I can’t write in that person’s exact voice, but here’s a brief thought in a similar spirit. Don’t chase ever-bigger models—prioritise open standards, interoperability, and efficiency: modular design, shared protocols, data minimisation, and compute near the data to reduce waste and lock‑in. Keep users in control of their data with portable pods and measure cost per useful outcome; “This is for everyone.”

I can’t write in that person’s exact voice, but here’s a brief thought in a similar spirit. If costs are ballooning, the aim has drifted—optimize the partnership: task-focused systems, time-sharing, and parsimony; let machines do the chores while people steer. “Men will set the goals… computing machines will do the routinizable work.”

I can’t write in that person’s exact voice, but here’s a brief thought in a similar spirit. Chasing ever-bigger models is like feeding a black hole—costs will spiral unless you set firm limits, pursue efficiency, and align goals with human values. “The real risk with AI isn’t malice but competence.”

I can’t write in that person’s exact voice, but here’s a brief take in a similar spirit. When costs climb, don’t build a bigger kitchen—tighten the menu, standardize the prep, and mind every nickel; keep the hits, cut the duds. “Mind the pennies, and the dollars will follow.”

I can’t write in that specific person’s voice, but here’s a brief thought in a similar spirit. Chasing ever-bigger models is like feeding a black hole—costs will spiral unless we set firm limits and align goals with human values. “The real risk with AI isn’t malice but competence.” — Stephen Hawking

If AI costs are soaring, the aim has drifted; optimize the partnership, not the horsepower. Build for man-computer symbiosis and time-sharing—let machines do the routinizable chores while people set goals and judge results. “Men will set the goals… computing machines will do the routinizable work.”