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Selfish mining undermines incentive compatibility of proof-of-work blockchains, letting a miner earn disproportionate rewards at a hashrate lower than the majority threshold. A decade of research has investigated whether a strategy is profitable, however, far less is understood about when it becomes profitable. Timing is critical since selfish mining operates at a loss before it turns a profit, typically requiring tens of weeks to break even in the classic case. In this paper, we present a holistic study of the time-to-profitability (TTP) of existing selfish mining strategies structured around four contributions. First, in the single-attacker setting, we characterize TTP across the full strategy space and find that TTP-minimizing and profit-maximizing strategies frequently diverge, making attack horizon a critical metric. In particular, under realistic fee dynamics, the use of incentive transactions to recruit honest-but-rational miners enables reaching profitability up to 15× faster than classic selfish mining at the same hash rate. Second, we study TTP for the first time in the multi-attacker setting, showing that the difference in strategies between opposing attackers has a dramatic impact on join profitability. Third, we generalize intermittent selfish mining by examining temporal composition over the full strategy space and showing that its purported benefits are largely overstated; alternating strategies rarely outperforms the best static strategy in terms of TTP and long-term profits. Finally, and building on these findings, we investigate adaptive, state-conditioned strategy selection at the difficulty adjustment period (DAP) level. We compare a general-purpose LLM agent against a fixed decision-tree selector. We find that both selectors reliably identify profit-maximizing strategies from observed network conditions, at a low operational cost, thus lowering the expertise barrier to exploiting adaptive selfish mining.