In 1908, 253 American automobile manufacturers competed for the market1. By 1929, just 44 remained. The assembly line didn’t just change how cars were made. It changed who got to make them.
Ford’s Highland Park plant, operational in 1913, slashed the time to build a Model T from 12 hours to 93 minutes2. That 90% productivity gain restructured an entire industry. Manufacturers who couldn’t match Ford’s efficiency faced a simple choice : adapt or exit.
Talented people get promoted to management. So do talented models. Claude manages code execution. Gemini routes requests across CRM & chat. GPT-5 can coordinate public stock research.
Why now? Tool calling accuracy crossed a threshold. Two years ago, GPT-4 succeeded on fewer than 50% of function-calling tasks. Models hallucinated parameters, called wrong endpoints, forgot context mid-conversation. Today, SOTA models exceed 90% accuracy on function-calling benchmarks1. Performance of the most recent models, like Gemini 3, is materially better in practice than the benchmarks suggest.
Will designers design first in a world where AI can code software immediately, or just describe the design? Will large enterprises pay for premium observability when AI can migrate & monitor open source competitors?
As Michael Mauboussin writes, there’s information in price. These questions are priced in. It’s too early to see revenue erosion, but the market is pricing in the risk.
The median SaaS stock is down 14-17% year to date. 64% of software companies are down. Adobe has fallen 32%, HubSpot 57%, Atlassian 54%.
After a decade of success, the modern data stack has entered consolidation. What comes next?
The postmodern data stack is AI.
The modern data stack created more than $100 billion in market cap with a simple promise : move the data via ETL, transform it inside a cloud data warehouse, put a semantic layer on top of it to unify metric definitions, & analyze it through BI.
This worked brilliantly for structured data, numerical data found within tables. But AI & businesses thrive on unstructured data, such as call transcripts, status reports, web searches, & PDFs.
This week I chatted with an acquaintance who mentioned a board game. I caught half the title & looked for the full title & Amazon link using my AI in Asana.
The system tried with Gemini & failed. The failover to Claude also failed. Rather than continuously iterating with the AI until it worked, I created a Ralph Wiggum loop.
AI is NVIDIA’s third climb up a steep slope.
First came gaming in the late 2010s.
Then cryptocurrency.
Now artificial intelligence.
Each wave pushed revenue growth above 50% & with it, P/E1 ratios surged. P/E ratios rose before the revenue growth materialized.
There’s a four-quarter offset between P/E ratio & TTM2 revenue growth. When you shift revenue growth forward by a year, the correlation with P/E jumps to 0.80.
“[Silicon Valley] is the biggest, most volatile petri dish of raw capitalism on the planet.” So what lessons does a successful short-seller living here have? As founder of Crown Capital Management, Scott Fearon has shorted the stocks of over 200 companies.
In his book Dead Companies Walking, Fearon distills three decades of meetings with executives into six failure modes :
Despite their differences, they all failed because their leaders made one or more of six common mistakes that I look for :
I asked Claude Cowork to read my tools folder. Eleven steps later, it understood how I work.
Over the past year, I built a personal operating system inside Claude Code : scripts to send email, update our CRM, research startups, draft replies. Dozens of small tools wired together. All of it lived in a folder on my laptop, accessible only through the terminal.
Salesforce, Datadog & Epic are building walls. After two decades of flourishing through open APIs & data portability, the software industry’s largest incumbents are locking down.
Salesforce restricted Slack’s API1 in May 2025, limiting third-party apps to one API call per minute & fifteen messages per request. Datadog deactivated accounts2 for Deductive AI, a competing observability startup. Epic faces a Texas lawsuit3 accusing it of turning patient records into a “gatekeeping tool.”
In 2012, we cared that we used software. Today we care how we use it.
The difference is trajectory.
In the last decade, adopting software was the priority. Moving from on-premise to the cloud or digitizing a manual workflow promised productivity gains. Adoption was the finish line.
Today software is ubiquitous. Every salesperson uses a CRM & every engineer uses an IDE. The edge no longer comes from having the tool but from the specific path & manner in which that tool is used to achieve an outcome : a trajectory through software.