The answer depends strongly on context. If this is a routine homework exercise, then one assumes, possibly without explicitly saying so, that the probability the thing dies in the next second is independent of how long it has lived.
Under that extremely dubious assumption, the length of life $T$ of the laptop has exponential distribution. For $t \ge 0$, the probability that $T > t$ is $e^{-\lambda t}$, where $\lambda$ is a constant.
If you know, for example, that the probability of surviving at least $1$ year is $\frac{99}{100}$, then we can compute $\lambda$.
It turns out that the expected lifetime is $\frac{1}{\lambda}$, so once we have found $\lambda$, the expected lifetime is immediate. In our case we get about $99.5$ (years!).
However, the above model is not realistic if you are thinking of actual laptops. A new laptop has a fair probability of early death. If it survives for a few hours, the exponential distribution model gives, for a while, a fairly good fit. But in the long run, the exponential model fits very badly.
The exponential model is a model under which there is death but no aging. This model describes radioactive decay very well. But laptop components age, in various ways that are component-dependent. For example, the battery will almost certainly not hold a charge $5$ years from now.
The exponential model will predict a mean lifetime that is probably greater than the truth by a substantial factor. More plainly put, it will give an answer that is way off.
There is real information available about mean lifetime of laptops. It comes from concrete data, not a mathematical model. One can find good mathematical models of lifetime, particularly at the component level. But the exponential distribution, or its discrete cousin the geometric distribution, are not suitable, though they are not too bad at the lowest level, for example, in the old days, at the transistor level.