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Today we’re partnering with AWS to launch Superblocks 3.0: the secure way for employees to vibe code production enterprise software. In a single prompt, Superblocks can replace million dollar SaaS, while IT & Security stay in control. OpenAI and Anthropic are releasing new models with advanced cyber attack capability...

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Brad Menezes

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1,882,903 просмотров • 4 дней назад •via X (Twitter)

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Introducing Superblocks 2.0: AI-generated enterprise apps – finally under IT control. Vibe-coded apps just became the #1 attack vector in the enterprise. Business teams are building on production data, while IT has zero visibility. No reviews. No audits. No permissions. No control. AI hackers are about to get 100x better. Anthropic proved it with Mythos. Superblocks 2.0 is the only platform to take back control: > Business teams build AI-powered apps with permissions baked in. > IT and Security can audit everything and lock down anything, instantly. > Engineering sets the standards. Every app follows them. Instacart, SoFi, and LinkedIn run Superblocks in production today. And larger organizations we can't yet name are too: A Fortune 500 just shut down 2,500 Replit users to standardize on Superblocks, running the platform air-gapped in their AWS environment. A 150,000-employee global services firm replaced Lovable with Superblocks to unlock AI-built apps on restricted internal systems. Every IT leader we’ve demoed to using Replit, Lovable or v0 asked for early access. Today we open access to the world. The genie is out of the bottle on employee vibe coding. Let it run wild, or take back control –
2:13

Introducing Superblocks 2.0: AI-generated enterprise apps – finally under IT control. Vibe-coded apps just became the #1 attack vector in the enterprise. Business teams are building on production data, while IT has zero visibility. No reviews. No audits. No permissions. No control. AI hackers are about to get 100x better. Anthropic proved it with Mythos. Superblocks 2.0 is the only platform to take back control: > Business teams build AI-powered apps with permissions baked in. > IT and Security can audit everything and lock down anything, instantly. > Engineering sets the standards. Every app follows them. Instacart, SoFi, and LinkedIn run Superblocks in production today. And larger organizations we can't yet name are too: A Fortune 500 just shut down 2,500 Replit users to standardize on Superblocks, running the platform air-gapped in their AWS environment. A 150,000-employee global services firm replaced Lovable with Superblocks to unlock AI-built apps on restricted internal systems. Every IT leader we’ve demoed to using Replit, Lovable or v0 asked for early access. Today we open access to the world. The genie is out of the bottle on employee vibe coding. Let it run wild, or take back control –

Brad Menezes

4,640,756 просмотров • 3 месяцев назад

Announcing Superblocks on Snowflake: Secure Enterprise Vibe Coding on the AI Data Cloud This couldn't be more timely. Vibe coding is exploding inside the enterprise. It’s also becoming the fastest path to a data breach. Every CEO is trying to accelerate adoption of vibe coding for business users automate, without blocking on engineering. Every VP of Data and CIO is trying to figure out how to lock down their data, harden their permissions, and gain centralized visibility. That’s exactly what this partnership unlocks. Superblocks becomes the enterprise platform for governed vibe coding across business teams, deeply embedded within Snowflake: > Snowflake Postgres becomes your secure vibe coding database. Superblocks spins it up in every app for fast writes and net new operational workflow data >Snowflake Cortex becomes the inference engine for Superblocks. Your prompts and vibe coded AI features run within your trusted security perimeter >Snowflake Warehouse governance is enforced. AI apps use federated token passthrough so your existing row level access policies, dynamic data masking, tag-based-access controls are respected “With Superblocks on Snowflake, any business team can safely build AI applications governed on their Snowflake data. It’s a dream come true.” -Unmesh Jagtap, Director of Product, Snowflake Now enterprises can stop playing whack-a-mole, and move your organization past the AI prototype graveyard. If you’re a Snowflake customer, book a demo (link in comments) Thanks to the Snowflake team for the partnership: Christian Kleinerman, Vivek Raghunathan, Bala Kasiviswanathan, Unmesh Jagtap, and Myles Borins for helping shape how the industry democratizes governed AI app development while centralizing control on the AI Data Cloud.
2:39

Announcing Superblocks on Snowflake: Secure Enterprise Vibe Coding on the AI Data Cloud This couldn't be more timely. Vibe coding is exploding inside the enterprise. It’s also becoming the fastest path to a data breach. Every CEO is trying to accelerate adoption of vibe coding for business users automate, without blocking on engineering. Every VP of Data and CIO is trying to figure out how to lock down their data, harden their permissions, and gain centralized visibility. That’s exactly what this partnership unlocks. Superblocks becomes the enterprise platform for governed vibe coding across business teams, deeply embedded within Snowflake: > Snowflake Postgres becomes your secure vibe coding database. Superblocks spins it up in every app for fast writes and net new operational workflow data >Snowflake Cortex becomes the inference engine for Superblocks. Your prompts and vibe coded AI features run within your trusted security perimeter >Snowflake Warehouse governance is enforced. AI apps use federated token passthrough so your existing row level access policies, dynamic data masking, tag-based-access controls are respected “With Superblocks on Snowflake, any business team can safely build AI applications governed on their Snowflake data. It’s a dream come true.” -Unmesh Jagtap, Director of Product, Snowflake Now enterprises can stop playing whack-a-mole, and move your organization past the AI prototype graveyard. If you’re a Snowflake customer, book a demo (link in comments) Thanks to the Snowflake team for the partnership: Christian Kleinerman, Vivek Raghunathan, Bala Kasiviswanathan, Unmesh Jagtap, and Myles Borins for helping shape how the industry democratizes governed AI app development while centralizing control on the AI Data Cloud.

