Moving from chatting back and forth with agents, to having agents that you spin up in the background to do useful work for you is probably on the order of 100X or 1000X more token consumption than what we’re seeing today. Most work right now is essentially rate limited by our ability to process and work with information. Moving data between systems. Reviewing a document. Looking through code for security issues. Processing log data to look for even patterns. Looking up information about a prospective client. Almost all of knowledge work is constrained by these steps, and AI has become a huge unlock to that. So our first step was to use AI to do these individual tasks much faster for us. That was the chatbot phenomenon that we have been experiencing for the past 3+ years. But we can still then only augment the work that we remain in the loop for, and we’re highly limited by our ability to parallelize this work by our own bandwidth and context. But where we’re going is quite different. As AI models continue to improve at incredible rates, we can hand off more and more work to the agent. First it’s letting the agent run with a harder problem. Then it’s giving a few agents multiple parts of a task at once. But we’re not far off from being able to throw an arbitrary number of agents at a problem and just say “come back when you’ve accomplished this goal”. Now imagine that agent swarm for any problem we can think of. Protecting our codebases, data, and systems. QAing every change we make and make sure all our infrastructure is robust and does what it should. Looking through every document for potential risks. Auditing this company with vastly more data reviewed. And on and on. Most of this isn’t even replacing existing work we do, it’s all net new. These agents will get deployed as swarms by people, by workflows, by triggers, and by other agents. And none of this would be possible if the models weren’t getting better, and if the cost of AI didn’t make it affordable. Both of those trends though are playing out. Now the next frontier is just having enough compute. But this is where it’s heading.

"What's the slowest thing to move in technology? The humans that work with it." @Alfred_Lin on why the gap between model capability and real-world use is where the opportunity is at @stanfordaiclub

AI can code. AI can design. AI can write. AI can analyze. What's left that only humans can do?

You're in a tech interview. The interviewer asks: "Why should we hire you when Claude Code can already write code?" What's your answer?

Where are the billion-dollar vibe-coded startups?

Today we’re laying off a large part of the Airtable team. This is not a reflection of their work but a reflection of our deep belief that small teams ship more and better. For Airtable, better is about to look like: - A new Core team shipping your top requests monthly (already 30+ shipped in 3 weeks) - Upgraded Omni, Airtable Canvas, a stronger MCP, and BYO agents, coming soon - New Performance and Infra teams focusing on speeding up bases and increasing limits on records, fields, and syncs To everyone leaving: thank you. If you’re hiring, these are excellent people and worth a call.

Ampersand is hiring! I recently took some time to reflect on what I appreciate about our existing team members (or Ampersonians as I like to call them), and came up with our 6 cultural values: - 𝗖𝗿𝗮𝗳𝘁. We care about the details and the quality of our work. - 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗲𝗺𝗽𝗮𝘁𝗵𝘆. We are our customers’ extended team. - 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽. We take full end-to-end responsibility for our work. - 𝗩𝗲𝗹𝗼𝗰𝗶𝘁𝘆. We move fast without cutting corners. - 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆. We thrive in ambiguity and are always learning. - 𝗛𝘂𝗺𝗶𝗹𝗶𝘁𝘆. We are low ego, and are open to feedback. I've been a part of companies where values were more for marketing than anything else, but these are 𝗹𝗶𝘃𝗲𝗱 values exemplified by everyone that's on this journey with us. If you or someone you know resonate with these values, check out our open roles at ampersand.ai/careers#open-ro…. We are hiring across eng + GTM (including our first Eng Manager and Sales Engineer!)

You're in a tech interview and they ask you: "Why should we hire you when Codex can write code?" What's your answer?

NEW from Coatue: winners outperform losers over time.

