Ruthanne Huising studied teams mapping their own company's real workflows. They found processes whose outputs nobody used and effort duplicated across groups. Walked through the map, the CEO admitted his grasp on his own operation had been imaginary.
This is why rule-based automation stalls in companies: it needs documented processes, and the pathways real organizations run on were negotiated between people rather than designed and written down. 43% of US workers use AI already, but informally, on their own tasks.
The two AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → philosophies side by side: Manus is hand-built, hundreds of lines of system promptThe standing instructions a model receives before any user input — defining its role, rules, tools, and tone for the whole conversation.Full definition →, always opening by writing itself a to-do list. ChatGPT agent was trained by reinforcement learning on the quality of finished outputs and follows no visible script.
Same prompt, different rigor. Asked for an unrecorded number, Deep Blue's chess rating, the hand-crafted agent settled for a speculative Reddit thread while the outcome-trained one found published sources. Asked for a working Excel file, only the latter's opened cleanly.
The asymmetry that decides the race: improving a hand-crafted agent takes more human crafting, improving an outcome-trained one takes more chips and more examples. One scales with scarce attentionThe mechanism that lets a model weigh which earlier words matter for the word it's currently processing — the core operation of a transformer.Full definition →, the other with money.
Terms in this piece · Glossary
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
system prompt — The standing instructions a model receives before any user input — defining its role, rules, tools, and tone for the whole conversation.
attention — The mechanism that lets a model weigh which earlier words matter for the word it's currently processing — the core operation of a transformer.
Why it matters
If you're deploying agents inside real organizations, this reframes the problem: instead of mapping chaotic, undocumented workflows, you may only need to define good outputs and let agents learn the path — a concrete lens for scoping enterprise AI adoption.
Key quotes
“The CEO, after being walked through the map, sat down, put his head on the table, and said, "This is even more fucked up than I imagined."”
“encoding human understanding into an AI tends to be worse than just letting the AI figure out how to solve the problem, and adding enough computing power until it can do it better than any human”
“To improve Manus will involve more careful crafting and bespoke work, to improve ChatGPT agents simply requires more computer chips and more examples.”
“Instead of untangling every broken process, he just needs to define success and let AI navigate the mess.”
