I discovered I was designing my AI tools backwards.

“The way to do a piece of writing is three or four times over, never once.” Writing is hard. John McPhee, who invented literary nonfiction that reads like a novel, developed a four-draft writing method that transforms chaotic ideas into compelling narratives.

Every portfolio manager knows the efficient frontier - the set of optimal portfolios offering maximum returns for given risk levels. What if AI prompts had their own efficient frontier?

How long & how quickly can a business compound? This is a question every investor asks of every business, public or private. In the 2010s, Slack & Atlassian became titans. On the day Salesforce announced its intent to acquire Slack, it was equally valuable to Atlassian at ~$27b.

OpenAI hit $12 billion ARR within five years of ChatGPT’s launch. Anthropic reached $200 million in revenue in January 2024. Meanwhile, Salesforce took ten years to reach $1 billion ARR.

You call the airline to change your delayed flight. The agent says he can’t help—the computer won’t let him. You ask to speak to his manager. She says the same thing...

We build teams in pyramids today. One leader, several managers, many individual contributors. In the AI world, what team configuration makes the most sense? Here are some alternatives :

One trillion tokens per day. Is that a lot? “And when we look narrowly at just the number of tokens served by Foundry APIs, we processed over 100t tokens this quarter, up 5x year over year, including a record 50t tokens last month alone.”

Now that we’ve compressed nearly all human knowledge into large language models, the next frontier is tool calling. Chaining together different AI tools enables automation. The shift from thinking to doing represents the real breakthrough in AI utility.

Like breakfast at a diner, software is cheap & fast to make. Ask for a new task management tool & you’ll have the first version in less time & for less money than an omelette.

Understanding technology is at the core of how we research new theories. As part of that effort, Theory is starting a developer relations team. Developer relations for venture capital? What does that mean?

When Anthropic introduced the Model Context Protocol, they promised to simplify using agents. MCP enables an AI to understand which tools rest at its disposal : web search, file editing, & email drafting for example.

$4b in revenue run-rate. There are two data giants both now at this mark after Databricks announced it surpassed the threshold. This is an opportunity to compare the two leading data companies at a revenue intersection.

At Theory, we create theories about the future & help them become reality. A significant part of our work is research, understanding spaces & their evolution. Internally, we’ve been working with living market maps since our foundation.

Another day, another earnings report with accelerating growth from AI. Snowflake earnings yesterday demonstrates yet again the impact of AI on a $4.4B revenue business. Snowflake’s revenue has rebounded from a low of 26% to 32% quarter over quarter.

AI vendor revenue will double classic software in terms of new bookings this year. This trend is so large it’s starting to have second-order effects.

Netflix invented a new role for their data team : the Media ML Data Engineer. Unstructured data is fundamentally different. It’s multimodal & contains derived fields like embeddings, captions, & transcriptions. It’s also at least 80% of the world’s data & essential for the field of AI.

The familiar rhythms of work are shifting beneath our feet. For the past fifteen years, we’ve lived in a strange plateau, standardizing workflows & settling into predictable patterns. The tools remained largely the same, the processes became routine, & many of us found comfort in that stability.

I set out to write a very different post : one with the thesis that venture capital holding periods were lengthening relative to private equity. But the data violates the narrative!

On September 4 at 5-9pm PDT in Berkeley, Hamel Husain will be leading a conversation featuring Claire Vo, Greg Ceccarelli, and me talking about how to achieve Mihaly Csikszentmihalyi’s flow state with AI.

Since launching EvoBlog internally, I’ve wanted to improve it. One way of doing this is having an LLM judge the best posts rather than a static scoring system. I appointed Gemini 2.5 to be that judge. This post is a result.

AI coding assistants like Cursor and Replit have rewritten the rules of software distribution almost overnight. But how do companies like these manage margins? Power users looking to manage as many agents as possible may find themselves at odds with their coding agent providers.

Groupon grew, in just a year & a half, from a staff of a few hundred to 350. Revenue & bookings also grew. It grew swiftly, & the company was valued at over a billion after just 16 months in business. It was the fastest company to ever reach this milestone. —Wikipedia (en.wikipedia.org/wiki/Groupon)

One of the hardest mental models to break is how disposable AI generated content is. When asking me to generate one blog post, why not just ask it to generate three, pick the best, use that as a prompt to generate three more, and repeat until you have a polished piece of content?

