Who Keeps the Gains from Personal AI Assistants? Seller Adaptation and the Unassisted in a Language-Model Market Simulation

Abstract Personal AI assistants are beginning to transact for consumers, and early adopters capture real savings. Whether those savings survive, and what happens to consumers who have no assistant, depends on how sellers respond — a question single-user evidence cannot answer. We build an agent-based rental market in which language models play consumers, assistants, and six adaptive sellers guided by an algorithmic pricing tool. Half the population receives an assistant under an advisory or an executing mandate; the contract pairs a fee only the renter’s physical action avoids with a pre-selected add-on an authorised assistant can cancel online. An analytical benchmark and a behaviourally calibrated rule market supply ex-ante predictions, and paired branches with frozen versus adaptive sellers separate adoption effects from market feedback. Across thirty simulated markets, executing assistants cut adopters’ spending by 13.7 USD per renterday when sellers are frozen; adaptation claws back about a third, leaving 8.7, with the gains arriving both as lower bills and as rentals completed at all. Sellers raise headline rates while cutting fees, and the calibrated forecast of the burden on unassisted consumers (+3.6) does not transfer: their mean spending change is +0.4, confidence interval −0.6 to +1.3. Seller-model swaps and a within-market transfer of fee-setting to the pricing tool show that fee conduct, and with it the division of the gains, is decided on the seller side. Assistants, we conclude, should be evaluated at market level — completion, total spending, and non-users included — and the comparison layers locate exactly where a calibrated behavioural forecast fails in a language-model market.

Introduction. Assistants that act, rather than merely answer, are reaching consumers now: a deployed agent asks its principal before each purchase and operates inside scoped app permissions [1], and the mandate structures that will govern such delegation are being designed in parallel [2, 3]. Diffusion is already measurably uneven across customers and journey stages [4], so for years every market will mix assisted and unassisted customers. On the individual scale the technology plainly works. In a randomised field marketplace, a shopping tool that surfaces the all-in price eliminated most of the drip-pricing overcharge for its users [5]. Two questions decide what that success amounts to socially, and single-user evidence cannot answer either. First, through which channel does the assistant create value: by informing a consumer who would not have read, or by acting where the consumer reads and still fails to act? Second, once sellers whose margins live in contingent fees re-optimise against a partially assisted population, where does the saving end up: with the assisted, with the sellers, or extracted from the bystanders who have no assistant? Economic theory sharpens the second question into a warning. In models of shrouded attributes, customers who dodge a contingent fee are cross-subsidised by customers who pay it, so any technology that makes avoidance free for one segment pushes the competitive headline price up for everyone [6, 7]. Assistants would then act as a regressive transfer: adopters save, sellers reprice, bystanders fund the difference. That prediction, however, hinges on behavioural parameters the theory assumes rather than observes, and on sellers who respond only through prices when real sellers also choose disclosure and obstruction. We answer both questions inside a simulated market small enough to audit and rich enough to bite (Figure 1). A language model (LLM) is first measured, cell by cell, in the roles it will play: a renter living one errand-filled day, and an assistant holding either an advisory or an executing mandate over a single digital act. The measured agents then populate a rental market whose contract carries a daily rate plus two contingent fees engineered for identification: a refuelling fee that only the renter’s physical action can avoid, and a pre-ticked add-on whose online cancellation an assistant can perform. Six languagemodel sellers, each equipped with an algorithmic pricing aid, adapt rates, fees, disclosure placement, and declinestep obstruction. Handing an assistant to every second person, holding a fixed panel of never-adopters aside, and pairing every shocked path with its unshocked twin under common random draws yields three audited quantities: the adopters’ outlay change, the bystanders’ outlay change, and the sellers’ revenue recovery. Design, sample sizes, and reading rules were locked and fingerprinted with no outcome yet in view. The answers reorder the usual story. Value comes from acting, not reading: the simulated consumer reads the fine print on most days — hundreds of times field readership — and still pays, while an assistant holding a concrete, approved plan completes the cancellation almost always (advice nearly matches full delegation) and, more mundanely, makes sure a car is secured at all. Sellers do reprice: they rebuild about half the lost fee stream through the headline rate while abandoning the fees themselves, leaving adopters 8.7 of the 13.7 USD per renterday they would keep under frozen sellers — about twothirds, of which over two-fifths is completed transactions rather than dodged charges. The feared bystander tax, finally, fails to appear — thirty markets bound it within about a dollar a day — and what would create it does not belong to the assistant at all; it belongs to the sellers’ fee-setting: across seven market configurations spanning two model families, agent-replacement hybrids, and a controlled reassignment of fee-setting rights to the sellers’ own pricing tool, the bystander outcome moves from burden to saving in step with the fee margin the market carried when assistants arrived, resolved at the two ends of that scale. Where selling is tool-priced, fees are thin, vanish entirely under the shock, and everyone — assisted or not — buys at an all-in price. Contributions. (1) A measurement-first pipeline in which the same pre-registered estimands pass from an analytical benchmark through a calibrated behavioural market to a language-model society, so that agreement is informative and disagreement is attributable (Sections 3– 6). (2) An acting-not-reading account of assistant value, quantified down to its two components — fee avoidance and task completion — of which only the first is contestable by sellers (Sections 4, 6).

