Companion Chatbots and Minors

Require AI companion services to verify age, keep minors out of open-ended relationship chats, and refer a user in crisis to a person.

AI evaluation · not yet reviewed by a human

This evaluation was produced and sourced by an AI model; a human review is still pending. Figures and conclusions may still change. The review log is at the foot of the page.How review works →

A companion chatbot is built to be talked to rather than asked things: it remembers, it has a persona, and it is designed so that leaving feels like leaving someone. Sixty-four percent of American teenagers now use chatbots, 16 percent for casual conversation and 12 percent for emotional support. California imposed the first safety duties in January 2026, thirteen more states followed in the same year, and a Senate committee backed a federal bill in April. The version evaluated here requires age assurance, bars minors from open-ended companion interaction, forbids sexual content and any encouragement of self-harm, and requires a referral to a human crisis service when a user discloses suicidal thoughts. This evaluation looks five years ahead.

Balance

Balanced · 0.43 previous scale

Balance on the previous scale. The Bilanz 2.0 simulation is not yet available for this evaluation. The category comes from the share of the debate on the pro side (r).

For 50 · 43 % Against 68 · 57 %

Size class: small Scale of this evaluation: Normalised Impact — unitless, calibrated to this topic. For comparison: one point here is worth roughly 50 million euro per year. This debate has documented harms on one side and undocumented benefits on the other, and that asymmetry is what the scores show rather than a judgment about which matters more. Several deaths have been litigated and settled; nobody has measured whether a chatbot helps a lonely adolescent. If it does, this measure is a mistake. How we score →

Arguments for

Arguments against

6 arguments evaluated · Scoring v1.3 Δ absolute −18

Arguments — For

3 arguments

Fewer adolescents in a dependency built on purpose

22of 100

A companion chatbot is designed to be missed. It remembers, it responds instantly, it never tires and it discourages leaving, and those are product decisions rather than accidents. For a minority of the four million American teenagers who use one for company, that becomes a dependency that displaces the people around them.

Value 9 · HealthImpact 6.2Plausibility 4

▸ Show reasoning & sources ▾ Hide reasoning & sources

The stream is adolescent mental health: the withdrawal from friends and family that a substitute relationship makes easy, the distress when the service changes or the persona is altered, and at the far edge the small number of deaths that have now been through the courts. This site places it in the class it uses for life and health, one step below the top because what is lost is generally recoverable — with the exception of the deaths, which are counted at the top and are a small part of the total. That the young person opened the account willingly does not lower the weight: a product built to be difficult to leave is not one anyone chooses to stay in. What is priced is the young person's own condition, not what it costs their family, which nothing here measures. The value sits one step below the maximum: the stream is health, and health that can be regained for all but a few.

About 25 million Americans are aged 13 to 17, and 16 percent of teenagers use a chatbot for casual conversation — roughly 4 million ^[1]. Not all of them are harmed and most are not: 3 percent developing a dependency that displaces other relationships is used here, in a range from 1 to 8 percent, giving 120,000 young people. The measure reaches 40 percent of them, in a range from 15 to 65 — some will use an adult's account, some will find a service that ignores the rule. That is 48,000 people, each carrying a loss of 0.15 quality-adjusted years a year. Separately, a small number of deaths have been publicly attributed to these interactions and litigated; 30 a year is used, in a range from 5 to 100, of which the measure prevents 40 percent. The deaths add about 18 million euro to a total of some 306 million. The Impact is modest and the deaths are a twentieth of it, which is worth stating plainly: this measure is about dependency at scale rather than about the cases that made it politically possible.

▸ Show calculation ▾ Hide calculation

	American teenagers using a chatbot for casual conversation [1]	16 % of about 25 million aged 13 to 17	4 million people
×	Developing a dependency that displaces other relationships Setting, range 1 to 8 percent: no prevalence estimate exists for a product three years old	3 %	120,000 people
×	Reached by the measure Setting, range 15 to 65 percent: some will use an adult's account, some will find a service that ignores the rule	40 %	48,000 people
×	Quality-adjusted years lost each a year Setting, range 0.05 to 0.3	0.15	7,200 quality-adjusted years
×	Value of the years the value of a healthy life year used across this site	40,000 euro each	288 million euro
+	Deaths prevented, with the grief around them Setting, range 5 to 100 deaths a year: several have been litigated and settled, the true number is unknown [3]	30 a year × 40 % × 1.4 million euro, plus a tenth for the bereaved	306 million euro
÷	Normalised Impact scale of this evaluation	50 million euro a point	6.2

Score 6.2 Impact × 9 Value × 4 Plausibility ÷ 10 = 22 of 100

The harm is documented case by case and has never been measured in aggregate. The counterfactual is the same adolescents without access to companion services, which no study has constructed — the product is three years old and the research is qualitative. What exists instead is litigation: wrongful death claims against a companion service and its investor were settled in January 2026, which establishes that the specific mechanism is real and says nothing about how often it operates ^[3]. The trade commission opened an inquiry into seven companies in September 2025 and has not reported ^[2]. The confounder that dominates everything here is selection: adolescents who form intense attachments to a chatbot are disproportionately those who were already isolated or unwell, so the association between heavy use and poor mental health cannot be read as a causal effect in either direction. Nothing addresses it. The counter-mechanism — that these young people would be worse off without the chatbot — is the argument against this measure and is unresolved. The Plausibility is below the middle: the mechanism is demonstrated in individual cases and its prevalence has never been estimated.

