If you are building a product where AI inference flows directly into your cost of goods sold, you have a pricing risk that most founders are not accounting for correctly. It is not a theoretical risk. It is happening right now, and the signals are loud enough that ignoring them
Something structural happened to software in early 2026. In a span of weeks, markets wiped $285 billion from SaaS company valuations. This was not a sentiment correction or a macro reaction. It was a judgment call: investors concluded that AI agents could replace entire categories of knowledge work that SaaS
I'm going to say something that will irritate a few vendors in my LinkedIn feed: most of what is being sold as 'agentic AI' right now is not agentic AI. It is automation with a new coat of paint, a friendly chat interface, and a press
On June 26, 2026, OpenAI launched GPT-5.6, a family of models it named Sol, Terra, and Luna. It did not launch them to the public. It launched them to a small group of, in its own words, 'trusted partners whose participation has been shared with the government.
Sometime in early 2026, someone at a large tech company decided the right way to measure AI adoption was to count tokens. Not outcomes. Not cost savings. Not shipping velocity. Tokens. Raw, billable tokens. And for a while, it worked as a cultural signal. Employees competed on internal leaderboards. Managers
On June 22, 2026, OpenAI launched Daybreak, an AI cybersecurity initiative that combines GPT-5.5-Cyber and Codex Security into a unified product for vulnerability detection and end-to-end patch automation. The target customer is the enterprise. The competitors being displaced are Microsoft and CrowdStrike. This is not
There's a moment from WWDC 2003 that's been sitting in the back of my mind lately. Steve Jobs was on stage demoing Xcode, and he stopped to talk about something he called the Turnaround Loop. The idea was simple: developers spend most of their time cycling
I've spent a lot of time building and shipping AI agents into production environments. Not prototypes. Not demos. Actual workflows that touch real data, real customers, and real business outcomes. So when I see another breathless post on X about a "fully autonomous AI agent" that
There is a pattern in technology that repeats so reliably it has become almost boring to point out. The closed, proprietary incumbent builds a moat, charges for access, and attracts enormous capital. The open alternative emerges, gets dismissed as inferior, and then quietly takes over the infrastructure layer. Every time.
On June 12, 2026, Anthropic launched Fable 5 and Mythos 5. Three days later, the U.S. Commerce Department issued an export control order and Anthropic had to kill them. For every customer. Globally. Immediately. Anthropic's own words: "the net effect of this order is that we
Last month I talked to three different founders who all had the same look on their face when I asked about their AI spend. That thousand-yard stare you get when you've seen something you can't unsee. One of them was burning $11,000 a month
Something happened this quarter that a lot of people in AI missed, or at least didn't want to say out loud. Kimi K2.7, built by a Chinese startup called Moonshot AI, beat Claude Opus 4.8 on MCP tool use and agentic coding benchmarks. Not close. Beat