Brad Menezes

13,098 просмотров • 3 месяцев назад

I’m excited to introduce Clark, the first AI Agent to build internal enterprise apps. We’ve raised $60M, including a fresh $23M from Spark Capital Kleiner Perkins, Meritech Capital and Greenoaks. Unlike consumer vibe coding tools like Lovable, Replit and Bolt that only generate prototypes, Clark builds production-ready internal apps — enforcing your enterprise standards: 🧩 UIs generated using your design system 🔌 Integrations with private APIs, databases and SaaS apps 🔐 Permissions mapped to Okta & Microsoft Entra ID (Azure AD) groups 🛡️ Security with audit logging, secrets management, and vulnerability scans Clark is designed to operate exactly like a human internal tools team. It's built on a state-of-the-art multi-agent architecture, emulating your Designer, IT admin, Engineer, Security Operations, and QA employees. When Clark generates an application, you can modify it in 3 ways: 1. Natural language - talk to Clark 2. Visual – Edit it like in Figma 3. Code – Use your IDE like Cursor or VSCode Global enterprises in regulated industries like Instacart (CART), Carrier (CARR), and Cvent (Blackstone) already run their mission-critical apps on our platform. Our customers save $5m on average. Book a demo and we'll save you $5m too. And we're so confident that if we can't, we'll donate $5,000 to a charity of your choice: Book a Demo – --------- As part of this launch, we’re giving away the system prompts of leading AI products like Cursor, Manus, and Codex. These 6,000 line system prompts have enabled them to become billion-dollar companies on top of foundation models. Retweet this post and comment ‘Superblocks’ below, and we'll send you the link so you can engineer world class prompts yourself. 👇 See Clark in action in the thread below
2:54

I’m excited to introduce Clark, the first AI Agent to build internal enterprise apps. We’ve raised $60M, including a fresh $23M from Spark Capital Kleiner Perkins, Meritech Capital and Greenoaks. Unlike consumer vibe coding tools like Lovable, Replit and Bolt that only generate prototypes, Clark builds production-ready internal apps — enforcing your enterprise standards: 🧩 UIs generated using your design system 🔌 Integrations with private APIs, databases and SaaS apps 🔐 Permissions mapped to Okta & Microsoft Entra ID (Azure AD) groups 🛡️ Security with audit logging, secrets management, and vulnerability scans Clark is designed to operate exactly like a human internal tools team. It's built on a state-of-the-art multi-agent architecture, emulating your Designer, IT admin, Engineer, Security Operations, and QA employees. When Clark generates an application, you can modify it in 3 ways: 1. Natural language - talk to Clark 2. Visual – Edit it like in Figma 3. Code – Use your IDE like Cursor or VSCode Global enterprises in regulated industries like Instacart (CART), Carrier (CARR), and Cvent (Blackstone) already run their mission-critical apps on our platform. Our customers save $5m on average. Book a demo and we'll save you $5m too. And we're so confident that if we can't, we'll donate $5,000 to a charity of your choice: Book a Demo – --------- As part of this launch, we’re giving away the system prompts of leading AI products like Cursor, Manus, and Codex. These 6,000 line system prompts have enabled them to become billion-dollar companies on top of foundation models. Retweet this post and comment ‘Superblocks’ below, and we'll send you the link so you can engineer world class prompts yourself. 👇 See Clark in action in the thread below

Brad Menezes

1,861,699 просмотров • 1 год назад

WorkOS: The Enterprise Stack for the AI Era AI companies are just B2B SaaS with a new engine. They monetize like SaaS, sell like SaaS, and scale into the enterprise like SaaS. The difference is velocity. These new products grow so fast that the old playbook of “PLG for years, enterprise later” simply does not work anymore. PMF is no longer enough. Winning your market requires crossing the Enterprise Chasm almost immediately. Enterprise auth, provisioning, RBAC, compliance, billing, IT integrations. If you wait, someone else takes your market while you are still wiring SCIM. This is why WorkOS exists. We give developers the infrastructure they need to scale up-market on day one. Even if you have never touched WorkOS, you have already used it through products like ChatGPT, Perplexity, Cursor and many others. Today we operate 81M+ enterprise user accounts, 67M API calls per day, and 38K+ connected enterprise environments. Modern AI tools already run through WorkOS. At ERC, we unveiled six major launches expanding that foundation for the AI era: • AuthKit for ChatGPT Apps: secure OAuth + MCP so developers can connect real enterprise data to 700M+ weekly ChatGPT users. • AuthKit for Platforms: embed full enterprise identity into frameworks like Supabase and Convex for zero-friction onboarding. • Stripe Usage Sync: actual per-seat billing with no glue code. Data stays consistent and invoices are always correct. • WorkOS Pipes: the fastest and most secure way to ship integrations (Salesforce, Slack, Intercom and more). • Agent-Ready API Keys: scoped, revocable keys designed for both developers and autonomous agents. • WorkOS Studio: vibe coding for the enterprise. A collaborative AI app builder with SSO, SCIM, RBAC, audit logs, workflows and third-party connectors built-in. Internal software at the speed of a prompt. AI has already changed how software gets created. The next shift is changing what software becomes. We are still “filming the play” like early cinema: using new technology to recreate old patterns. The opportunity ahead is to invent entirely new applications that only AI-native development makes possible. WorkOS is building the enterprise infrastructure that lets teams design the next era of software itself. Enterprise-ready AI is where the real acceleration happens. Let’s build that future together.
1:10:04

WorkOS: The Enterprise Stack for the AI Era AI companies are just B2B SaaS with a new engine. They monetize like SaaS, sell like SaaS, and scale into the enterprise like SaaS. The difference is velocity. These new products grow so fast that the old playbook of “PLG for years, enterprise later” simply does not work anymore. PMF is no longer enough. Winning your market requires crossing the Enterprise Chasm almost immediately. Enterprise auth, provisioning, RBAC, compliance, billing, IT integrations. If you wait, someone else takes your market while you are still wiring SCIM. This is why WorkOS exists. We give developers the infrastructure they need to scale up-market on day one. Even if you have never touched WorkOS, you have already used it through products like ChatGPT, Perplexity, Cursor and many others. Today we operate 81M+ enterprise user accounts, 67M API calls per day, and 38K+ connected enterprise environments. Modern AI tools already run through WorkOS. At ERC, we unveiled six major launches expanding that foundation for the AI era: • AuthKit for ChatGPT Apps: secure OAuth + MCP so developers can connect real enterprise data to 700M+ weekly ChatGPT users. • AuthKit for Platforms: embed full enterprise identity into frameworks like Supabase and Convex for zero-friction onboarding. • Stripe Usage Sync: actual per-seat billing with no glue code. Data stays consistent and invoices are always correct. • WorkOS Pipes: the fastest and most secure way to ship integrations (Salesforce, Slack, Intercom and more). • Agent-Ready API Keys: scoped, revocable keys designed for both developers and autonomous agents. • WorkOS Studio: vibe coding for the enterprise. A collaborative AI app builder with SSO, SCIM, RBAC, audit logs, workflows and third-party connectors built-in. Internal software at the speed of a prompt. AI has already changed how software gets created. The next shift is changing what software becomes. We are still “filming the play” like early cinema: using new technology to recreate old patterns. The opportunity ahead is to invent entirely new applications that only AI-native development makes possible. WorkOS is building the enterprise infrastructure that lets teams design the next era of software itself. Enterprise-ready AI is where the real acceleration happens. Let’s build that future together.

