I sleep better knowing my agents work through the night. Less work for me in the morning.
My podcast processor transcribes & analyzes conversations. I started on my laptop, needed a little database to collect podcast data & metadata, & booted up a DuckDB instance.
But then the data started to grow, & I wanted the podcast processor to run by itself. I changed two little letters, & the database moved to the cloud :
Microsoft & Google both announced earnings yesterday, & the scale of AI adoption remains staggering. The infrastructure businesses are growing at accelerating growth rates that are the envy of businesses one-hundredth the size.
Capital expenditures were $34.9 billion, driven by growing demand for our Cloud and AI offerings. This quarter, roughly half of our spend was on short-lived assets, primarily GPUs and CPUs…. We will increase our total AI capacity by over 80% this year and roughly double our total data center footprint over the next 2 years, reflecting the demand signals we see. Just this quarter, we announced the world’s most powerful AI data center, Fairwater in Wisconsin, which will go online next year and scale to 2 gigawatts alone. - Microsoft
As the world follows every whisper & rumor from AI, what has happened to public SaaS?
The answer is not much! All the fun is in the private markets.
Multiples haven’t moved outside of a narrow band since the post-Covid crash in 2022.
Even here, AI is the story, with AI SaaS companies seeing different valuation correlates : revenue growth. Meanwhile, non-AI SaaS companies are valued more broadly with efficiency metrics included.
There’s this notion that product market fit is this binary condition that once you pass the gate, you’re done. And that’s no longer the case. Product market fit is like United Miles. You continue to fly to maintain 1K. And we saw it between 2021 and 2024, companies that were classic software companies literally overnight lost their product market fit. And that’ll be very much the case.
AI is changing what the market wants & what’s possible so quickly that PMF is no longer static.
AI is changing the way we work. I can’t imagine going back to the way I worked with a computer just a year ago, so powerful is the leverage from AI.
Calvin French-Owen, former co-Founder & CTO of Segment, helped lead the launch of OpenAI’s Codex, one of the most ambitious developer tools of the modern era.
At this Office Hours, we’ll dive into three key topics shaping the next generation of builders :
When I worked at Google, I was lucky to collaborate with some of the brightest machine-learning (ML) engineers. They worked on feature engineering. By picking the factors to guide the ML model, their advances could generate tens to hundreds of millions of additional revenue.
Imagine an Excel spreadsheet with hundreds of columns of data. Add two columns, multiply two, divide by another, and subtract a fourth. Each of these is a feature. ML models used features to predict the best ad to show.
It’s 9:30 AM. Do you know where your agent is?
As we enter the era of agentic AI, this is an increasingly important question. ChatGPT launched the consumer use of AI, compressing all of human knowledge into a single model. Now we are asking AI to act on our behalf at work.
Analyze this mortgage statement. Figure out if this person has medical insurance. Answer this inbound call about a 2026 Honda Odyssey minivan. AI agents are doing all of this today. Plus, AI is coding millions of lines every day.
I asked Claude this morning about the most important news in tech. A few follow-up questions about Salesforce’s Dreamforce, Samsung’s XR headset, & TSMC earnings later, I popped over to ChatGPT Pulse. It suggested some updates about the Fed’s rates policy. Then, I listened to podcasts through Gemini-powered AI transcriptions.
Why was the Fivetran-dbt merger all but inevitable?
Fivetran & dbt Labs announced their merger yesterday. The all-stock deal combines two companies into an entity approaching $600 million in ARR.
The beauty of the modern data stack was the explosion in choice. As the cloud exploded onto the scene, the legacy data warehouse was replaced by a collection of fast-moving platforms. In that era, specialization won. The pendulum is now swinging back towards consolidation.
Philip Schmid dropped an astounding figure1 yesterday about Google’s AI scale : 1,300 trillion tokens per month (1.3 quadrillion - first time I’ve ever used that unit!).
Now that we have three data points on Google’s token processing, we can chart the progress.
In May, Google announced at I/O2 they were processing 480 trillion monthly tokens across their surfaces. Two months later in July, they announced3 that number had doubled to 980 trillion. Now, it’s up to 1300 trillion.