GPT-5 achieves 94.6% accuracy on AIME 2025, suggesting near-human mathematical reasoning. Yet ask it to query your database, and success rates plummet to the teens.

Perplexity AI just made a $34.5b unsolicited offer for Google’s Chrome browser, attempting to capitalize on the pending antitrust ruling that could force Google to divest its browser business.

In 1999, the dotcoms were valued on traffic. IPO metrics revolved around eyeballs. Then Google launched AdWords, an ad model predicated on clicks, & built a $273b business in 2024.

GPT-5 launched yesterday. 94.6% on AIME 2025. 74.9% on SWE-bench. As we approach the upper bounds of these benchmarks, they die.

2025 is the year of agents, & the key capability of agents is calling tools. When using Claude Code, I can tell the AI to sift through a newsletter, find all the links to startups, verify they exist in our CRM, with a single command. This might involve two or three different tools being called.

What force could dethrone AWS after more than a decade of unchallenged dominance? For years, Amazon Web Services ruled the cloud infrastructure market. It was the default choice without a question for every startup.

Why is the sub-$5 million seed round shrinking? A decade ago, these smaller rounds formed the backbone of startup financing, comprising over 70% of all seed deals. Today, PitchBook data reveals that figure has plummeted to less than half.

The internet is about to look a whole lot more like the online advertising world. No, I don’t mean there’ll be more ads. In fact, I think there’ll be far fewer.

OpenAI receives on average 1 query per American per day. Google receives about 4 queries per American per day. Since then 50% of Google search queries have AI Overviews, this means at least 60% of US searches are now AI.

In working with AI, I’m stopping before typing anything into the box to ask myself a question : what do I expect from the AI? 2x2 to the rescue! Which box am I in? On one axis, how much context I provide : not very much to quite a bit. On the other, whether I should watch the AI or let it run.

What happens when technology evolves faster than your sales process can adapt?

That little black box in the middle is machine learning code. I remember reading Google’s 2015 Hidden Technical Debt in ML paper & thinking how little of a machine learning application was actual machine learning. The vast majority was infrastructure, data management, & operational complexity.

If 2025 is the year of agents, then 2026 will surely belong to agent managers. Agent managers are people who can manage teams of AI agents. How many can one person successfully manage?

For the last decade, the biggest line item in any startup’s R&D budget was predictable talent. But AI is pushing its way onto the P&L. How much should a startup spend on AI as a percentage of its research and development spend? 10%? 30%? 60?

When you query AI, it gathers relevant information to answer you. But, how much information does the model need? Conversations with practitioners revealed the their intuition : the input was ~20x larger than the output.

If I have a dollar to invest in a stock or a crypto token, how do I decide? I need to compare across the two. Historically, that comparison was impossible. Crypto traded on a potent cocktail of hype, narrative, & the promise of a decentralized future. Perception drove valuations.

Yesterday, Figma filed its beautifully designed S-1. It reveals a product-led growth (PLG) business with a remarkable trajectory. Figma’s collaborative design tool platform disrupted the design market long-dominated by Adobe.

Gmail’s AI email assistant writes like a committee of lawyers designed it.

In “Data Rules Everything Around Me,” Matt Slotnick wrote about the difference between SaaS & AI apps. A typical SaaS app has a workflow layer, a middleware/connectivity layer, & a data layer/database. So does an AI app.

Remember when you took a family photo & Ghibli-styled it? Or that vibe coding session, when you pasted a screenshot of the browser so the AI can help you debug some Javascript?

Systems of record are recognizing they cannot “take their survival for granted.” One strategy is to acquire : the rationale Salesforce gives for the Informatica acquisition.

DuckLake is one of the most exciting technologies in data. While data lakes are powerful, the formats that manage them have become notoriously difficult to work with.

Databricks seems to be closing the gap on Snowflake faster than expected. Last week Databricks shared some important updates on their business which allows us to compare the progress of the two companies.

The Seed Surge of 2021 will lead to a raft of acquihires. In 2021 the total number of US software & AI seeds jumped from 2900 to 4300 - a 49% jump. Seeds fell to about 3000 creating a seed tabletop.

Doctors and security research have more in common than you might think. Doctors defend human bodies against an ever-shifting landscape of viruses & infections. Security researchers do the same thing, but at massive scale—protecting thousands of servers instead of a single patient.

Where venture capital flows, innovation follows. And for more than a decade, few faucets have been watched more closely than Y Combinator...