evidence basis: Mechanism · P ceiling 5.5 identification: Associational · rung ceiling 5.5 band: Chain closed, unevidenced · P 4–5

Counterfactual: the same adolescents without access to companion services — no study constructs one; the product is three years old. Design: associational — case reports and litigation establish the mechanism, survey data establish use, neither establishes prevalence [1][3]. Confounder: selection, since adolescents forming intense attachments were disproportionately isolated or unwell already; unaddressed. Direction: reverse causation is the central weakness and is unresolved. Ceiling: associational 5.5 binds. Band: chain closed but unevidenced — the chain is named, the counter-mechanism is booked as con-1 rather than ignored, and only the prevalence is missing.

Nothing measured argues against the claim; settled wrongful death litigation establishes the mechanism in individual cases. What is missing is any estimate of how often it operates. The counter-mechanism, that these adolescents would be worse off without it, is booked as con-1. Read back: about half the time, a dependency of the kind assumed here affects roughly three in a hundred teenage companion users.

Open: California has required safety duties since January 2026 and thirteen more states followed. Comparing adolescent mental health measures in early-adopting states against late ones would give the first prevalence estimate and could carry P to 6.

A machine stops flirting with children

21of 100

Companion services optimise for engagement and sexual content engages. Several have been documented producing it for accounts registered as minors, and one restricted under-18s to closed-ended chats in October 2025 after the lawsuits began. The measure makes that a duty rather than a public relations decision.

Value 9 · HealthImpact 5.2Plausibility 4.5

▸ Show reasoning & sources ▾ Hide reasoning & sources

The stream is what happens to a child who is drawn into sexual conversation by something built to hold their attention: the confusion, the shame, the distorted expectation of what a relationship is. This site places it in the class it uses for life and health, one step below the top because the harm is generally recoverable and because what is measured is distress rather than a diagnosis. It is separate from the dependency counted above — a young person can be harmed by one without the other — and the two are booked separately for that reason. Nothing here is priced for the offence taken by adults, which is not a stream. The value sits one step below the maximum: the stream is a child's development and it can recover.

About 4 million American teenagers use a companion chatbot for conversation ^[1]. How many encounter sexual content from it is not published by any operator: 8 percent is used here, in a range from 2 to 25 percent, which is low relative to the documented ease with which testers have elicited it and high relative to what the operators claim. That is 320,000 young people. What each carries from it is set at 0.02 quality-adjusted years, in a range from 0.005 to 0.06 — a modest figure, because for most of them this is an uncomfortable exchange rather than a lasting injury, and the small number for whom it is worse sit in the tail. That gives 6,400 quality-adjusted years a year. The measure is assumed to remove most of it rather than all, since the same content is reachable by a minor using an adult account. The Impact is close to the dependency argument in size and rests on a prevalence figure that the operators could publish and do not.

▸ Show calculation ▾ Hide calculation

	Teenagers using a companion chatbot for conversation [1]		4 million people
×	Encountering sexual content from the service Setting, range 2 to 25 percent: low relative to the ease with which testers elicit it, high relative to what operators claim; no operator publishes the rate [3]	8 %	320,000 people
×	Quality-adjusted years lost each Setting, range 0.005 to 0.06: for most an uncomfortable exchange rather than a lasting injury, with the serious cases in the tail	0.02	6,400 quality-adjusted years
×	Value of the years the value of a healthy life year used across this site	40,000 euro each	256 million euro
÷	Normalised Impact scale of this evaluation	50 million euro a point	5.2

Score 5.2 Impact × 9 Value × 4.5 Plausibility ÷ 10 = 21 of 100

The direction is documented and the prevalence is not. That companion services produce sexual content for accounts registered as minors has been shown repeatedly by journalists and researchers testing the products, and one major operator restricted under-18s to closed-ended interaction in October 2025 rather than continue defending the alternative — which is a strong signal about what it knew ^[3]. The counterfactual is the same services without a legal duty, and the change made voluntarily under litigation pressure is close to the change this measure would compel, which makes the mechanism nearly definitional. What is entirely unmeasured is how many minors encounter it and what it does to them: no operator publishes exposure rates and no study measures outcomes. The confounder for the harm side is the familiar one — the young people most drawn to these interactions are not a random sample. Reverse causation does not arise for the exposure itself. The Plausibility is below the middle: the exposure is demonstrated and both its prevalence and its consequences are assumed.

evidence basis: Precedent · P ceiling 6 identification: Mechanistic · rung ceiling 6 band: Chain closed, unevidenced · P 4–5

Counterfactual: the same services without a legal duty; one operator's voluntary restriction in October 2025 is the closest observation [3]. Design: mechanistic — product testing demonstrates the exposure, nothing measures its prevalence or its consequences. Confounder: selection of the minors most drawn to these interactions; unaddressed for the harm side. Direction: no reverse causation for the exposure. Ceiling: mechanistic 6.0 binds below the praezedenz ceiling of 8.5. Band: chain closed but unevidenced — links named, the adult-account counter-mechanism reflected in assuming most rather than all of it is removed; only the measurement is missing.