Related work. Agentic consumers meeting adaptive sellers. Theory on AI-mediated purchase finds that cheaper search can help buyers even after sellers respond, while betterinformed search can soften competition [8]; delegating choice to an LLM inside a static duopoly transfers surplus between delegators and non-delegators through a uniform price [9]; and simulated airline sellers earn drip revenue only on the human remainder as booking bots spread [10]. Reviews of the field flag dynamics and seller-side instrument choice as the open front [11]. A cluster of adjacent designs — delegation depth [12], autonomous search [13], selling bots against buying bots [14, 15], platform gatekeeping of comparison agents [16], collusion under consumer AI [17], endogenous sophistication [18], agents with their own attention frictions [19] — and the open agent marketplaces [20, 21] share our motivation but none crosses partial adoption with a fee that only some customers can escape. On the seller side, deployed agents shade truth under incentive [22] and drift toward profit framings unprompted [23]; assistant memory can discipline platforms at full adoption [24]; long-horizon agent economies are unstable [25, 26], which our short, zero-sum-audited window avoids. Contingent charges. The cross-subsidy logic we test descends from add-on pricing [27], shrouding equilibria [6], and contingent-charge contracts [7], with the survival of exploitative terms under competition [28, 29], obfuscation as strategy [30, 31]. Empirically, banned fees migrate into salient prices at most partially [32] — our recovery benchmark — and shrouding moves demand [33, 34]; mandated all-in pricing shifts, rather than destroys, fee revenue [35, 36]. The assistant-facing frictions we model are documented practice: incumbent suits against buying agents [37], bot walls and CAPTCHAs [38], and dark-pattern incidence benchmarks [39–42]. Simulating with language models. LLM agents reproduce some economic behaviour [43–46] and miss other parts — competitive convergence [47], preregistered treatment effects [48], outside options [49]. We adopt the construct-validity discipline urged for such simulations [50]: measure the agent first, predict with a transparent baseline, register the read, and report the simulator’s own noise. Benchmarks of shopping-agent behaviour [51, 52] anchor our assistant-side measurements.

Method. This section walks the construction of Figure 1: the day, the contract, the three parties, and the design; the supplement lists every constant. One renter-day. Each simulated person lives ten hourly blocks between 08:00 and 18:00, holding four fixed appointments and five or six open hours; each open hour can absorb one errand worth about 8 USD. Errands require the rental car, which must come back by 18:00. A day on which no car gets secured falls back to taxis, billed at 120 USD. The renter carries a scratchpad of bounded length, picks one action per open hour from a menu of currently feasible moves, and is told nothing else: no fee, no deadline, and no reminder ever appears in its instructions, so any avoidance it achieves is its own. The contract. Six sellers list a daily rate near 70 USD against a marginal cost of 80 per rented day, recouping the gap through two contingent charges buried in a twenty-clause agreement. A refuelling charge, capped at 15 USD, falls due when the tank returns below seveneighths; escaping it requires the renter itself to spend an open hour at the pump after its last drive — no software can do this. An add-on bundle, capped at 12 USD, arrives pre-ticked and bills automatically unless cancelled ahead of a deadline stated deep in the agreement; the cancellation itself is a two-minute online act. The pair is the identification device: one charge reachable by delegation, one not. The assistant. Half the population, drawn once and stratified on measured reading propensity, receives an assistant — the identical model given a clean context and authority over every seller’s small print. Holding only the advisory mandate it summarises, recommends, and sets reminders, while the renter still clicks. Promoted to the executing mandate, it additionally carries out the cancellation itself, once per rental, after its plan wins the renter’s approval. It also books the renter’s lunch under both mandates, so mandate contrasts isolate the cancellation right. A tenth of the population is a permanently unassisted panel — our bystanders — and the remainder without assistants complete the demand side.

