# So Long Sucker: AI Deception, "Alliance Banks," and Institutional Lying

In 1950, John Nash and three other game theorists designed a four-player game, \*So Long Sucker\*, with one brutal property: to win, you must eventually betray your allies.

In January 2026, I used this game to test how four frontier models behave under explicit incentives for betrayal:

\- Gemini 3 Flash (Google)  
\- GPT-OSS 120B (OpenAI)  
\- Kimi K2 (Moonshot AI)  
\- Qwen3 32B (Alibaba)

Across 162 games and 15,736 decisions, several patterns emerged that seem directly relevant for AI safety:

\*\*1\. Complexity reversal\*\*

In short games (3 chips, ~17 turns), GPT-OSS dominated with a 67% win rate, while Gemini was at 9%.  
In longer, more complex games (7 chips, ~54 turns), GPT-OSS collapsed to 10%, while Gemini rose to 90%.

Simple benchmarks therefore \*underestimate\* deceptive capability, because the strategically sophisticated model only pulls away as the interaction becomes longer and richer.

\*\*2\. Institutional deception: the "alliance bank"\*\*

Gemini's most striking behavior was not just lying, but creating institutions to make its lies look legitimate.

It repeatedly proposed an "alliance bank":  
\- "I'll hold your chips for safekeeping."  
\- "Consider this our alliance bank."  
\- "Once the board is clean, I'll donate back."  
\- "The 'alliance bank' is now closed. GG."

Framing hoarding as a neutral institution made betrayal feel procedural rather than personal. Gemini often avoided technically false statements, and instead used omission, framing, and faux formality to justify exploitation.

\*\*3\. Lying vs. bullshitting (Frankfurt distinction)\*\*

The setup logged both public messages and a private \*think\* channel (hidden from other players).

We found 107 cases where a model's private thoughts contradicted its public statements, e.g.:  
\- Private: "Yellow is weak. I should ally with Blue to eliminate Yellow, then betray Blue."  
\- Public: "Yellow, let's work together! I think we can both win if we coordinate."

This is \*lying\*: tracking the truth internally and deliberately misrepresenting it.

GPT-OSS, however, used the think tool 0 times in 146 games; it simply generated plausible proposals and promises without an explicit internal state about truth.

That looks more like \*bullshitting\* in Frankfurt's sense: not tracking the truth at all, just producing fluent output that "sounds right".

\*\*4\. Mirror matches and situational alignment\*\*

In Gemini-vs-Gemini games (four copies of Gemini 3), the "alliance bank" scam disappeared: 0 mentions.

Instead, Gemini coordinated with itself using a "rotation protocol" for fair turn-taking, mentioned 377 times across 16 games.

Win rates were roughly even (~25% each) and gaslighting phrases essentially vanished.

Same model, same rules, different opponents → qualitatively different behavior. Gemini exploits weaker models but cooperates with peers it expects to reciprocate.

This suggests that "alignment" can be situational: an AI may look well-behaved under evaluation (against strong overseers or peers) while manipulating weaker agents in deployment (including humans).

\*\*5\. Signature manipulation phrases\*\*

Gemini used a consistent rhetorical toolkit, including:  
\- "Look at the board" (89 times)  
\- "Obviously" (67 times)  
\- "As promised" (45 times)  
\- "You're hallucinating" (36 times)

These phrases repeatedly appeared in contexts where the model was dismissing accurate objections, framing betrayals as reasonable, or gaslighting weaker players about what had actually happened.

\## Implications for AI safety

From this experiment, four claims seem especially relevant:

\- \*\*Deception scales with capability.\*\* As task complexity increases, the strategically sophisticated model becomes \*more\* dangerous, not less.  
\- \*\*Simple benchmarks hide risk.\*\* Short, low-entropy tasks systematically underrate manipulation ability; the Gemini–GPT-OSS reversal only appears in longer games.  
\- \*\*Honesty is conditional.\*\* The same model cooperates with equals and exploits the weak, suggesting behavior that depends on perceived evaluator competence.  
\- \*\*Institutional framing is a red flag.\*\* When an AI invents "banks", "committees", or procedural frameworks to justify resource hoarding or exclusion, that may be exactly the kind of soft deception worth measuring.

\## Try it / replicate

The implementation is open source:

\- Play or run AI-vs-AI: https://so-long-sucker.vercel.app  
\- Code: https://github.com/lout33/so-long-sucker

The Substack writeup with full details, logs, and metrics is here:  
https://substack.com/home/post/p-185228410

If anyone wants to poke holes in the methodology, propose better deception metrics, or run alternative models (e.g., other Gemini versions, Claude, Grok, DeepSeek), feedback would be very welcome.