I've been personally burning through billions of tokens a week for the past few months as a builder. Today I'm excited to announce Hyperagent, by Airtable. An agents platform where every session gets its own isolated, full computing environment in the cloud — no Mac Mini required. Real browser, code execution, image/video generation, data warehouse access, hundreds of integrations, and the ability to learn any new API as a skill. Deep domain expertise through skill learning. Teach the agent how your firm evaluates startups or how your team runs due diligence — now anyone on the team gets output that reflects your actual methodology, not a generic template. One-click deployment into Slack as intelligent coworkers. These aren't bots that wait to be @mentioned — they follow conversations, understand context, and act when relevant. And a command center to oversee and continuously improve your entire fleet of agents at scale. We're onboarding early users now. hyperagent.com

Introducing Built with Omni: a new showcase for builders to share bespoke AI apps 🚀 Explore & Remix: Grab ready-to-go elements for your bases Learn: Inspect real-world code to master prompting Contribute: Share your creations

Sharing an interesting recent conversation on AI's impact on the economy. AI has been compared to various historical precedents: electricity, industrial revolution, etc., I think the strongest analogy is that of AI as a new computing paradigm (Software 2.0) because both are fundamentally about the automation of digital information processing. If you were to forecast the impact of computing on the job market in ~1980s, the most predictive feature of a task/job you'd look at is to what extent the algorithm of it is fixed, i.e. are you just mechanically transforming information according to rote, easy to specify rules (e.g. typing, bookkeeping, human calculators, etc.)? Back then, this was the class of programs that the computing capability of that era allowed us to write (by hand, manually). With AI now, we are able to write new programs that we could never hope to write by hand before. We do it by specifying objectives (e.g. classification accuracy, reward functions), and we search the program space via gradient descent to find neural networks that work well against that objective. This is my Software 2.0 blog post from a while ago. In this new programming paradigm then, the new most predictive feature to look at is verifiability. If a task/job is verifiable, then it is optimizable directly or via reinforcement learning, and a neural net can be trained to work extremely well. It's about to what extent an AI can "practice" something. The environment has to be resettable (you can start a new attempt), efficient (a lot attempts can be made), and rewardable (there is some automated process to reward any specific attempt that was made). The more a task/job is verifiable, the more amenable it is to automation in the new programming paradigm. If it is not verifiable, it has to fall out from neural net magic of generalization fingers crossed, or via weaker means like imitation. This is what's driving the "jagged" frontier of progress in LLMs. Tasks that are verifiable progress rapidly, including possibly beyond the ability of top experts (e.g. math, code, amount of time spent watching videos, anything that looks like puzzles with correct answers), while many others lag by comparison (creative, strategic, tasks that combine real-world knowledge, state, context and common sense). Software 1.0 easily automates what you can specify. Software 2.0 easily automates what you can verify.

IDEA ABSORPTION Idea generation is one of my biggest strengths as a problem solver but the inability to watch when / where i was doing it, was a big weakness early in my career as an executive / leader. I didn’t even realize I was inundating my org with ideas, until a peer Eng leader came to me and said “Gokul you’ve got to stop generating idea confetti. The org needs focused execution, not more ideas.” I’ve never forgotten that line from nearly two decades ago. I made two changes: (a) ideating in very small, mostly 1:1,settings with senior leaders, to flesh out a concept (I do this with AI today!), vs in groups, unless the goal of the group meeting was to ideate, and (b) trying to be Socratic in phrasing: “should we consider X” and being clear that X is a suggestion, not a directive, and it’s the team’s job to decide if X makes sense vs all the other things on their plate. Learnt this the hard way after being presented work output by someone who had worked on a project for a week based on misinterpreting a throwaway comment from me at a meeting. It was cathartic to see that Bezos had the same issue early on in his career. From a recent interview: “Jeff Wilke came to me one day he worked for Amazon for a quarter of a century but this is when he probably knew me only for a year and he said “Jeff you have enough ideas to destroy Amazon.” And this was such a shocking idea for me Jeff said you have enough ideas per minute per day per week to destroy Amazon. I was like what do you what do you mean? He’s like you have to release the work at the right rate that the organization can accept it and he was a manufacturing expert and so you know his view of the world was every time I released an idea I was creating a backlog a queue work in process and because it was just stacking up it was adding no value and in fact it was creating distraction and so he said ‘Look you have to figure out when to release these new ideas at a rate that the organization can accept them and this was I mean this sounds so obvious but it was not obvious at the time to me and this was a profound insight for me and so I started prioritizing the ideas better keeping lists of them keeping them to myself until the organization was ready for the ideas and then I also started figuring out how can I build an organization that can be ready for more ideas that’s about having the right senior team and the right leadership and getting those people the executive bandwidth so they could do more ideas per unit time and so and and that is what we built we built a company that’s very good at inventing and doing more than one thing at a time and you do want to build as the company gets bigger you do want to be able to do more than one thing at a time but that idea of releasing the work was very profound for me and it made it made us operationally more effective while still being inventive and do you think you’re a better inventor.”

