A laptop on my desk now handles 78% of my AI work, with the rest sent to the cloud. The shift came out of my skill distillation work.
Here’s how it works.
I create tasks in Asana. An agent sees the task : scheduling, email triage, research, a CRM update ; & classifies it as easy or hard. If it’s straightforward, a local model on my Mac handles it in seconds. If it’s complex, the same model routes it to a cloud model.
Yesterday Microsoft added a new metric to a model release card, one that will likely become a standard.1
Average token usage.
In the first row, the Microsoft model hits 71.6 on SWE-Bench Verified using about a third of the tokens Claude Haiku 4.5 burns.
Benchmarks are now measured on two different dimensions, the overall performance & the cost to achieve that intelligence.
Competition is a discovery procedure. — Friedrich Hayek
And developers are discovering the value of open models.
OpenRouter offers a useful view into the model market.1 It is not the whole AI economy. But it is close to the API frontier, where developers can switch models quickly, compare price-performance daily, & route each request to the best available option.
Since 2025, open models have grown sharply on OpenRouter. In the latest model-level snapshot, open-weight models generated 69.1% of named open-versus-closed token volume. Closed models produced 30.9%.
With Michael Burry 1 & Leopold Aschenbrenner 2 placing heavy short trades on AI, questions about GPU depreciation, & the Saaspocalypse, how negative is the financial market on AI?
We can look at the percentage of shares sold short, a bet the stock will decline.
Across all software, semiconductor, neocloud, data center, & hyperscalers, the median short interest (short shares / total shares) has increased by about 24% in the last quarter.
I’ve been using state-of-the-art models to teach small models running on my computer how I work.
My personal agent, based on Pi, runs my inbox, my deal pipeline, my blog publishing, my calendar, & my research. It looks less like a chatbot & more like a small operating system.
The first layer is QMD, a local markdown knowledge base of about eighty workflow files in ~/memories. Before answering any procedural question, the agent searches QMD for the right playbook.
When security practitioners become engineers, the mission changes from managing people to architecting the automated policies that govern an agentic world.
Jonathan Jaffe, CISO at Lemonade, joined me on Office Hours to discuss what this means for how we build, secure, & operate AI systems when both sides are automated.
The end of the software era is the beginning of the harness era.
AI outmoded SaaS managed databases with fixed workflows with intelligence. Like a mustang, AI is powerful but wild. Harnessing the power means domestication.
There are seven parts to this domestication :
If data gravity was the most important force in the Decade of Data, agent gravity will be the same in the Decade of Agents.
Agents are wonderfully powerful technologies & they require tremendous compute to power. That compute is big business & major platforms will fight to keep them on their platforms. The more agents & data running through a platform, the greater the agent gravity.
The most recent episode with a new Databricks feature on Microsoft’s platform :
Salesforce has gone headless : a sales person can update their deal sheet without ever logging into salesforce.com through AI. Many companies are following suit with MCPs. English as an interface to complex systems is a tremendous innovation.
And yet, some of the most sophisticated thinkers in AI are pushing more than markdown text, a format AI & computer systems use. These thinkers espouse richer UIs :
“Imagine using iMessage to do everything, when in fact every other app has a unique interface…With e-commerce, you want a very rich user interface.” - Brian Chesky, CEO of AirBNB
