"Representativeness" has to do with the process of sampling, not the sample size -- a sample of one is in fact a representative sample of the population in the sense of being unbiased for the quantity of interest.
Here's a simple anecdote: suppose you want to measure how often a coin comes up heads. The true answer is 50% heads. If your "sample" is a single coin flip, the answer will always be 100% or 0% (both wrong answers!). Maybe you do this experiment and get 100% and I do it and get 0%. But since the magnitude of the error will be the same on either side, on average across many repetitions of the experiment (this is called a "sampling distribution") we'll get the right answer.
What adding additional sample size does is reduce the variance of the estimated statistic -- that is to say reduce the degree to which the estimate of the parameter moves around across samples. If I flip the coin 100 times and you flip the coin 100 times, we're both likely to get very close answers to one another.
The bigger concern here is not sample size, it's whether the sampling was random (it was not) and whether the sample frame -- the population from which they were sampling -- matches the population of interest (it does not, as you suggest in your post, so your instincts here are good!).
There is very little reason to believe the people who chose to reply to the email are as-if random with respect to the question being asked. Rather, I would expect diehards of RideShareGuy (who likely converge on RSG's approximate editorial position on this issue) are more likely to reply. There is also likely to be confounding based on age, hours worked per week, geographical location in the country, etc.
There is also very little reason to believe RideShareGuy's mailing list represents rideshare drivers as a whole; again, selection based on age, tech savvy, English competency, SES, geographic location, etc. all likely to be confounders.
If this were a classical random sample of a valid sample frame, the parameter of interest would have a classical margin of error +- 3.6%, which is small compared to the overall story being told. This speaks to your concern. A simple rule of thumb is that classical MOEs are +- 1/sqrt(n) where n is the sample size. This comment is too long so I won't get into the derivation here.
I actually think this question presents a lot of interesting problems for a survey statistician. In particular, I would guess there is extreme subgroup heterogeneity -- that is to say there are classes of people who overwhelmingly want to be contractors and classes of people who overwhelmingly want to be employees. My guess would be that the population-wide parameter is of little interest compared to identifying those groups. If we discovered that, say, every person above 40 hours a week wanted to be an employee and every person below wanted to be a contractor, it'd be an error to present a weighted average of the groups versus exploring policy solutions that reflect that heterogeneity.