Michael Grinich

37,894 просмотров • 9 месяцев назад

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.
1:21

Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Aaron Levie

91,863 просмотров • 11 месяцев назад

Dario Amodei keeps warning that open-source AI models are dangerous and need to be restricted. David Sacks David Sacks answered with a rhetorical question: "Dangerous to whom?" It was the sharpest thing said on the latest All-In The All-In Podcast. Not dangerous to the enterprise that wants to keep control of its own data. Open weights are what finally give that enterprise a choice. It is dangerous to Anthropic's business model. That model only works if customers have no real alternative at the model layer. So the safety argument starts to look like a moat argument wearing a lab coat. Line it up with the rest of the strategy. Dominate the layer everyone builds on. Launch Claude into science, security, legal, finance, and design, the verticals your own customers used to own. And campaign to restrict the open models that would hand those customers the exit. Here is the giveaway. When the person selling you safety also needs you to have no other option, you are not who is being kept safe.
0:22

Dario Amodei keeps warning that open-source AI models are dangerous and need to be restricted. David Sacks David Sacks answered with a rhetorical question: "Dangerous to whom?" It was the sharpest thing said on the latest All-In The All-In Podcast. Not dangerous to the enterprise that wants to keep control of its own data. Open weights are what finally give that enterprise a choice. It is dangerous to Anthropic's business model. That model only works if customers have no real alternative at the model layer. So the safety argument starts to look like a moat argument wearing a lab coat. Line it up with the rest of the strategy. Dominate the layer everyone builds on. Launch Claude into science, security, legal, finance, and design, the verticals your own customers used to own. And campaign to restrict the open models that would hand those customers the exit. Here is the giveaway. When the person selling you safety also needs you to have no other option, you are not who is being kept safe.

Fireside Alpha

10,037 просмотров • 1 месяц назад

We've all experienced how consumer AI can help in our personal lives, but that same experience hasn't been available at work. Most enterprise data is siloed, consumer AI can't access your work tools, and many orgs won't allow it due to security concerns. We built Amazon Quick Suite to solve those problems. It’s an agentic AI app that acts as your AI teammate at work – connecting to your business data and apps, and letting you interact with AI agents that can find insights, conduct research, and even take actions across your systems – with the security and privacy enterprises need. Quick works across your existing ecosystem, from emails to Salesforce to Slack to AWS services. You can create custom agents to draft content in your personal style, use Quick Research for in-depth analysis, or set up Quick Flow to automate tasks. And most importantly, your interactions never train the underlying models. We've been testing with Amazon employees and select customers and early results show just how transformational Quick is… cutting analysis time from months to days, and turning week-long tasks into minutes. This is an exciting moment and we're just getting started. Quick is available now, so have at it!
0:50

We've all experienced how consumer AI can help in our personal lives, but that same experience hasn't been available at work. Most enterprise data is siloed, consumer AI can't access your work tools, and many orgs won't allow it due to security concerns. We built Amazon Quick Suite to solve those problems. It’s an agentic AI app that acts as your AI teammate at work – connecting to your business data and apps, and letting you interact with AI agents that can find insights, conduct research, and even take actions across your systems – with the security and privacy enterprises need. Quick works across your existing ecosystem, from emails to Salesforce to Slack to AWS services. You can create custom agents to draft content in your personal style, use Quick Research for in-depth analysis, or set up Quick Flow to automate tasks. And most importantly, your interactions never train the underlying models. We've been testing with Amazon employees and select customers and early results show just how transformational Quick is… cutting analysis time from months to days, and turning week-long tasks into minutes. This is an exciting moment and we're just getting started. Quick is available now, so have at it!

Andy Jassy

49,241 просмотров • 10 месяцев назад

"Claude Code is the first AI tool that can actually recreate how a data scientist thinks." Here's my new episode with Sumeet Marwaha (Head of Data at Brex) on exactly how to build an AI analyst with Claude Code. We covered: ✅ 3 queries to build a data analyst MCP ✅ How to give the analyst both data + context ✅ Exclusive Brex data on which AI tools are crushing it in startups and enterprise Some quotes from Sumeet: "I've set up dashboards many times in my career. They end up getting ignored. Claude will always read it. Claude will always ask the right questions." "You have to set up Claude Code to not just look at the data. It should be able to search Slack, find the incident, and understand why your metrics look wrong." "This tool is absolutely crushing it based on our data. It's both the startup AND enterprise coding tool of choice." 📌 Watch now:
0:46

"Claude Code is the first AI tool that can actually recreate how a data scientist thinks." Here's my new episode with Sumeet Marwaha (Head of Data at Brex) on exactly how to build an AI analyst with Claude Code. We covered: ✅ 3 queries to build a data analyst MCP ✅ How to give the analyst both data + context ✅ Exclusive Brex data on which AI tools are crushing it in startups and enterprise Some quotes from Sumeet: "I've set up dashboards many times in my career. They end up getting ignored. Claude will always read it. Claude will always ask the right questions." "You have to set up Claude Code to not just look at the data. It should be able to search Slack, find the incident, and understand why your metrics look wrong." "This tool is absolutely crushing it based on our data. It's both the startup AND enterprise coding tool of choice." 📌 Watch now:

Peter Yang

39,600 просмотров • 6 месяцев назад

While your team is just getting started vibe coding prototypes, the team at Stripe is vibe coding a full vibe coding platform. Owen Williams ⚡ is a design manager at Stripe who built Protodash - an internal AI prototyping tool that lets designers and PMs spin up real, clickable prototypes in minutes. In this ep he walks through: - why generic AI tools produce "blurple slop" that doesn't match your design system - how he built a full prototyping studio on top of dev boxes with cursor + cc - why more PMs than designers now use Protodash - the design review mode that lets teams ship feedback direct in the app Plus, we talk about getting to the ultimate dream: demos, not memos. ty to our sponsors! 🧠 CESAR - Intelligent automation built for AI 💻 Cursor - The best way to code with AI Full episode on yt:
0:41

While your team is just getting started vibe coding prototypes, the team at Stripe is vibe coding a full vibe coding platform. Owen Williams ⚡ is a design manager at Stripe who built Protodash - an internal AI prototyping tool that lets designers and PMs spin up real, clickable prototypes in minutes. In this ep he walks through: - why generic AI tools produce "blurple slop" that doesn't match your design system - how he built a full prototyping studio on top of dev boxes with cursor + cc - why more PMs than designers now use Protodash - the design review mode that lets teams ship feedback direct in the app Plus, we talk about getting to the ultimate dream: demos, not memos. ty to our sponsors! 🧠 CESAR - Intelligent automation built for AI 💻 Cursor - The best way to code with AI Full episode on yt:

claire vo 🖤

92,220 просмотров • 3 месяцев назад

Agentic AI will transform every enterprise–but only if agents are trusted experts. The key: Evaluation & tuning on specialized, expert data. I’m excited to announce two new products to support this–Snorkel AI Evaluate & Expert Data-as-a-Service–along w/ our $100M Series D! --- Snorkel Evaluate is our new data-centric agentic AI evaluation platform for specialized, mission-critical enterprise settings where vibe checks and out-of-the-box metrics driven by simple LLM prompts are not enough. Snorkel Expert Data-as-a-Service is our white glove service for expert-level AI datasets, powering frontier LLM developers in areas like expert knowledge, reasoning, agentic action and tool use, and more! Both built on top of Snorkel AI’s Data Development Platform, using our programmatic technology to drive higher-quality expert data, faster– for getting specialized AI to real production value. If you’re building enterprise AI and want to partner around the key ingredient in AI today–the data–book a demo and let's talk! Finally, see thread for details on 🧵👇 - 📽️ A walkthrough of Snorkel Evaluate and Expert Data-as-a-Service on an agentic AI enterprise task - 📅 An upcoming event on Enterprise Agentic AI with innovators from Accenture @BNY Comcast Stanford University QBE & others - 📊 An upcoming series of benchmark datasets and model artifact releases 👀 Want early access to the full agentic AI dataset? Retweet this post and we'll send you the link!
5:42

Agentic AI will transform every enterprise–but only if agents are trusted experts. The key: Evaluation & tuning on specialized, expert data. I’m excited to announce two new products to support this–Snorkel AI Evaluate & Expert Data-as-a-Service–along w/ our $100M Series D! --- Snorkel Evaluate is our new data-centric agentic AI evaluation platform for specialized, mission-critical enterprise settings where vibe checks and out-of-the-box metrics driven by simple LLM prompts are not enough. Snorkel Expert Data-as-a-Service is our white glove service for expert-level AI datasets, powering frontier LLM developers in areas like expert knowledge, reasoning, agentic action and tool use, and more! Both built on top of Snorkel AI’s Data Development Platform, using our programmatic technology to drive higher-quality expert data, faster– for getting specialized AI to real production value. If you’re building enterprise AI and want to partner around the key ingredient in AI today–the data–book a demo and let's talk! Finally, see thread for details on 🧵👇 - 📽️ A walkthrough of Snorkel Evaluate and Expert Data-as-a-Service on an agentic AI enterprise task - 📅 An upcoming event on Enterprise Agentic AI with innovators from Accenture @BNY Comcast Stanford University QBE & others - 📊 An upcoming series of benchmark datasets and model artifact releases 👀 Want early access to the full agentic AI dataset? Retweet this post and we'll send you the link!

Alex Ratner

50,043 просмотров • 1 год назад

Chamath just delivered the clearest diagnosis of what is happening to enterprise software and the OpenAI Deployment Company is the most damning piece of evidence he could have picked. "The low end of the market is basically finished. There is no safe space." 90% of public SaaS stocks are down 30-80% from their 52 week highs, the median software stock is now negative over the last 3-6 months. Goldman Sachs reported that software forward P/E multiples fell from 35x to 20x, the lowest absolute level since 2014 and the smallest premium to the S&P 500 since 2010. The low end died first and fastest, because AI replaced it most directly. The small business tools, the lightweight project managers, the single function SaaS products that charged $49 a month per seat, those are being replaced by AI agents that do the same work as a workflow, not a product. You do not buy an AI powered tool, you describe what you need and it builds it and the seat based model that created the SaaS industry simply does not apply to that transaction. But Chamath's more interesting argument is about the high end and the tell he points to is perfect. OpenAI just raised $4 billion from 19 investors including TPG, Brookfield, Bain, and McKinsey to launch a consulting company and guaranteed those investors a 17.5% annual return to do it. On $4 billion in committed capital, that is roughly $700 million per year in guaranteed payouts, owed by a company that is projected to lose $14 billion in 2026. The goal of this venture is to compete directly with Deloitte, PwC, Ernst & Young, Andersen, and Cognizant. Think about what that structure reveals. OpenAI lost half of its enterprise LLM API market share from 50% to 25% between late 2023 and mid-2025, with Anthropic now leading at 32%. Its response was not to build a better model but rather to raise $4 billion, offer guaranteed PE-tier returns and hire embedded engineers to physically sit inside client organizations and make AI actually work in production. The reason, as Chamath identified, is that the high end of the market is not easy. "It's not like boop boop boop, put in a prompt and beep bap boop, it all works," he said and the data confirms exactly that. 88% of organizations running AI agents reported a security incident in the past year, 42% of C-suite executives say AI adoption is creating internal organizational conflict. The average enterprise AI consulting implementation costs $228,000 in year one versus $77,000 for platform-based approaches and most still stall before reaching production. Anthropic immediately matched OpenAI with a competing $1.5 billion consulting venture backed by Blackstone, Goldman Sachs, and Hellman & Friedman bringing the combined spend by the two leading AI labs on human powered enterprise deployment to $5.5 billion in a single month Chamath's read is that the high end, the large enterprise platforms like Salesforce with proprietary data flywheels, Palantir with its FDE model already proven at scale, Oracle with vertical specific data moats will survive and consolidate. The mid-market point solutions, the single function tools, the lightweight enterprise apps without defensible data assets, those are on the conveyor belt. The AI industry is not just disrupting the companies that use software but rather disrupting the companies that sell it.
1:05

