A good assistant is not always the one that answers fastest. Sometimes it should ask for another file. Sometimes it should stop reading and act. Sometimes it should think privately for a few more steps. Sometimes it should say nothing, because another paragraph of “reasoning” would merely burn tokens while impressing nobody except the invoice. ...
Spreadsheet work has a special kind of comedy. A person asks an AI agent to load a dataset, clean a few columns, train a model, generate predictions, and save a prediction.csv file. The agent writes plausible Python. The model architecture is reasonable. The explanation sounds confident. Then the whole thing fails because the agent forgot to pass the filename into the execution tool. ...
When Less Proves More: The Case for Minimalist AI Theorem Provers Proof is a good place to test AI humility. In ordinary business writing, a model can sound confident, cite familiar patterns, and still be quietly wrong. The error may not surface until the contract is signed, the policy memo is circulated, or the spreadsheet has already acquired the authority of a sacred object. In formal theorem proving, the arrangement is less polite. The model writes code. Lean compiles it. The compiler either accepts the proof or sends it back covered in red ink. ...
Meetings are where context goes to suffer. A product manager remembers the customer constraint. A data engineer remembers the schema problem. A finance lead remembers the cost ceiling. A compliance officer remembers the rule nobody else wanted to read. The trouble begins when everyone is forced to work from the same swollen transcript, the same vague summary, or the same “shared memory” that turns specialists into slightly different versions of the same forgetful intern. ...
Control is the unglamorous word hiding under the fashionable one. A telecom operator says: “Enter energy-saving mode, but keep user 3 above 50 Mbps and everyone else above 10 Mbps.” That sounds like a natural-language interface problem. Parse the sentence, extract the numbers, send the command. Very modern. Very demo-friendly. Also very incomplete. ...
Architecture is usually where AI enthusiasm goes to become expensive. A team starts with a capable model. Then it adds a planner. Then memory. Then a tool router. Then a critic. Then a second critic because the first critic was apparently too polite. A few weeks later, the “agent” works on the demo path, fails on the second edge case, and nobody can explain whether the problem is the prompt, the retrieval layer, the tool schema, the memory policy, or the small parliament of LLM calls now debating inside the workflow. ...
Email is a fine place to see the problem. Ask a large language model to draft a reply, and it usually performs well. Ask it to clear a messy inbox, identify urgent client messages, compare them with your calendar, draft replies, escalate risks, update a CRM, and avoid accidentally sending confidential material to the wrong person, and the cheerful single-assistant fantasy begins to sweat. ...
Memory is where many AI systems quietly lose their dignity. A user corrects an agent. A compliance rule changes. A contract clause is clarified. A retrieval system finds a newer document that contradicts an older one. The system must decide what to do with the new information. Should it update because the world has changed, or revise because its earlier belief was wrong? ...
Trading teams rarely fail because nobody had a title. They fail because the signal gets lost somewhere between the analyst, the sector specialist, the portfolio manager, and the final trade list. Someone sees momentum. Someone else sees valuation. A news analyst notices a red flag. A macro analyst says the regime is awkward. Then the PM receives a pile of half-compatible opinions and performs the ancient institutional ritual known as “synthesis,” which is often just a polite word for discretionary compression. ...
Chest X-rays are not mysterious objects. They are images that radiologists interrogate through a disciplined sequence: find the anatomy, measure what matters, compare against criteria, and then make a diagnostic judgment. The modern vision-language model often skips the middle of that sequence. It looks at the image, produces a polished explanation, and hopes the reader will not ask too aggressively where the evidence came from. This is how medical AI becomes impressive in a demo and uncomfortable in a clinic. Fluency is cheap. Verifiability is expensive. ...