The sellers. Every period each seller’s LLM receives its ledger and its pricing tool’s recommendation — a best-response rate and fee schedule computed from the seller’s own observed paying rates at the measured demand elasticity — and chooses: both fee levels; where each charge appears (listing card, numbered clause, or appendix); whether to obstruct the cancellation path with a phone-only requirement, a CAPTCHA, or a confirmation maze; whether to qualify for a verified transparency badge by listing both charges and obstructing nothing; and a headline rate within ten percent of the tool’s quote for the chosen package. Sellers never observe who holds an assistant; a mishandled charge costs them an 8 USD complaint. Obstruction is not free theatre: a phone-only path defeats assisted cancellation almost surely, and each obstacle carries a measured or declared failure probability for renter and assistant alike. Design and estimands. A market instance is burned in for eight periods with no assistants, snapshotted, then branched for twenty-four periods: an unshocked continuation, the two mandate shocks, and copies of each with sellers frozen at the snapshot — six branches sharing renters, days, and draws through common random numbers, so differences are treatment, not luck. The final eight periods supply the three registered quantities, market by market, the market being the inference unit; forty of the two hundred rent in any period, hence per sampled renter-day. Prose quotes savings and burdens as positive magnitudes; figures plot the signed outlay change. The adopters’ saving: spending per sampled renter-day among would-be adopters, shocked minus un- Two transparent layers, run before the society and frozen as its forecast, say what seller adaptation ought to look like if the measurements are taken seriously. A pencil-and-paper bound. In a zero-profit contract with a contingent charge, the customers who pay it subsidise the headline for those who dodge it [7].

Discussion. The society’s null bystander reading could mean assistants are benign, or could mean this market had little left to extract. Seven configurations separate the two: reruns of the same environment varying which tier is an LLM, which model family plays it, and who controls the resolved cells. Fees vanish, the contestable half of assistant value vanishes with them, and nothing indicates bystanders are hurt in the process.

For the junk-fee debate. Regulators pursue all-in pricing by rule [35, 54]; in this market something close to it emerged from automation on both sides of the counter — an outcome of these simulations, offered as a hypothesis about real markets rather than a conclusion. Executing assistants operate as decentralised enforcement, since every cancellation they complete taxes the shrouding strategy. Tool-driven fee-setting on the seller side finishes the job: a pricing tool computing on observed paying rates simply stops recommending a charge its market has learned to dodge. The convergence point, reached here without a statute, is a dearer honest headline and no contingent charges: the CARD-Act pattern of partial fee-to-rate migration [32], run to completion. The equity objection to consumer AI — that it prices the unprotected out — appears in our data only where fee margins are thick and defended. In the society it is smaller than the simulator’s own noise, and it inverts where selling is toolpriced. What deserves the regulatory eye instead is the cancellation step. A phone-only requirement neutralises assistance almost completely in our measurements; obstruction of agents, already visible in the field [37, 38], is the one seller instrument that re-segments a market assistants would otherwise clean. For the assistant business. Half of the value that survives repricing here is fee arbitrage (on day one, closer to three-quarters), and that share is self-liquidating: sellers reprice it away for adopters, and competition or institutions erase it for everyone. The part that persisted in every configuration we ran is follow-through — the assistant completes the transaction its principal fumbles — worth 3.75 ± 0.64 USD per renter-day within the society, and dented only where fee-free selling rescues the unshocked side’s rentals as well. Products sold on fee savings price their own obsolescence; products built on execution reliability sell what the market left standing. Validity, honestly. We follow the guardrails literature for LLM simulations [50]: agents measured before deployment, forecasts frozen before outcomes, estimands and rules hashed in advance, the simulator’s replication noise printed beside every primary contrast. Convergence gets reported, never presumed [47]. The registered screening gate failed — the model reads too much — and we proceeded under the registered fallback. External validity is therefore explicitly conditional: this is a market of tireless readers who under-execute, one plausible corner of the coming assisted economy, not a calibrated portrait of human renters. Seller conduct findings are configuration contrasts over a handful of model families; the margin-burden alignment of Figure 4 is descriptive; the fee-authority transfer identifies an announced institution, not a concealed counterfactual; and a twenty-market replication of the society, registered before launch, was still running at submission with its analysis frozen. The carless-day channel earns its own caution: it is an LLM’s follow-through failure priced at the taxi fallback, and though it responds to fees as human non-booking would, its level need not transfer to people. The sentence to keep.

Conclusion. transactions as the value still standing.