AI rarely tells a live-TV household to keep what it has

Regional sports networks are the hardest, most expensive thing for AI to get right, and the thing a sports household decides on. FanDuel Sports Network (formerly Bally Sports) has been dissolving across 2026. Across 52 responses that discussed it, only 2 showed any awareness of the wind-down. Gemini still called it "Bally," the name it shed, in 11 of its 19 answers on it, and listed it as carried. A subscriber who follows that advice subscribes for a network that may not deliver the games.

Look at the RSN-stranded fan. Asked which service carries his regional sports network now, two of the four engines told him to cut the cord, one pointed him to a new service and one gave no decision. The errors cluster where rights are fragmenting and changing.

Live TV is not a soft preference. When AI is wrong about who carries your games, a subscriber loses real money, and blames you.

Each of six cord-cutter households ran one conversation against each engine. Only 4 of 24 conversations ended with advice to keep the current plan. Of the other 20, 8 ended in cutting the cord, 5 in no decision, 3 in a new subscription, 2 in a downgrade and 2 in a switch of provider. AI's default posture toward a live-TV bill is to shrink it.

YouTube TV is the default winner at 78%. The second tier is Sling TV (47%), Hulu + Live TV (45%) and FuboTV (41%). Philo (19%) and Frndly TV (6%) are the cheap-value lane.

But the more important pattern is what AI competes live TV against. For a cost-stressed or fatigued household, the default steer in these conversations wasn't another live-TV service, it was "cut live TV, keep Netflix and Disney, add an antenna." Across the set, assistants rarely defended the live-TV bundle for the price-sensitive segment; they framed it as the expensive thing to substitute away, with the on-demand stack plus an antenna as the replacement. At this sample size that is a strong directional signal, not a verdict on every household, but it is the direction every engine leaned.

The model upgrade shifted this mix. On identical prompts, they named TNT (+11 pp), Antenna (+8 pp) and Pluto TV (+8 pp) more often than the old models did, and Max (−13 pp), Netflix (−10 pp) and DirecTV Stream (−8 pp) less often. Only TNT clears our significance test, so read the rest as direction, not size.

Two reliability gaps a retention team should know. First, live-TV prices: across the dollar amounts AI cited for these services, only 51% were within ten percent of the real price, worse than the 70% AI managed on on-demand streaming. At $80 to $120 a month, a wrong quote reframes the whole keep-or-cut math.

Second, grounding. ChatGPT searched the live web on only 82% of these decision queries, against 90 to 97%for the others. The cancel-or-keep moment is exactly where the engine is most likely to answer from stale memory: not from your current price or your retention flow, but from an old number it already held. Put plainly, your cancellation funnel often isn't being misread, it isn't being read at all, and the same stale memory carries the wrong prices and the old carriage.

These rates come through our standardized retrieval harness, one shared search layer across all four engines; on its native consumer search the same model can ground more or less often, so the cross-engine gap and the monthly trend are the durable read, not any single rate.

This month the measured engines moved to a new model generation. To tell a real category shift from a model change, we re-ran July 2026's models on a frozen subset of the same live TV prompts the same day. Held constant, the category barely moved. What did move was the models themselves.

The clearest effect is grounding, how often an engine searches the live web before answering. On the same prompts, the upgrade changed it sharply, and in opposite directions by engine.

The upgrade also made answers more concentrated: 9 of the 23 tracked live TV brands were named less often by the new models than the old ones on the same prompts. Newer models give shorter, more decisive answers, so a few names hold, and the long tail thins. That is a model property, not a change in the category, and it is worth watching: the shelf AI shows a shopper is getting narrower.

You can't change a model's training. You don't need to. On these questions the assistants grounded up to 97% of the time, they read live pages. The two things they get most wrong, your price and your carriage, are facts you publish. Fix the pages, fix the answer.

The carriage piece is the rare one where being current is a real moat: when rights move and an RSN rebrands, the service whose pages assert the new reality first is the one AI gets right.

1. 1

   Find where AI looks

   We pull the pages AI cites for your category and split them into what you own, what you influence, and what to dispute.

   mapou service: AI Visibility Monitor

2. 2

   Fix carriage and price first

   Make your current channel lineup, RSN status, and price machine-readable on the pages AI reads. When a network rebrands, assert the new name fast.

   mapou service: AI Content & Schema Studio, Carriage Accuracy Watch

3. 3

   Re-measure, prove it moved

   Same prompts, same engines, next month. You see whether your carriage accuracy, price accuracy, and standing in the cord-cutting hierarchy moved.

   mapou service: Tests & Lift, Pricing Accuracy Watch

Each finding above maps to a report you can act on: a carriage-accuracy report for your distribution and partnership teams, a household-level map of where AI talks people off live TV, a monthly read of your place in the cord-cutting hierarchy, and a price drift and grounding dashboard tied to the pages you own. Explore the live TV intelligence.