How AI Finds Optimal Ill-Gotten Gains Combos in MTG

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Ill-Gotten Gains card art by Greg Staples

Image courtesy of Scryfall.com

Harnessing AI to Find Optimal Ill-Gotten Gains Combos in MTG

Magic: The Gathering has always been a playground for clever strategies, patient planning, and a little dash of luck. Today, AI researchers and deck builders alike are asking a familiar question from a slightly different angle: how can machines uncover the most efficient, scalable combos for a given card? Take Ill-Gotten Gains from the Conspiracy set as a perfect case study. This rare black sorcery, with mana cost 2BB, exiles itself and triggers a dramatic reset: each player discards their hand, then returns up to three cards from their graveyard to their hand. It’s a social, chaotic tool that can swing the table in surprising ways 🧙‍♂️🔥💎.

In the hands of a human, Ill-Gotten Gains is a puzzle piece: you crave a sequence where discarded cards come back in a way that benefits you more than it harms you, or where the exile lock can be leveraged for a late-game swing. For an AI, the challenge is richer still. The model must account for multiplayer dynamics, the variability of hand sizes across opponents, and the way graveyard interactions ripple through the game. The card’s flavor text—“Urza thought it a crusade. Xantcha knew it was a robbery.”—reminds us that the ethics and the spectacle of deceit are baked into the design. It’s this narrative that makes the AI exploration not just a technical exercise, but a flavor-rich investigation into how value is created and contested in MTG 🧙‍♂️🎲.

“Exile Ill-Gotten Gains. Each player discards their hand, then returns up to three cards from their graveyard to their hand.” The clause looks simple, but it reshapes the entire game state—hand sizes, graveyard content, and the tempo of everyone at the table. That layered, branching potential is exactly what a modern AI search through a combinatorial landscape loves to chew on.

So how does an AI go about this in practice? At a high level, researchers frame Ill-Gotten Gains as a node in a vast graph of possible game states. From each state, the AI evaluates legal moves and their consequences, guided by a reward function that estimates who is ahead (or who is most likely to pivot into a winning line). Here are some concrete strategies an AI typically explores: - Card-ecosystem curation: The AI builds a repository of graveyard-interaction cards—reanimation spells, fetch-and-fix tools, global effects that alter discards or draws, and engines that refill the hand or protect important pieces. Even without naming specific combos, the AI learns which archetypes tend to synergize with a mass-discard card in a black-heavy setup. - Tempo and parity awareness: Because Ill-Gotten Gains can shift momentum across all players, the AI evaluates not just its own ideal outcomes but how the discard-and-reanimate cycle affects opponents’ plans. It looks for lines where you endure a moment of disadvantage to unlock a longer-term advantage that compounds over multiple turns. - Probability and variance modeling: The trickier part is predicting opponent plays. The AI uses Monte Carlo simulations and probabilistic reasoning to estimate outcomes across a variety of human-like decisions, then leans into lines with robust win probability rather than flashy but fragile loops 🔥⚔️. - Endgame evaluation: In a world where a board state can tilt dramatically after a single discard and graveyard refill, the AI calibrates its long-term goal—are you aiming to assemble a reanimation engine, a stall-and-win plan, or a graveyard disruption scheme? It scores each path by consistency, resilience to opposing disruption, and the potential for a surprise finish 🎨. - Risk-aware exploration: The search process deliberately capstones on safe, repeatable sequences before attempting high-variance plays. In practical terms, this means preferring lines where Ill-Gotten Gains clears the field or resets the table in a way that you can ride into a decisive next turn. Beyond the computational mechanics, the exercise yields a different kind of insight: Ill-Gotten Gains embodies a paradox of MTG design. A single spell—two black mana plus two generic—can orchestrate a social reset that benefits players in echoing, sometimes chaotic, ways. AI testing against this card often reveals that some of the most reliable lines revolve around building resilience into your own graveyard setup while shaping the opponents’ options so that the discard-and-revive effect becomes a net boon for your plan rather than a shared misfortune 🧙‍♂️🎲. This is also where aesthetics meet algorithm. The CNS Conspiracy edition, with Greg Staples’ evocative art and its draft-innovation framing, invites players to imagine a world where power is negotiated at the table and finessed through cunning. AI approaches can help enthusiasts picture how different playgroups might experience Ill-Gotten Gains—whether in a tight, competitive Legacy table or in a sprawling Commander circle where potions of discard and graveyard recursions shape every round 🔥💎. Of course, you don’t need a lab full of servers to appreciate what AI curiosity brings to MTG. The real value is in better understanding deck-building tradeoffs, discovering robust lines against a range of opponents, and appreciating how a single card’s text can unlock dozens of plausible pathways. As you study Ill-Gotten Gains, you can imagine the AI’s thought process: it’s not chasing a single “perfect combo,” but a lattice of resilient options that adapt to the unpredictable theater of multiplayer magic 🧙‍♂️🎨. If you’re curious about applying AI insights to your own collection, it’s worth noting that CNS-era cards like Ill-Gotten Gains live at an interesting intersection of rarity, collectible value, and strategic depth. The card’s nonfoil and foil variants circulate with modest and aspirational price points, reflecting its status as a historically memorable piece from a draft-focused era. For collectors and competitive players alike, the card stands as a reminder that the most flavorful wins often come from clever setup and thoughtful timing—not just raw power. As you explore these ideas, remember that the best AI-driven exploration of Ill-Gotten Gains respects the card’s elegant design and its place in MTG history. The thrill of a well-timed discard, a graveyard refill, and a shared moment of table mischief remains a quintessential MTG experience 🧙‍♂️⚔️.

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