At some point in the last 12–18 months, I realized I was no longer just negotiating against people. I was negotiating against people who had their own AI sitting in the background. You can feel it. Emails get tighter. Redlines look strangely consistent. Brokers show up with eerily polished talking points that hit every weak spot in your position. So I stopped thinking of AI as my private edge and started treating it as the other side’s junior partner . Then I asked a simple question: “If they are using AI to press their advantage, how do I use AI to negotiate against their AI?” What The Other Side’s AI Is Probably Doing You do not need to see their screen to make a pretty good guess. On any serious CRE negotiation, the other side’s AI is likely helping them: Summarize documents faster OMs, leases, LOIs, PSAs, lender quotes. It is compressing complexity into talking points. Normalize positions across deals “What is market” on SNDA language, exclusivity, assignment, caps, carve‑outs. Generate counters and “reasonable” alternatives Tighter deadlines, smaller caps, higher deposits, more seller covenants. Surface your weak spots Any inconsistency between what you said before and what you are saying now. If you pretend they are not doing this, you negotiate like it is 2015 and you are outnumbered on the back end. Instead, I assume their AI is in the room, and I bring mine too. Step 1: Build A Model Of The Other Side Before You Ever Talk Numbers Before I send a serious mark‑up or LOI, I feed my own AI: The full email thread The OM and major exhibits Any public information on the counterparty (prior deals, press, litigation, capital sources) Redlines or term sheets they have sent on other deals, if I have them Then I ask very direct questions: “Based on this pattern, what does this party care about more: price, speed, certainty, or control?” “Where have they held firm in past deals, and where have they traded?” “If you were advising them, what three points would you tell them not to give up here?” AI is very good at this kind of pattern spotting. The output is not magic. It is a working profile: They hate open‑ended indemnities They are obsessed with timeline They will swallow some economic pain if they can brag about headline price Their credit committee seems rigid on leverage or DSCR I walk into the negotiation already assuming: “Their AI has told them exactly where I am weakest and where they have room. I am going to do the same from my side.” Step 2: Let AI “Red‑Team” Your Own Position Before They Do I do not send a mark‑up or proposal until my AI has tried to tear it apart. Here is the sequence. Give AI my draft LOI / redlines plus their last version Ask: “If you are the counterparty’s advisor, what are the five easiest ways to attack this draft?” “Where do my positions look extreme relative to the data and to what I have already said?” “Where am I accidentally signaling that I am more flexible than I claim?” Then tighten or reframe: Language that is vulnerable to being split or re‑interpreted Numbers that do not sync with my stated story Soft spots where my walkaway does not match the economics on the page This is where AI for CRE is particularly helpful because it knows the domain: It can compare my carve‑outs and caps to “market” samples I have fed it It can see when my debt assumptions do not line up with current lender term sheets It will flag when my “I need 30 days” line makes no sense given the clean file I already admitted I have I want those hits from my side before their model lands the same punches in someone else’s inbox. Step 3: Use AI To Design Concessions And Packages, Not Just Responses Most people use AI to rephrase what they already want to say. You can use it to structure the entire negotiation . Given the counterparty profile and my real constraints, I ask: “Design three concession packages that keep my economics intact but feel meaningful to them, based on their priorities.” “If I must give ground on X, what can I reasonably ask for on Y and Z to more than compensate?” “What sequence of asks has the highest chance of getting me to [target outcome] in [N] rounds?” Examples: For a seller who loves certainty: I may let AI help structure tighter financing and DD timelines in exchange for: Real price protection Clear closing mechanics Fewer “hidden” obligations post‑close For a buyer obsessed with price: I may frame non‑price terms (reps, survival, escrows, access) as chips they can “win” While AI checks that my total risk does not quietly explode in the background The point is to negotiate like a chess player, not a checkers player. Their AI is already generating sequences of counters. I want mine designing the whole game tree from my side, given what we know about them. Step 4: Let AI Watch The Tape Between Rounds After a hard call or key email exchange, I drop the transcript or thread into my system. I ask: “What did they reveal about their real constraints or walkaway point that they did