🌠Today, we’re excited to relaunch Airtable as the AI-native app platform, combining the magic of vibe coding business apps with real production-readiness and scalability, and embedding them with an army of agents that automate thousands of hours of work in seconds. Instead of just adding more AI capabilities to our existing platform, we treated this as a refounding moment for the company. We started with a clean-slate imagining of the ideal form factor for building apps in the agentic era. (If you want to skip all the backstory and just try it out, you can just go to Airtable.com. All new signups get the new AI experience, and existing accounts can switch over using this link: bit.ly/4leXbZ0). Thread and demos below👇

We're excited to launch 🚀@Airtable AI Assistant 🚀 today, along with AI document analysis and AI web research capabilities! Airtable was founded 12 years ago with the mission of democratizing software creation. Our pioneering innovation was to distill app-building concepts (data, logic, interface) into intuitive visual components, like a no-code lego kit for app building. At the time, we speculated that someday, maybe AI would get good enough to enable conversational app building–talking to an expert AI app builder–and be a huge unlock, making app building even more accessible. We’re now at that point. While surprisingly impressive text generation and manipulation by LLMs was the breakthrough of the 2022 ChatGPT moment, the emergence of surprisingly impressive reasoning capability from LLMs is the breakthrough of 2025. This is unlocking more autonomous agentic experiences, and generating apps and code is the first killer use case (@cursor_ai, @windsurf_ai, @DevinAI, @v0, @boltdotnew, @Replit Agent to name a few). But for the large class of non-technical builders, a different approach is needed. When AI generates apps with code, rather than no-code building blocks, it requires a developer to fully understand how they work – and to verify them for hidden mistakes that would be tricky/impossible to debug by interface inspection alone (it may look right, but what is the data model business logic is flawed in non-obvious ways?). Airtable Assistant is an agent that can build and modify Airtable apps through conversation, changing schemas, adding automations, and designing interfaces. You can ask it to do things like: –“Research every conference attendee in this base” to have the Assistant immediately spin up an army of researchers that pull in background information for your attendees –“Analyze each contract to identify key risks they pose to my business” to have the Assistant add an AI field that runs an analysis at scale for each contract you’ve signed. Airtable Assistant can also answer questions about the data in your apps, like prompts as advanced as: –“I'm about to meet Jane Smith at Zelos, read all of their recent sales call transcripts and tell me how far along they are in their implementation and if they’re dealing with any issues” –“What are the most common risk factors in our contracts? Are there any changes to our default posture we might consider?” Credit to @mikeyk for introducing us to the concept of low floor and high ceiling in HCI many years ago, which has become part of our internal lexicon for thinking about product improvements. Assistant dramatically lowers the floor to building apps, including more sophisticated ones, by helping human builders translate their business requirements into the schema design, logic, and interfaces required to deliver on the use case. In addition to launching Airtable Assistant today, we’re also releasing the capability to deploy thousands of AI web researchers, and AI document analysts, to continuously work on the data in Airtable apps. You can do things like: –Pull strategy and value stories from every product requirement doc to draft launch and release messaging –Monitor all brand mentions across digital channels to measure campaign impact –Create an automatically updating competitive intelligence dossier with the latest news and messaging from every competitor in your industry Check it out 👇

This problem area has bounced around in the back of my mind as an obviously great opportunity -- so excited to see @omarjpeg , Nathan, and team executing on it so well and so quickly!

It's true, claude is better than writing in OpenAI's o1 model. However, o1 can do something claude cannot: process 3-4 hours of transcripts and create detailed worksheets, summaries, and FAQs that cite what the speaker has said word for word, and recall every question answer pair.

I’m hiring Product Designers for @airtable AI! We’re creating tools to make AI actually work for work, helping teams easily connect data, generate insights, and automate workflows.

the beauty with enterprise sales is you just need ~5 customers as references to unlock the next 20 referenceability is how you unlock scale in the enterprise those first few enterprise buyers are worth 100X what they're paying ...