Chamath just delivered the clearest diagnosis of what is happening to enterprise software and the OpenAI Deployment Company is the most damning piece of evidence he could have picked. "The low end of the market is basically finished. There is no safe space." 90% of public SaaS stocks are down 30-80% from their 52 week highs, the median software stock is now negative over the last 3-6 months. Goldman Sachs reported that software forward P/E multiples fell from 35x to 20x, the lowest absolute level since 2014 and the smallest premium to the S&P 500 since 2010. The low end died first and fastest, because AI replaced it most directly. The small business tools, the lightweight project managers, the single function SaaS products that charged $49 a month per seat, those are being replaced by AI agents that do the same work as a workflow, not a product. You do not buy an AI powered tool, you describe what you need and it builds it and the seat based model that created the SaaS industry simply does not apply to that transaction. But Chamath's more interesting argument is about the high end and the tell he points to is perfect. OpenAI just raised $4 billion from 19 investors including TPG, Brookfield, Bain, and McKinsey to launch a consulting company and guaranteed those investors a 17.5% annual return to do it. On $4 billion in committed capital, that is roughly $700 million per year in guaranteed payouts, owed by a company that is projected to lose $14 billion in 2026. The goal of this venture is to compete directly with Deloitte, PwC, Ernst & Young, Andersen, and Cognizant. Think about what that structure reveals. OpenAI lost half of its enterprise LLM API market share from 50% to 25% between late 2023 and mid-2025, with Anthropic now leading at 32%. Its response was not to build a better model but rather to raise $4 billion, offer guaranteed PE-tier returns and hire embedded engineers to physically sit inside client organizations and make AI actually work in production. The reason, as Chamath identified, is that the high end of the market is not easy. "It's not like boop boop boop, put in a prompt and beep bap boop, it all works," he said and the data confirms exactly that. 88% of organizations running AI agents reported a security incident in the past year, 42% of C-suite executives say AI adoption is creating internal organizational conflict. The average enterprise AI consulting implementation costs $228,000 in year one versus $77,000 for platform-based approaches and most still stall before reaching production. Anthropic immediately matched OpenAI with a competing $1.5 billion consulting venture backed by Blackstone, Goldman Sachs, and Hellman & Friedman bringing the combined spend by the two leading AI labs on human powered enterprise deployment to $5.5 billion in a single month Chamath's read is that the high end, the large enterprise platforms like Salesforce with proprietary data flywheels, Palantir with its FDE model already proven at scale, Oracle with vertical specific data moats will survive and consolidate. The mid-market point solutions, the single function tools, the lightweight enterprise apps without defensible data assets, those are on the conveyor belt. The AI industry is not just disrupting the companies that use software but rather disrupting the companies that sell it.

Milk Road AI

1,659,119 просмотров • 2 месяцев назад

Big pharma just handed the AI industry one of the most important reality checks of 2026 (Save this). david friedberg revealed that Anthropic approached major life sciences companies with a pitch, share your proprietary data, sign an NDA and we will give you early access to a specialized life sciences model and nearly every company they spoke with said no. Here is what these pharma companies understood that many enterprises still have not. A large pharmaceutical company may have spent decades and tens of billions of dollars generating proprietary datasets, clinical trial results, genomic sequences, drug interaction data, compound libraries. That data is the business and the competitive moat that separates them from every other player in the industry lives in those datasets. Handing it to an AI lab in exchange for early access to a model is essentially handing your most valuable asset to a company whose entire business model depends on combining your data with everyone else's and then selling the output back to you and to your competitors. Palantir CEO Alex Karp made this exact point that enterprise leaders are paying for AI tokens that generate no tangible business value while simultaneously surrendering their most sensitive operational data to external providers. He called this transferring a company's alpha, the unique advantage that secures the business directly to a third-party lab. Microsoft CEO Satya Nadella echoed the same concern independently, warning that entire sectors might find their accumulated knowledge commoditized if they do not build their own data and model ownership layers. The structural problem is not unique to pharma but it applies to every enterprise sector. Every time an employee runs a query through a third-party frontier model, proprietary workflows, customer data, and strategic processes pass through infrastructure the enterprise does not control. The data already shows the market moving, Open-source captured 67% of all AI tokens processed in the first half of 2026, up from a fraction of that just twelve months earlier. The performance gap between proprietary frontier models and open-source alternatives has nearly closed, DeepSeek costs approximately 1/36th of GPT-5 for comparable workloads. What pharma figured out and what enterprises across every sector are starting to realize is that the model is not the moat but the data is. And once you hand your data to a model company, you have permanently surrendered the asset that took you decades and billions of dollars to build.
3:21

