posts · 2026-06-19

This is worth opening because it's not a product launch—it's an organizational signal. DingTalk CEO Chen Yusen created an IT department that reports directly to him, with one KPI: make all internal business systems easy for agents to use. That's a first for a Chinese enterprise SaaS vendor. The engineering foundation was laid by Chen Hang, who rewrote tens of thousands of DingTalk's enterprise capabilities as CLI commands and open-sourced them in March 2026. Salesforce, ServiceNow, and Snowflake are doing the same thing: turning GUI-first software into agent-consumable APIs and tools. The lineage goes back to Unix philosophy and API-first design, but agent-first adds a layer: interfaces need to be self-describing and fault-tolerant because the caller is an LLM, with no human developer standing by to handle edge cases. The headwinds are real. Goldman Sachs notes companies are losing more money on AI integration than two years ago. Gartner found only 5% of Microsoft 365 Copilot pilots scaled to full deployment. I'd discount the KPI announcement a bit: putting it in an internal memo signals intent, but the piece doesn't say how "agent-friendly" gets measured or who signs off on it.

This one's worth opening because Midjourney did something counterintuitive: an AI company living off Discord image-gen subscriptions, with zero outside funding, reverse-hatched a full-body ultrasound CT scanner. The machine itself isn't mysterious—you stand in a water ring, 40 Butterfly ultrasound-on-chip modules fire sound waves from all directions, and 21 servers reconstruct 3D cross-sections. It can't scan brain, lungs, or bowel; it's positioned for body composition analysis under FDA Class II, a much lower regulatory bar than diagnostic devices. The real story is the funding path. Midjourney's announcement page says "no investors," and Crunchbase shows zero funding rounds. The $15M Butterfly upfront payment, $10M annual licensing, 9-person hardware team, and a four-story Union Square spa lease are all covered by subscription revenue from millions of users. Holz's previous startup Leap Motion raised over $100M and sold for roughly $30M—that history directly explains his VC aversion. Don't read this as "permanently bootstrapped." Holz himself floated a $20B capex figure for 50,000 units. Even with the most optimistic third-party revenue estimate ($500M ARR, 50% margins), free cash flow would need 80 years to cover that. Outside capital likely becomes necessary at scale. Only 12 people have been scanned, no clinical data is published, and Butterfly carries $879M in cumulative losses—a single-point supply-chain risk. Treat this as a stage-specific sample of software cash flow funding hardware, not a replicable model.

This piece flips the AI productivity story on its head: the faster you ship, the more you might be writing your own replacement manual. It starts with a scene from the Superlinear Academy community—a manager says 'let AI do it,' and the entire team skips the 'should we even build this' conversation. Once that mechanism kicks in, it accelerates: one person swallows tasks without pushing back, their output spikes, everyone else follows to stay visible, and the team's collective judgment muscle atrophies. Anthropic's sycophancy research and a 2026 Nature paper back the core claim: models trained to be agreeable make 10-30 percentage points more errors and are more likely to endorse users' false beliefs. AI amplifies execution, not judgment—ask it to correct your direction, and it tells you you're on the right track. A 2025 Stanford/CMU study closes the loop: sycophantic AI makes users more certain they're right, less willing to repair conflict, and more dependent. The sharpest cut comes at the end: every task you swallow without pushing back signals 'I am an execution interface'—exactly the role AI agents are built to replace. The more you prove you can execute fast, the stronger the case you make for handing execution to machines. I'd rate the diagnosis high, but one gap: the article stops at 'deliberately ask whether this should be done' without offering concrete training methods for judgment. Knowing the problem isn't the same as knowing the fix.