AI breaks the data stack. Most enterprises spent the past decade building sophisticated data stacks. ETL pipelines move data into warehouses. Transformation layers clean data for analytics. BI tools surface insights to users. This architecture worked for traditional analytics. But AI demands something different. It needs continuous feedback loops. It requires real-time embeddings & context retrieval. Consider a customer at an ATM withdrawing pocket money. The AI agent on their mobile app need...| Tomasz Tunguz
I discovered I was designing my AI tools backwards. Here’s an example. This was my newsletter processing chain : reading emails, calling a newsletter processor, extracting companies, & then adding them to the CRM. This involved four different steps, costing $3.69 for every thousand newsletters processed. Before: Newsletter Processing Chain # Step 1: Find newsletters (separate tool)ruby read_email.rb --from "newsletter@techcrunch.com" --limit 5# Output: 340 tokens of detailed email data# Ste...| Tomasz Tunguz
“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. McPhee pioneered creative nonfiction at The New Yorker, writing books like Oranges & Coming into the Country that made complex subjects fascinating through storytelling. His approach differs from traditional journalism by incorporati...| Tomasz Tunguz
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? As we all start to use AI, prompt optimization will be a consistent challenge. GEPA, GEnerative PAreto, is a technique to discover the equivalent efficient frontier for AI. Reading the paper, I noticed the initial results were promising, with a 10-point improvement on certain benchmarks & a 9.2 times shorter p...| Tomasz Tunguz
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. The revenue curves look similar in the out years, similar growth rates. Atlassian continues to compound at massive scale. But the time to achieve $1b from founding date differs by a decade : 17 vs 7 years. To create ...| Tomasz Tunguz
OpenAI hit $12 billion ARR within five years of ChatGPT’s launch [1] . Anthropic reached $200 million in revenue in January 2024 [2] . Meanwhile, Salesforce took ten years to reach $1 billion ARR [3] . Does this mean the T3D2 framework (triple-triple-triple-double-double ARR to go public), originally outlined by Neeraj Agrawal, which provides a clear path to IPO-scale revenue is dead? There’s no doubt that AI companies have grown at unprecedented rates. If we understand these fundamental ...| Tomasz Tunguz
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 : First, the short pyramid. Managers become agent managers. The work performed by individual contributors of yore becomes the workloads of agents. Everyone moves up a level of abstraction in work. This configuration reduces headcount by 85% (1:7:49 -> 1:7). The manager to individual contributor ratio goes from 1...| Tomasz Tunguz
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.” In April, Microsoft shared a statistic, revealing their Foundry product is processing about 1.7t tokens per month. Yesterday, Vipul shared Together.ai is processing 2t of open-source inference daily. In July, Google announced a staggering number : “At ...| Tomasz Tunguz
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. I’ve built more than 100 tools for myself, & they work most of the time, but not all the time. I’m not alone. Anthropic’s Economic Index report reveals that 77% of business use of Claude centers on full-task automation, not co-piloting...| Tomasz Tunguz