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As traffic to traditional websites plummets due to AI answering user queries directly, there is a new explosive form of distribution.

“AI agents are now creating Neon databases at 4x the rate of human developers, driving new requirements for instant provisioning, automatic scaling, & isolated environments.”

If I ask an AI agent to create a web application, I want it to select all the components : the front end framework, the database, & the hosting service. I just want the website to work.

Election night struck the regulatory asterisk from web3. But it did more than that.

It triggered a broader shift of application investing versus infrastructure.

The last few years of crypto & Web3 investing have focused predominantly on infrastructure, the databases (called Layer 1s/L1s & Layer 2s/L2s), security, analytics, & decentralized finance or DeFi (typically lending products). But these categories are slowing.

“We processed over 100t tokens this quarter, up 5x year over year, including a record 50t tokens last month alone.”

If the market harbored any doubt for the insatiable demand for AI, this statement during Microsoft’s quarterly earnings yesterday, quashed it.

What could this mean for a run rate? Using some basic assumptions¹, this implies :

So AI is roughly between 2 to 22% of Azure revenue. Error bars here are quite large, though.

By 2026, AI agents will consume 10x more enterprise data than humans, but with none of the contextual understanding that prevents catastrophic misinterpretations.

In this presentation I shared yesterday, this is the main argument.

Historically, our data pipelines have served people. We’ve architected complex pipelines to ingest, filter, and transform information in different systems of record: cloud data warehouses, security information and event management systems (SIEMs), and observability platforms.

We then interpreted these outputs and acted upon them.

If every employee starts managing agents, how does a company change?

First, “83% of global leaders say AI will let employees take on more complex, strategic work earlier in their careers.”

One executive recently framed this transition of teams evolving to three areas of work : operational, tactical, & strategic. Operational work can be mostly fully automated today. Agents are chomping away at tactical work - better accuracy will improve their share. Humans will focus on strategy work, but likely assisted by AI.

The ServiceNow team is raising the bar by innovating on our own platform, taking an end-to-end agentic AI-first approach to running our business. We see this in a 16x improvement from lead-to-sale conversion & an over 86% deflection of the soul-crushing work people used to do themselves.

ServiceNow reported earnings yesterday alongside Google & many other companies. All the insights out of those reports were interesting, but this was the one that stood out the most to me.

When you buy a stock or a bond, do you know which database that transaction is running on?

I’m sure the answer is no, but the database will change & that has far-reaching implications.

data via Allium

Blackrock’s tokenized Treasury fund, BUIDL, has amassed $2b of treasuries so far, with a recent significant surge. This fund is still a bit smaller than BlackRock’s classic Treasury Fund BYTXX at $21b.

Two years ago I published an image of a rabbit on a fire truck to demonstrate how effective image generation was for blog posts.

The conclusion at the time : the images were unusable.

In 2025, the opposite is true - with one caveat.

Prompt: Coupa & Thoma Bravo logos each in front of a cloud with a plus sign between them.

2023	2025

Two years has brought tremendous professionalism to new image. Corporate street art is now pixel-perfect professional. The clouds and the + sign are perfect, and the logos look the part.

Software systems work best when they’re connected to each other. For years, incumbents use deep integrations as a competitive moat.

But AI upends this dynamic.

A few of our portfolio companies are starting to develop integrations with AI in a matter of hours, completely upending the two or three quarter timeframes of classic enterprise integration development.

This enables two important impacts to the sales cycle.

First, the integration a customer desires can be built during the sales cycle, demonstrating the startup’s technical agility.

I want to see how you use AI.

The biggest challenge to AI adoption is reimagining workflows, AI is best discovered socially.

Midjourney’s launch on Discord was brilliant. As a new user, I knew enough to ask for an image of a rabbit on a firetruck. But by watching others, I discovered I could create vector images, recast photos into Ghibli-style art, change aspect ratios, or convert photos to pencil-shaded drawings.