Big pharma just handed the AI industry one of the most important reality checks of 2026 (Save this). david friedberg revealed that Anthropic approached major life sciences companies with a pitch, share your proprietary data, sign an NDA and we will give you early access to a specialized life sciences model and nearly every company they spoke with said no. Here is what these pharma companies understood that many enterprises still have not. A large pharmaceutical company may have spent decades and tens of billions of dollars generating proprietary datasets, clinical trial results, genomic sequences, drug interaction data, compound libraries. That data is the business and the competitive moat that separates them from every other player in the industry lives in those datasets. Handing it to an AI lab in exchange for early access to a model is essentially handing your most valuable asset to a company whose entire business model depends on combining your data with everyone else's and then selling the output back to you and to your competitors. Palantir CEO Alex Karp made this exact point that enterprise leaders are paying for AI tokens that generate no tangible business value while simultaneously surrendering their most sensitive operational data to external providers. He called this transferring a company's alpha, the unique advantage that secures the business directly to a third-party lab. Microsoft CEO Satya Nadella echoed the same concern independently, warning that entire sectors might find their accumulated knowledge commoditized if they do not build their own data and model ownership layers. The structural problem is not unique to pharma but it applies to every enterprise sector. Every time an employee runs a query through a third-party frontier model, proprietary workflows, customer data, and strategic processes pass through infrastructure the enterprise does not control. The data already shows the market moving, Open-source captured 67% of all AI tokens processed in the first half of 2026, up from a fraction of that just twelve months earlier. The performance gap between proprietary frontier models and open-source alternatives has nearly closed, DeepSeek costs approximately 1/36th of GPT-5 for comparable workloads. What pharma figured out and what enterprises across every sector are starting to realize is that the model is not the moat but the data is. And once you hand your data to a model company, you have permanently surrendered the asset that took you decades and billions of dollars to build.

Milk Road AI

16,317 просмотров • 1 месяц назад

We’re launching a brand new, full-stack vibe coding experience in Google AI Studio, made possible by integrations with the Google Antigravity coding agent and Firebase backends. This unlocks: — Full-stack multiplayer experiences: Create complex, multiplayer apps with fully-featured UIs and backends directly within AI Studio — Connection to real-world services: Build applications that connect to live data sources, databases, or payment processors and the Antigravity agent will securely store your API credentials for you — A smarter agent that works even when you don't: By maintaining a deeper understanding of your project structure and chat history, the agent can execute multi-step code edits from simpler prompts. It also remembers where you left off and completes your tasks while you’re away, so you can seamlessly resume your builds from anywhere — Configuration of database connections and authentication flows: Add Firebase integration to provision Cloud Firestore for databases and Firebase authentication for secure sign-in This demo displays what can be built in the new vibe coding experience in AI Studio. Geoseeker is a full-stack application that manages real-time multiplayer states, compass-based logic, and an external API integration with Google Maps 🕹️
0:33

We’re launching a brand new, full-stack vibe coding experience in Google AI Studio, made possible by integrations with the Google Antigravity coding agent and Firebase backends. This unlocks: — Full-stack multiplayer experiences: Create complex, multiplayer apps with fully-featured UIs and backends directly within AI Studio — Connection to real-world services: Build applications that connect to live data sources, databases, or payment processors and the Antigravity agent will securely store your API credentials for you — A smarter agent that works even when you don't: By maintaining a deeper understanding of your project structure and chat history, the agent can execute multi-step code edits from simpler prompts. It also remembers where you left off and completes your tasks while you’re away, so you can seamlessly resume your builds from anywhere — Configuration of database connections and authentication flows: Add Firebase integration to provision Cloud Firestore for databases and Firebase authentication for secure sign-in This demo displays what can be built in the new vibe coding experience in AI Studio. Geoseeker is a full-stack application that manages real-time multiplayer states, compass-based logic, and an external API integration with Google Maps 🕹️

Google AI

4,747,498 просмотров • 4 месяцев назад

New short course: Vibe Coding 101 with Replit! Learn to build and host applications with an AI agent in this course, built in partnership with Replit ⠕ and taught by its President Michele Catasta and Head of Developer Relations . Coding agents are changing how we write code. "Vibe coding" refers to a growing practice where you might barely look at the generated code, and instead focus on the architecture and features of your application. However, contrary to popular belief, effectively coding this way isn't done by just prompting, accepting all recommendations, and hoping for the best. It requires structuring your work, refining your prompts, and having a systematic process that lead to a more efficient and effective workflow. I code frequently using LLMs, and asking an LLM to do everything in one shot usually does not work. I'll typically take a problem, partition it into manageable modules, spend time creating prompts to specify each module, and use the model to produce the code one module at a time, and test/debug each module before moving on. A process like this is making me and many other developers faster and more efficient. In this video-only course, you’ll learn how to use Replit’s cloud environment--with an integrated code editor, package manager, and deployment tools--to build and deploy web applications. Along the way, you’ll learn strategies for working effectively with agents and improve your development skills. In detail, you’ll: - Understand principles of agentic code development such as being precise, giving agents one task at a time, making prompts specific, keeping projects tidy, starting with fresh sessions for each new feature, and how to approach debugging. - Learn how to get started with Replit, and key skills for vibe coding: Thinking, using frameworks, checkpoints, debugging, and providing context. - Create a product requirement document (PRD) and wireframe for your agent to build a prototype of a website performance analyzer. - See how to use an agent to make your prototype more visually appealing, and deploy it application others to access . - Learn to build a head-to-head national park ranking app, from a sample dataset, with voting capabilities and persistent data storage, and refine further ask the assistant to recap and explain what it built to find room for improvement and reinforce your learning. By the end of this course, you’ll have a solid foundation in building with coding agents, and a process you can use to keep vibe coding effectively. Please sign up here:
2:10

