cost $270,000 a month, and that is the cheap end. That buys one tested idea a month. Man Group's AI writes hundreds of new signals a week, on a floor where the humans used to manage twenty in a quarter. Fifty AI agents do the reading and the writing. One of them exists only to destroy what the others build, and that one is the reason any of it works. Every job on that payroll is now an agent you can run yourself: > AI research agents reading filings, transcripts, options flow and on-chain data overnight, fifty personas at once, none of them allowed to see what the crowd is pricing > coding agent that turns one sentence of plain English into entry rules, exit rules, sizing and risk limits, then debugs itself until it runs > backtest agent that replays five years of that idea in about 12 seconds > breaker agent, an AI whose only job is killing the thing, at double the trading costs and in the ten worst markets that asset has ever seen > critic agent that reads your journal and names the mistake you keep making Nobody on that list asks for a bonus in January. $200 million is already run this way on one platform where 52,000 people mostly sit and watch the machine work. The full map of what is built and what is missing is in the article below. Bookmark & read it If you're not yet using AI in your trading and investing.

cvxv666

172,836 views • 21 days ago

Sat down with Aaron Levie, CEO of Box, to discuss where AI agents are going, how agentic workflows are transforming software, why agents will require much more memory, and how token budgets for enterprises are evolving. Thank you Aaron Levie for taking the time! 00:00 - Intro 01:30 - Where are we in AI 06:25 - The Role of Agents in Software 11:40 - The Role of Memory for Agents 14:45 - TokenMaxxing 24:54 - Why Are There AI Doomers? 31:50 - Aaron's Take on Nvidia

Sat down with Aaron Levie, CEO of Box, to discuss where AI agents are going, how agentic workflows are transforming software, why agents will require much more memory, and how token budgets for enterprises are evolving. Thank you Aaron Levie for taking the time! 00:00 - Intro 01:30 - Where are we in AI 06:25 - The Role of Agents in Software 11:40 - The Role of Memory for Agents 14:45 - TokenMaxxing 24:54 - Why Are There AI Doomers? 31:50 - Aaron's Take on Nvidia

amit

96,221 views • 2 months ago

The ceo of Box says if you're not building for agents, you won't exist as a software company in 2-3 years. Aaron Levie recalled how in the 90s, it was mainframes built for institutions. If you didn't build for the CIO or IT org, you didn't exist. You could build software for users, but no one cared. Similarly, in the mid 2000s, it was software built for end users. If you didn't take that route, you got completely trounced. The likes of Yammer, Slack, Zoom, etc., ripped through enterprises with viral adoption. Bottom-up and completely user driven. And now it's software built for agents. We're going to have trillions of agent runs. The systems that optimize for agents the most will win. - lower latency - better API exposure - better system intelligence - serves the right information when called Agents will eventually choose their own tools on the fly and make purchasing decisions at runtime. All based on past experiences/online reviews/performance, sans any procurement process. The more pragmatic version is companies swapping out bad tools / tech that don't work well with agents. - crm doesn't expose the right APIs - auth can't handle agent scale requests - database has too much latency for agent workloads Being good enough for humans won't work because they have to pass the litmus test for agents to make the cut.

The ceo of Box says if you're not building for agents, you won't exist as a software company in 2-3 years. Aaron Levie recalled how in the 90s, it was mainframes built for institutions. If you didn't build for the CIO or IT org, you didn't exist. You could build software for users, but no one cared. Similarly, in the mid 2000s, it was software built for end users. If you didn't take that route, you got completely trounced. The likes of Yammer, Slack, Zoom, etc., ripped through enterprises with viral adoption. Bottom-up and completely user driven. And now it's software built for agents. We're going to have trillions of agent runs. The systems that optimize for agents the most will win. - lower latency - better API exposure - better system intelligence - serves the right information when called Agents will eventually choose their own tools on the fly and make purchasing decisions at runtime. All based on past experiences/online reviews/performance, sans any procurement process. The more pragmatic version is companies swapping out bad tools / tech that don't work well with agents. - crm doesn't expose the right APIs - auth can't handle agent scale requests - database has too much latency for agent workloads Being good enough for humans won't work because they have to pass the litmus test for agents to make the cut.

Ivan Burazin

22,655 views • 4 months ago

Elon Musk on Grok: “I think an AI that is sort of SpaceX’s baby will be a very good AI. I think SpaceX is a collection of some of the very best humans on Earth, both in capability and morality and goodness, and I think we want to have an AI that is born of that capability and that morality and that goodness. I think that’s very, very important. So I’d like to encourage people to help out, actually, with the AI hardware on the ground and the AI software and, of course, with the AI satellites. Solar is going to be a very important part of that. The TerraFab will be a very important part of that, but we must also succeed on the software front, and we’re going to be training Grok on the sum total of all SpaceX information. So in a way, it will be trained on you. You will effectively be the parents of the AI. It will inherit your thoughts and ideas and beliefs, and I think that’s a good thing. So, yeah, we’ve got to win here on the AI hardware and the AI software. That maybe is the most important message. I think that is actually the most important message I wanted to convey today is that we must win on AI because the future is overwhelmingly AI and robots. So we won’t ultimately be able to control the AI. It’ll be too smart for that. But just like if you have a child that is a super genius child, you can still instill in that child the values and beliefs that you think are good and right. And so that’s why it’s incredibly important that we succeed with Grok. So, yeah, well, Grok 4.5 you’ve tried probably. We’ve got 4.6 coming out in about a week. And then 4.7 should be really pretty special. So I’d like to encourage everyone at SpaceX to use AI and to make it better.”

Elon Musk on Grok: “I think an AI that is sort of SpaceX’s baby will be a very good AI. I think SpaceX is a collection of some of the very best humans on Earth, both in capability and morality and goodness, and I think we want to have an AI that is born of that capability and that morality and that goodness. I think that’s very, very important. So I’d like to encourage people to help out, actually, with the AI hardware on the ground and the AI software and, of course, with the AI satellites. Solar is going to be a very important part of that. The TerraFab will be a very important part of that, but we must also succeed on the software front, and we’re going to be training Grok on the sum total of all SpaceX information. So in a way, it will be trained on you. You will effectively be the parents of the AI. It will inherit your thoughts and ideas and beliefs, and I think that’s a good thing. So, yeah, we’ve got to win here on the AI hardware and the AI software. That maybe is the most important message. I think that is actually the most important message I wanted to convey today is that we must win on AI because the future is overwhelmingly AI and robots. So we won’t ultimately be able to control the AI. It’ll be too smart for that. But just like if you have a child that is a super genius child, you can still instill in that child the values and beliefs that you think are good and right. And so that’s why it’s incredibly important that we succeed with Grok. So, yeah, well, Grok 4.5 you’ve tried probably. We’ve got 4.6 coming out in about a week. And then 4.7 should be really pretty special. So I’d like to encourage everyone at SpaceX to use AI and to make it better.”

DogeDesigner

64,019 views • 22 days ago

In the past, it wasn't possible for companies to tap into their unstructured data. But that's now changing with AI. On Greylock Partners Change Agents, Aaron Levie shared "We see a future where you can actually begin to bring agents to all that data and answer any kind of question the customer has, and then eventually be able to automate any kind of workflow that the customer has."

In the past, it wasn't possible for companies to tap into their unstructured data. But that's now changing with AI. On Greylock Partners Change Agents, Aaron Levie shared "We see a future where you can actually begin to bring agents to all that data and answer any kind of question the customer has, and then eventually be able to automate any kind of workflow that the customer has."

Box

315,675 views • 8 months ago