It’s not often that venture capital thinks about the credit markets. I remember becoming a venture capitalist three months before Lehman fell, when mortgage securities topped the news of the global financial crisis.
Recently Oracle’s bonds have been weighing on my mind.
AI’s capital expenditure in 2025 represents about 1.6% of US GDP. In 2026, that number should top 3% of US GDP according to Goldman Sachs estimates1.
No, founders are not adopting Bryan Johnson’s regimen to reverse aging. Quite the opposite : the average founder raising capital ages six months every year.1
I suspect founder age has been increasing steadily for three reasons. First, venture capital has shifted toward AI, which grew from roughly 10% to 60% of investment in just three years.2 AI founders skew older. Many AI labs are started by PhDs who spent extended periods in school & often come from industry, commercializing initiatives from major labs or hyperscalers.
Enterprises learned a lesson from cloud data warehouses. They handed over both data & compute, then watched as the most strategic asset in their business, how they operate, became someone else’s leverage, which created an opportunity for Iceberg.
Fool me once…
Leaders have recognized their companies need a new system of record for AI agents in the form of a context database. There are two different kinds of these context databases :
A new study from OpenAI shows AI saves the average white-collar worker 54 minutes per day. Where does all that value go?
BLS data shows median weekly earnings for full-time workers hit $1,165 in Q3 2024, or $60,580 annually. Across 2,080 working hours, hourly compensation equals $29.13. One hour saved per day equals 250 hours per year, or $7,282 in recovered productivity per seat.
Current AI pricing captures between 3% & 5% of this value :
Streaming is the next category to consolidate within the modern data stack. IBM announced its intent to acquire Confluent.
The deal values Confluent at $11.1 billion, or 10.0x LTM revenue. Confluent commands more than 40% of the Fortune 500 as customers & has grown into a $1.1 billion revenue business.
The founders of Confluent, including CEO Jay Kreps, created Apache Kafka, a streaming technology built inside LinkedIn. Founded in 2014, Apache Kafka now runs at more than 80% of the Fortune 100. Kafka powers real-time data pipelines & stream processing, updating data systems whenever a new event happens. When a taxi ride is booked, a credit card is swiped, or a user likes a comment, Kafka handles the data flow.
Since I watched software engineers using AI, I’ve become jealous.
I’ve seen the most sophisticated software engineers assign 15 to 20 coding tasks in GitHub to an AI. They play foosball. They grab coffee. They return to evaluate the agent’s work.
The agent tackles the same task three different ways. Sometimes it nails the solution on the first try. Other times it needs more input. That engineer has paralleled her time by 10x to 15x.
While OpenAI signed $1.15 trillion in compute contracts through 2035, DeepSeek trained a frontier model for $6 million. This was 2025’s central question : are we building on bedrock or quicksand?
The top 10 posts of 2025 examined some of these topics :
I remember hosting a dinner of sales leaders to talk about AI. I asked them what will your CRM do for you in the future?
Nearly unanimously came the reply : enable salespeople to spend most of their time with customers.
Listening to Lenny’s Podcast with Jeanne Grosser, COO at Vercel, I discovered how some companies are achieving that milestone by transforming their inbound sales teams in six weeks.
For us, we had 10 SDRs doing this inbound workflow, & now we just have one that is effectively QAing the agent. The other nine, we deployed on outbound.
Private equity firms have emerged as the newest distribution channel for AI startups.
While public companies have decreased from 6,639 in 2000 to 3,550 in 2024, PE-owned companies in the US have grown from 1,950 to 14,300. The rate of growth continues to accelerate.
The AI market today is bacon in a hot skillet. Everything is sizzling, moving, & changing at an incredible pace. We’re all watching it closely.
Market share is fluid because no one yet knows what AI can do & the second we think have grasped it, models improve. The Nvidia chip performance & the launch of Gemini 3 the biggest gain ever in Google model performance suggest no simmering ahead.
As long as the underlying models hurtle towards PhD level performance, people will continue to test. How much better is Gemini 3 at coding? tool calling? writing?