New short course: Vibe Coding 101 with Replit! Learn to build and host applications with an AI agent in this course, built in partnership with Replit ⠕ and taught by its President Michele Catasta and Head of Developer Relations . Coding agents are changing how we write code. "Vibe coding" refers to a growing practice where you might barely look at the generated code, and instead focus on the architecture and features of your application. However, contrary to popular belief, effectively coding this way isn't done by just prompting, accepting all recommendations, and hoping for the best. It requires structuring your work, refining your prompts, and having a systematic process that lead to a more efficient and effective workflow. I code frequently using LLMs, and asking an LLM to do everything in one shot usually does not work. I'll typically take a problem, partition it into manageable modules, spend time creating prompts to specify each module, and use the model to produce the code one module at a time, and test/debug each module before moving on. A process like this is making me and many other developers faster and more efficient. In this video-only course, you’ll learn how to use Replit’s cloud environment--with an integrated code editor, package manager, and deployment tools--to build and deploy web applications. Along the way, you’ll learn strategies for working effectively with agents and improve your development skills. In detail, you’ll: - Understand principles of agentic code development such as being precise, giving agents one task at a time, making prompts specific, keeping projects tidy, starting with fresh sessions for each new feature, and how to approach debugging. - Learn how to get started with Replit, and key skills for vibe coding: Thinking, using frameworks, checkpoints, debugging, and providing context. - Create a product requirement document (PRD) and wireframe for your agent to build a prototype of a website performance analyzer. - See how to use an agent to make your prototype more visually appealing, and deploy it application others to access . - Learn to build a head-to-head national park ranking app, from a sample dataset, with voting capabilities and persistent data storage, and refine further ask the assistant to recap and explain what it built to find room for improvement and reinforce your learning. By the end of this course, you’ll have a solid foundation in building with coding agents, and a process you can use to keep vibe coding effectively. Please sign up here:

Andrew Ng

752,508 просмотров • 1 год назад

Your AI Agents can only analyze 1GB of data Enterprises have 1,000,000x as much data We fixed that... It took 3 years and building an entirely new execution environment to pull this off. TextQL lets AI agents analyze your ENTIRE enterprise data - not just the 0.0001% they can handle today. Here's what makes it so different: 1. ALL your data, not samples. AI today: 1GB demos. Your reality: 350TB across 47 systems. TextQL: Everything. At once. 2. Real enterprise questions.ChatGPT writes poems. We answer why renewal rates dropped 3% after ERP upgrades. 3. No more sandbox limitations.Everyone else: Jupyter notebook with 10GB RAM. Us: A data center. ------------------------------ In 2021, I watched Fortune 500s spend millions on AI that could only analyze data that fits on a USB stick. Today: - We process 10+ petabytes daily - Our biggest customer analyzes 100TB in real-time - Average query spans 50+ data sources And we just proved what everyone said was impossible - AI that can actually build a digital twin of your enterprise ------------------------------ To celebrate, we're giving away free data architecture assessments. We'll show you exactly how much of your data is invisible to AI right now (spoiler: it's probably 99.9%). Comment "TextQL" and we'll send you the assessment link.
1:09

Your AI Agents can only analyze 1GB of data Enterprises have 1,000,000x as much data We fixed that... It took 3 years and building an entirely new execution environment to pull this off. TextQL lets AI agents analyze your ENTIRE enterprise data - not just the 0.0001% they can handle today. Here's what makes it so different: 1. ALL your data, not samples. AI today: 1GB demos. Your reality: 350TB across 47 systems. TextQL: Everything. At once. 2. Real enterprise questions.ChatGPT writes poems. We answer why renewal rates dropped 3% after ERP upgrades. 3. No more sandbox limitations.Everyone else: Jupyter notebook with 10GB RAM. Us: A data center. ------------------------------ In 2021, I watched Fortune 500s spend millions on AI that could only analyze data that fits on a USB stick. Today: - We process 10+ petabytes daily - Our biggest customer analyzes 100TB in real-time - Average query spans 50+ data sources And we just proved what everyone said was impossible - AI that can actually build a digital twin of your enterprise ------------------------------ To celebrate, we're giving away free data architecture assessments. We'll show you exactly how much of your data is invisible to AI right now (spoiler: it's probably 99.9%). Comment "TextQL" and we'll send you the assessment link.

ethan ding 📊

161,984 просмотров • 1 год назад

Box CEO Aaron Levie's defense of software over vibe-coded, n-of-1 internal tools: "If you're Ford, and you're doing your supply chain on an ERP system, you want that to work the exact same way every single time." "The billions of transactions going through that ERP system, you cannot take for granted. So the idea that you're going to go vibe-code that is not possible, or at least not likely." "The other point is: your company has a fixed amount of IT resources. And you have to decide what you're going to spend your time on as an organization." "Do you want to spend time on rebuilding something that the market can supply you, that's seen best practices thousands of times? Or do you want to go and build that out with your n-of-1 experience?" "Or do you want to spend your limited, scarce resources on building software, and building experiences, that will make you more money, and that will actually be used by your customers?" "I think on the margin the average enterprise is going to spend their time and energy on the latter." "I'm 100% bullish on vibe-coding, 100% bullish that we're going to have 100x more software. But that still doesn't cross the threshold where I would want to go build our own CRM system."
1:37

Box CEO Aaron Levie's defense of software over vibe-coded, n-of-1 internal tools: "If you're Ford, and you're doing your supply chain on an ERP system, you want that to work the exact same way every single time." "The billions of transactions going through that ERP system, you cannot take for granted. So the idea that you're going to go vibe-code that is not possible, or at least not likely." "The other point is: your company has a fixed amount of IT resources. And you have to decide what you're going to spend your time on as an organization." "Do you want to spend time on rebuilding something that the market can supply you, that's seen best practices thousands of times? Or do you want to go and build that out with your n-of-1 experience?" "Or do you want to spend your limited, scarce resources on building software, and building experiences, that will make you more money, and that will actually be used by your customers?" "I think on the margin the average enterprise is going to spend their time and energy on the latter." "I'm 100% bullish on vibe-coding, 100% bullish that we're going to have 100x more software. But that still doesn't cross the threshold where I would want to go build our own CRM system."