Assorted things I've been mulling over regarding agents in the economy: • Companies are in some sense the original superintelligence. It's often the case that they can use their amortized cognitive surplus to advance their interests against those of certain consumers: relative to the optimization capacity of companies, consumers can be inattentive, underinformed, irrational, etc. (Think, say, of some of the classic insurance upsells at the end of a booking process, which is typically priced substantially above what a regular policy would cost.) Herbert Simon introduced the idea of bounded rationality, and firms often end up benefiting from it. Thanks to personal agents, we will probably get somewhat more rational in how we engage with companies. • How exactly will outcomes change? Well, certain kinds of price discrimination will work less well (how do coupons work if every agent hunts fastidiously for them?), and certain kinds of customer cross-subsidy will change (today, people who forget to cancel subscriptions subsidize those who don't). Taken across the economy as a whole, I'm not sure what the incidence of all of this will be. • In corporate strategy, people have long debated the relative importance of distribution and product quality. Perhaps you've made something better, but does anyone know about it? Won't people just continue to buy the ACME Corp Mousetraps? Personal agents will, I think, act as a kind of structural subsidy for product quality. How would markets look different if individuals always spent at least 10 hours researching their purchases? On the margin, focusing on making a product better is, I think, going to become a more effective strategy for companies. This is good! • It true, a corollary is that information about product quality will become more important and impactful. Today, most people have very limited ability to aggregate and integrate such data, but agents will have insatiable thirst for such signals. (It's often said that data is becoming more valuable because it helps in the training of models. That's obviously true, but I think there's a secondary way in which it's more valuable simply as a complement to cheaper intelligence.) • A big question is whether agents will increase superstar effects (rational agents all settle on the same product) or increase dispersion (because everyone has slightly different tastes and preferences, which agents are good at eliciting, finally overcoming the artificial clustering we've been subjected to). • Many internet companies have monetized demand routing, which is in turn often predicated on short attention spans. "I'll click on the first thing I see." This has led to some amount of inefficiency, as companies try to propitiate capricious ranking algorithms and incur various placement fees. It's not all deadweight loss (willingness to pay is itself a kind of signal, which is valuable to buyers), but it's not great. How will the nature and value of this routing change in a world of personal agents? I don't know the answer, but I think that the effects might be pretty big. • Much of what I'm writing hinges on the assumption that personal agents will be on the consumer's side. I suspect that that will be the winning strategy, but whether it's true, and what it means to be true, involves a lot of other questions. Taken as a whole, the market is a massively parallelized, decentralized, and highly imperfect value attribution machine, and, taking stock of all of the effects, it seems to me that the quality point may well be the biggest: that agents will change the nature of the backpropagation such that the rewards for better products increase. Maybe this is too optimistic, but I suspect (and hope) that companies that make things that they're proud of will feel that the universe is a little more partisan in their favor.

The quality of the questions that a founder asks the VC at the end of the meeting shows me so much about the quality and calibre of the founder.

enterprise maturity arc with AI = Dunning–Kruger Stage 1: building with agent’s internally Stage 2: a lot f*kin’ harder than we thought Stage 3: take our $$$, here’s what we want; let’s go

Don't confuse motion with progress. Many startups are very busy, but they aren't actually moving the needle. Velocity is speed with a specific direction.

2/ So, how does Ampersand help you build better AI products? Agents require a different level of integration with systems of record. Great integrations take an order of magnitude more engineering time. That means bi-directional actions on any object, custom fields included, with permissions, rate limits, configurations, and any other restriction scoped per customer. And at agent speed. Anyways, my co-founder explains this much better than me. Lauren built Firebase integrations at @Google.

OPENAI MATH NUKE | NVIDIA INVESTMENTS | NATIONAL COMPUTE GRID xitter.cf/i/broadcasts/1RKZzBZzP…

That's a good worry. I worry about that too. HubSpot is not overcharging for API/CLI/MCP. Our goal is to move towards economic ambivalence. That is -- we don't care how you access the platform (our classic web UI, our Breeze assistant or an AI superapp), we just want to make the platform capability available. It's very much a "platform play". Not easy, because the instinct is always to create a preference for your own internal apps/agents/harness. But what works long-term is to follow the customers and their preference. It's why we were the first to launch connectors for ChatGPT and Claude over a year ago. And, the growth in our customers using is growing *fast*. Troubling for some, but we try to just lean into it.