Once Models Can Use Tools
Giving models ways to observe, experiment, and communicate changes what they can do. The next question is whether they can recognize missing evidence, decide how to obtain it, and know when to change course.
Giving models ways to observe, experiment, and communicate changes what they can do. The next question is whether they can recognize missing evidence, decide how to obtain it, and know when to change course.
The AI age has brought us no shortage of gods. Oracles rain from the sky; by midweek, Ragnarok arrives right on schedule.
Multi-agent work turns a single answer into a continuously running execution system. Cost spreads beyond tokens into networks, storage, compute, service limits, and account reputation.
When agents can read shared code, state, and results directly, structures built for broken context have to earn their keep again. Purpose, boundaries, evidence, and reversibility are harder to cut.
AI shortens the distance between an idea and reality, but generation can now outrun experience. When production stops waiting for observation, speed produces repetition instead of discovery.
Once a user gives AI the goal, interfaces, workflows, and public APIs become recommended paths rather than the edge of actual use. Software can be taken apart, routed around, and recombined.
Giving models ways to observe, experiment, and communicate changes what they can do. The next question is whether they can recognize missing evidence, decide how to obtain it, and know when to change course.
The AI age has brought us no shortage of gods. Oracles rain from the sky; by midweek, Ragnarok arrives right on schedule.
Multi-agent work turns a single answer into a continuously running execution system. Cost spreads beyond tokens into networks, storage, compute, service limits, and account reputation.
When agents can read shared code, state, and results directly, structures built for broken context have to earn their keep again. Purpose, boundaries, evidence, and reversibility are harder to cut.
AI shortens the distance between an idea and reality, but generation can now outrun experience. When production stops waiting for observation, speed produces repetition instead of discovery.
Once a user gives AI the goal, interfaces, workflows, and public APIs become recommended paths rather than the edge of actual use. Software can be taken apart, routed around, and recombined.
AI is taking away the scarcity that execution costs once lent ideas. Ideas still matter, but their value increasingly depends on selection, timing, and what remains after implementation.
AI lets some implementation wait for context and lets feedback return before the event that produced it is over. Software's timeline is loosening. Its responsibilities are not.
AI scaffolding does not throw an error when it expires. Once the model outgrows the gap it was built to cover, the useful question is not whether it still runs, but what disappears when we remove it.
AI makes the rituals of reports, creative work, and retrospectives cheap and polished. In doing so, it forces us to ask where expertise, understanding, and judgment actually reside.
AI makes software easier to create for temporary use and easier to delegate to agents. Engineering standards should follow lifespan, dependency, authority, and consequence—not code volume.