A solo founder uses Claude to solve a DNS configuration problem in five minutes, a task he would have outsourced two weeks ago. On a different continent, a Fortune 500 company builds a parallel digital twin of its invoice department using the same class of tools. These are not separate trends. AI agents are collapsing the distinction between startup agility and enterprise scale, making internal coordination more expensive than external execution.
Salim Ismail argues on Moonshots that Ronald Coase’s seminal theory of the firm is obsolete. The cost of holding a meeting to discuss a software feature now exceeds the cost of having an agent build it. This economic flip is why companies must stop trying to retrofit AI and instead build AI-native entities at their edge, migrating workflows one by one. Ismail estimates a successful transition yields 100x annual performance gains and lets companies eventually operate with a quarter of their current staff.
The primary casualty is middle management. Their core function - packaging information for leadership - is now a native AI capability. The C-suite’s role is shifting to oversight and exception handling, evaluating agent outputs rather than generating strategy themselves.
"Middle management will shrink by 60% as coordination tasks vanish, while the C-suite shifts to dashboard oversight and exception handling."
- Salim Ismail, Moonshots with Peter Diamandis
This isn't just about efficiency; it's about a new infrastructure layer. Nathaniel Whittemore notes on The AI Daily Brief that the industry has moved into a 'harness engineering' phase. A model’s performance is now dictated more by its runtime environment than its underlying weights. Data from Endor Labs shows GPT-5.5’s functionality score on a coding benchmark jumped from 61% to 87% when moved from its native harness into Cursor’s SDK.
These managed runtimes, or 'Harness as a Service,' abstract away the complexity of persistent memory and tool protocols. They allow non-developers to build agentic apps, which is accelerating adoption in mainstream industries. Jake Woodhouse cites a Melbourne construction company using Claude to analyze material costs and project timelines, collapsing days of manual work into minutes.
For developers, the landscape is polarizing. Milan, co-founder of NanoGPT, notes the market is splitting. Open-source models win on price and lack of censorship for automated tasks, but for high-stakes work like complex programming or medical advice, proprietary models like Claude 4.7 - used five times more than any other model on his platform - maintain a three-to-six-month intelligence lead.
The final barrier is organizational immune response. Ismail points out that 44% of Gen Z workers have sabotaged AI training to protect their jobs. The transition is less a technical challenge and more a cultural siege against a legacy system designed for human bottlenecks.
"We are moving past the era of 'hobbyist' agent building. Nathaniel Whittemore argues we have entered the age of 'Harness as a Service' (HaaS)."
- The AI Daily Brief: Artificial Intelligence News and Analysis
The wave is monetizing. Google Cloud revenue grew 63% year-over-year, with a $460 billion backlog, while Amazon is pouring capital into infrastructure spoken for by customer demand. The proof is shifting from speculative bubbles to booked revenue, funded by companies betting their survival on moving faster than their own meeting schedules.





