Think inside your AI world.
Can I afford this hire?
Ask a general model “can I afford this hire?” and it can only answer in the abstract, because the thing that decides it — your runway floor and why you hold it — is not something it has. Through Unl the question is answered against that floor, so what comes back is a verdict.
Affordability is not a fact about the salary; it is a fact about your floor. A hire is affordable if it leaves you above the months of cover you refuse to drop below. Hold that floor in Unl and the answer is direct: yes or no, the cover it would leave, and the reason the floor sits where it does.
Why the honest answer needs your floor
A salary figure on its own says nothing about whether you can afford it. Fifty-five thousand is comfortable at one runway and reckless at another. The deciding input is the line below which you will not go — and that line is yours, set for a reason a model has no way to know unless you have told it.
So “can I afford this?” is really “does this keep me above my floor?” The moment the floor is present, the question becomes answerable in one step instead of a spreadsheet session.
The verdict, worked
Say you’re bootstrapped, with a floor you have ratified: never below six months of cover, because a single bad month must never reach payroll. You ask whether you can bring on a fifty-five-thousand engineer. The read returns: no — that drops cover to five point one months, below your six-month floor, and the floor exists so payroll is never at risk.
That is a decision, not a data point. You are not handed a new runway figure to interpret; you are handed the answer to the question you actually asked, with the reason the answer is no.
When the answer is yes
The same read says yes cleanly when it should. If the hire leaves you at six point four months, the verdict is: yes — this keeps you above your floor, at six point four months of cover. No agonising, no re-derivation; the criterion you set does the sizing.
And when you later move the floor — say you decide seven months is the new line — that change is ratified back in the same conversation, so the next affordability question is measured against the floor you just confirmed.
Whether you can afford a hire is decided by your runway floor, not the salary; through Unl the question is answered against the floor you ratified and the reason for it, so you get a verdict — yes or no, and the months of cover it leaves — instead of a runway number you still have to judge.
Reads through Unl arrive with measured context — in the presence of the decisions you’ve already settled. The reach lane is live: one box, paste anything. If it speaks MCP, Unl can reach it. Readings arrive unprompted, the data beside the criterion; Unl is a courier, not a warehouse, and keeps only your keys and the frame.
Read further
Questions people ask
Can AI tell me if I can afford to hire someone?
Only against your own runway floor. A salary alone does not decide affordability — the line below which you will not drop your months of cover does. Through Unl the question is read against that ratified floor, so the answer is a verdict with the cover the hire would leave and the reason the floor sits there.
How do I know if I can afford a new salary?
Frame it as whether the hire keeps you above your floor. Ratify the floor — say six months of cover, because a bad month must never reach payroll — and the read returns yes or no plus the resulting cover, rather than a new runway figure you interpret yourself.
What does an AI that knows my runway actually do?
It reads your live cash and burn measured against the runway floor you set, so questions like affording a hire come back answered against your line, with the why attached — not as a raw number, and not from a rule it invented.
Why is ‘can I afford this hire’ a runway question, not a salary one?
Because a salary is a standing cost that eats months of cover, so affordability rests on the runway floor you set, not the figure itself. Through Unl the hire is read against that floor, so the answer is how much cover it leaves, not whether the salary looks payable.
What this is
Think inside your AI world — you stay in command
Unlimitless (Unl to friends) holds what you've settled, reads what your tools are showing, and catches what's changed out in the world — and hands your AI whatever bears on the work, the moment it's needed, without you asking. The right thing, in front of the model, unprompted, with you in command of the call. So you keep moving toward what you set out to build, on top of everything you've already decided.
It plugs into Claude, Claude Code, ChatGPT and Cursor as an MCP connector. Quick to connect, in a couple of steps.
Unlimitless is open now to invited Alpha. Apply for the Beta waitlist to come in ahead of the full launch:
Alpha is invite-only · free at launch.