So, fine, a probabilistic model might work well. And you might decide to bid on 100% of impressions. And you might decide that you have to bid $200 CPM to win -- which you're OK doing, because they're sparse.
And then say that FooConf happens in NYC and your aggressive $200 CPM bid 100% of the time blows out your budget.
Often you can charge the customer you're acting on behalf of your actual spend + X% up to their campaign threshold. So you really want to ensure that you spend as much as possible, without spending too much. Pacing is hard. Google AdWords, for example, only promise to hit your budget +/- 20% over a 1 month period.
With a real-time data stack you can avoid the duplicated ad a good percent of the time. Better experience for buyers, for publishers, and for users.
Yeah, but when that happens I usually don't think, oh hey they are lacking an optimal in memory distributed database solution.
I think, well... their engineers suck. Or they don't care. Pick one.
edit: His point is vague, so there is nothing technical to respond to. I am very much interested in a good technical example - but the things mentioned so far are by all appearances relatively straight-forward and linear, hence lack of effort or bad engineering are the only reasonable assumptions left.
Like most problems, scale makes it hard.
A cookie won't fit all that data and a more traditional database generally won't work. In memory k/v stores like Redis won't work due to data size (TBs of data). Hbase/Cassandra/etc sort of work with latency in the 5ms range. That's fairly expensive in a 90ms SLA, but you can make it work. It does limit the amount of work you are able to do.
During that delay you might have actually already processed new bid requests (auctions) for the same user.
Depending on the order's characteristics and how much you're willing to deviate from target - especially when observed within a small time window - the above poses additional challenges w.r.t. overspending.