TBPN

383,971 просмотров • 6 месяцев назад

Box CEO Aaron Levie's defense of software over vibe-coded, n-of-1 internal tools: "If you're Ford, and you're doing your supply chain on an ERP system, you want that to work the exact same way every single time." "The billions of transactions going through that ERP system, you cannot take for granted. So the idea that you're going to go vibe-code that is not possible, or at least not likely." "The other point is: your company has a fixed amount of IT resources. And you have to decide what you're going to spend your time on as an organization." "Do you want to spend time on rebuilding something that the market can supply you, that's seen best practices thousands of times? Or do you want to go and build that out with your n-of-1 experience?" "Or do you want to spend your limited, scarce resources on building software, and building experiences, that will make you more money, and that will actually be used by your customers?" "I think on the margin the average enterprise is going to spend their time and energy on the latter." "I'm 100% bullish on vibe-coding, 100% bullish that we're going to have 100x more software. But that still doesn't cross the threshold where I would want to go build our own CRM system."
1:37

Box CEO Aaron Levie's defense of software over vibe-coded, n-of-1 internal tools: "If you're Ford, and you're doing your supply chain on an ERP system, you want that to work the exact same way every single time." "The billions of transactions going through that ERP system, you cannot take for granted. So the idea that you're going to go vibe-code that is not possible, or at least not likely." "The other point is: your company has a fixed amount of IT resources. And you have to decide what you're going to spend your time on as an organization." "Do you want to spend time on rebuilding something that the market can supply you, that's seen best practices thousands of times? Or do you want to go and build that out with your n-of-1 experience?" "Or do you want to spend your limited, scarce resources on building software, and building experiences, that will make you more money, and that will actually be used by your customers?" "I think on the margin the average enterprise is going to spend their time and energy on the latter." "I'm 100% bullish on vibe-coding, 100% bullish that we're going to have 100x more software. But that still doesn't cross the threshold where I would want to go build our own CRM system."

TBPN

34,529 просмотров • 5 месяцев назад

Introducing Anything Max: Vibe Coding that's leaps above Lovable and Bolt We've raised money at a $100M valuation and built what we believe is the future of vibe coding. We asked 100 vibe coders to build their apps side by side on Lovable, Bolt, and Anything Max and they rated Anything Max the winner across all 3 categories - accuracy, design, and 'overall'. Here's why: • Full-stack control: Max can test backend hooks, branch database states, and debug issues, because Anything owns the full infrastructure. • Max can load up your app in its own browser and click on all buttons like a human tester to find all edge case bugs, then trace the bug across the stack - could be a frontend, backend, or a database issue (only we can do this, read #1) and autonomously fix it with 97% accuracy. Lovable and Bolt build prototypes, but Max users are building production-ready apps and already charging money for them. Blake built a gut biome app to $10K run rate Anthony built a referral tool to $20k in revenue Yuri built a suite of apps doing $40K Build your app with Max: -------------------------------------------- We're hosting a $100K Hackathon to help people grow their app to $10K MRR. - We'll teach you everything we know about growing to 1M users. - You'll have 30 days to build a real product in public and get paying customers for it. If you do it well, you can start the New Year with a functioning business. Retweet and comment “LFG”, and we’ll send you a $100 discount code and the link to participate
2:14

Introducing Anything Max: Vibe Coding that's leaps above Lovable and Bolt We've raised money at a $100M valuation and built what we believe is the future of vibe coding. We asked 100 vibe coders to build their apps side by side on Lovable, Bolt, and Anything Max and they rated Anything Max the winner across all 3 categories - accuracy, design, and 'overall'. Here's why: • Full-stack control: Max can test backend hooks, branch database states, and debug issues, because Anything owns the full infrastructure. • Max can load up your app in its own browser and click on all buttons like a human tester to find all edge case bugs, then trace the bug across the stack - could be a frontend, backend, or a database issue (only we can do this, read #1) and autonomously fix it with 97% accuracy. Lovable and Bolt build prototypes, but Max users are building production-ready apps and already charging money for them. Blake built a gut biome app to $10K run rate Anthony built a referral tool to $20k in revenue Yuri built a suite of apps doing $40K Build your app with Max: -------------------------------------------- We're hosting a $100K Hackathon to help people grow their app to $10K MRR. - We'll teach you everything we know about growing to 1M users. - You'll have 30 days to build a real product in public and get paying customers for it. If you do it well, you can start the New Year with a functioning business. Retweet and comment “LFG”, and we’ll send you a $100 discount code and the link to participate

Anything

777,723 просмотров • 8 месяцев назад

.carried_no_interest says the rumors that enterprise customers are no longer signing up for 3-year contracts — combined with "adjacency in the market map" — is way more of a threat to software private equity than vibe coding: "The entire basis of software PE being one of the best private market categories was that you had enterprise customers with 3-year contracts." "If every other enterprise customer says, 'AI is just so amazing now, and we don't know where we're going to be, and what it's going to look like, so we're only doing 1-year deals,' that has much bigger implications than someone vibecoding a Notion clone on a Sunday. That's a big deal." "And I think the reason they're doing that — and this is the bigger threat to a lot of these companies — is the adjacency in the market map." "If you look like Rippling, and you have a very talented group of people like their employees, they can attack so many parts of the HR platform space now that they simply couldn't 3 years ago." "That's a lot scarier than vibe coding, than somebody just whipping up [a clone] and going out with a bunch of cold emails. It's the adjacency threats in the market map. Because now, who's Rippling going to go after?"
1:37

.carried_no_interest says the rumors that enterprise customers are no longer signing up for 3-year contracts — combined with "adjacency in the market map" — is way more of a threat to software private equity than vibe coding: "The entire basis of software PE being one of the best private market categories was that you had enterprise customers with 3-year contracts." "If every other enterprise customer says, 'AI is just so amazing now, and we don't know where we're going to be, and what it's going to look like, so we're only doing 1-year deals,' that has much bigger implications than someone vibecoding a Notion clone on a Sunday. That's a big deal." "And I think the reason they're doing that — and this is the bigger threat to a lot of these companies — is the adjacency in the market map." "If you look like Rippling, and you have a very talented group of people like their employees, they can attack so many parts of the HR platform space now that they simply couldn't 3 years ago." "That's a lot scarier than vibe coding, than somebody just whipping up [a clone] and going out with a bunch of cold emails. It's the adjacency threats in the market map. Because now, who's Rippling going to go after?"

TBPN

18,851 просмотров • 4 месяцев назад