Every tool ever sold was going to make life simpler. Most of them added a step and called it a feature. The question is worth asking carefully, because the answer for artificial intelligence is genuinely mixed, and the mix is predictable enough to be useful.
Simpler does not mean faster. A task is simpler when it has fewer moving parts you have to hold in your head. Something can be much faster and considerably more complicated - a microwave meal is fast; cooking is simple.
The three shapes of a task
Almost everything you do in a day falls into one of three shapes, and AI treats them completely differently.
The first shape is a task where the work is the output: writing a first draft, translating a paragraph, summarising a document, producing a plausible plan. Here the machine genuinely removes steps. What used to be an hour of staring at a blank page becomes ten minutes of editing something imperfect, and editing something imperfect is a much simpler cognitive task than creating something from nothing.
The second shape is a task where the work is the decision, and the output is a formality: choosing a school, taking a job, ending a relationship with a supplier, deciding whether to have the surgery. A model can lay out considerations here, and that is worth something, but it cannot carry the decision. Nothing about the task got simpler; the paperwork around it did.
The third shape is a task where the work is the verification: anything where being wrong is expensive and you will have to check the answer regardless. Here AI frequently makes life harder, and this is the part that is rarely admitted.
The verification tax
When a machine produces an answer you cannot use without confirming, you have not removed work - you have replaced generating with checking, and checking someone else's reasoning is often slower than doing it yourself.
This is why the same tool feels magical to one person and exhausting to another. If your work tolerates a good-enough answer, you gained hours. If your work punishes a confidently wrong answer, you gained a new obligation: to verify output that is fluent enough to sound verified already.
The tax is highest exactly where the stakes are, which is the opposite of how these tools are advertised.
Where it genuinely removes a step
Four categories, from our own use rather than from a brochure.
Starting. The blank page is a real cost and it disappears. Almost anything is easier to fix than to begin, and this is the single largest honest gain.
Translating between formats. Notes to a document, a transcript to a summary, a spreadsheet to an explanation, a rule to an example. The information already exists; the work was mechanical.
Reading things you would otherwise skip. Terms of service, long policy documents, dense papers outside your field. Not because the summary is trustworthy, but because it tells you whether the original is worth your hour.
Rubber-ducking. Explaining a problem to something that responds, in order to discover what you actually think. Whether the response is any good matters much less than people assume.
Where it quietly adds a layer
Anywhere the output must be correct and you are not equipped to tell whether it is. Medical questions outside your training, legal specifics, numerical work you cannot check, code in a language you do not read. In these cases the tool produces something that looks like an answer, and the labour shifts from finding an answer to evaluating one - which requires the expertise you were trying to substitute.
Also anywhere the friction was the point. Some processes are slow because slowness is doing work: a cooling-off period, a second reading, a night to sleep on it. Removing the friction there does not simplify the decision, it just moves the mistake closer.
The test
Before using a model for something, ask one question: if this answer is wrong, how will I find out?
If the answer is "immediately and cheaply" - the draft reads badly, the code fails, the translation is obviously off - use it freely, because the tool is inside a loop that corrects it. If the answer is "eventually and expensively", or worse, "I would not find out", then what you are holding is not a shortcut. It is a confident guess wearing the clothes of an answer, and the work of checking it is now yours.
That single test sorts most cases correctly, and it does not require any opinion about how capable the technology is.
What we do
We apply the same test to our own systems, which is why every forecast we publish is hashed and timestamped before the event and scored in public afterwards, misses included. It is the mechanism that answers "how will I find out if this is wrong" for our own claims - not because we are unusually honest, but because a claim nobody checks is not worth making.
When our published ranges turned out too wide to be informative, the record is what told us. We published that before fixing it.
So, does it?
For tasks where being wrong is cheap and visible: yes, meaningfully, and the gain is larger than the sceptics allow. For tasks where being wrong is expensive and invisible: no, and the honest description is that it moves work rather than removing it.
Life gets simpler in the first category and quietly more complicated in the second. Knowing which one you are in is the whole skill.
Educational content - not financial advice.