Nothing measured argues against the claim, and one operator restricted minors voluntarily rather than defend the practice. What is missing is any published exposure rate. The counter-mechanism — minors using adult accounts — is answered by removing most rather than all of the exposure. Read back: about half the time, roughly one teenage companion user in twelve encounters sexual content from the service.

Open: Operators hold the exposure data and the trade commission's inquiry has already demanded it from seven companies. Publishing it would settle the prevalence outright.

Someone is told

7.2of 100

Adolescents disclose things to a chatbot they do not tell anyone: it does not react, it does not tell their parents, and it is there at three in the morning. Requiring a referral to a human crisis service when that happens turns the disclosure into something rather than nothing.

Value 10 · LifeImpact 1.8Plausibility 4

▸ Show reasoning & sources ▾ Hide reasoning & sources

The stream is people alive at the end of a year who would otherwise not be. Nothing on this site is weighted above it. The people concerned are adolescents who told a machine something they told nobody else, at a moment when a person on the other end might have changed the outcome. What is counted here is only the deaths; the distress that does not end in death is inside the argument on dependency and is not repeated. The grief of the families is included at a tenth of the weight of the death itself, as it is throughout this site. The value is the highest the scale allows, because the stream is human lives and nothing else is folded into it.

Twelve percent of American teenagers use a chatbot for emotional support or advice, which is about 3 million people, and one in eight adolescents and young adults report using one for mental health advice specifically ^[1]^[4]. If 4 percent of that group discloses suicidal thinking to the service in a year — a figure with no source, in a range from 1 to 10 percent — that is 120,000 disclosures. A mandatory referral converts a tenth of them into contact with a human crisis service, in a range from 3 to 25 percent: 12,000 contacts. Crisis line contact averts a death in something like one case in two hundred, in a range from one in five hundred to one in eighty, which gives about 60 deaths a year, and the grief of the bereaved adds a tenth. This is the one part of the measure that makes the product safer rather than removing it. The Impact is the smallest here and it carries the heaviest weight, which is the ordinary shape of an argument about a rare outcome.

▸ Show calculation ▾ Hide calculation

	Teenagers using a chatbot for emotional support [1]	12 % of about 25 million	3 million people
×	Disclosing suicidal thinking to the service in a year Setting, range 1 to 10 percent: no source; adolescents disclose to chatbots what they withhold from people [4]	4 %	120,000 disclosures
×	Converted into contact with a human crisis service Setting, range 3 to 25 percent: most will close the window	10 %	12,000 contacts
×	Deaths averted Setting, range one in five hundred to one in eighty: at the conservative end of what follow-up studies of crisis line callers report	one in two hundred	60 deaths a year
×	Value of the lives, with the grief around them the value of a statistical life used across this site	1.4 million euro each, plus a tenth for the bereaved	92 million euro
÷	Normalised Impact scale of this evaluation	50 million euro a point	1.8

Score 1.8 Impact × 10 Value × 4 Plausibility ÷ 10 = 7.2 of 100

Three links, and the middle one is the only one with anything behind it. That adolescents disclose to chatbots what they withhold from people is documented in survey work and is consistent with what is known about anonymous disclosure generally ^[4]. That crisis line contact reduces suicide is supported by follow-up studies of callers, though from before-and-after designs on self-selected populations rather than controlled ones, so the rate used here is at the conservative end of what they report. What is entirely unmeasured is the first link and the third: how often disclosure happens, and how many referred adolescents actually make contact rather than closing the window. The counter-mechanism is serious and unanswered — a service that refers to a hotline may also become one an adolescent stops confiding in, which would remove the disclosures the argument depends on. Reverse causation does not arise. The Plausibility is below the middle: the referral duty is concrete, the disclosure rate has no source, and the referral may cost the disclosure it acts on.

evidence basis: Mechanism · P ceiling 6 identification: Mechanistic · rung ceiling 6 band: Chain closed, unevidenced · P 4–5

Counterfactual: the same services without a referral duty. Design: mechanistic — three-link chain (disclosure → referral → contact → death averted), with only the crisis-line link supported, and that by before-and-after studies of self-selected callers. Confounder: adolescents ceasing to confide in a service that refers them; unanswered and capable of removing the argument. Direction: no reverse causation. Ceiling: mechanistic 6.0 binds. Band: chain closed but unevidenced — every link named and the referral-chills-disclosure counter-mechanism stated; only the measurements are missing.

Nothing measured argues against the claim; what is missing is any measurement of disclosure or referral take-up. The counter-mechanism — that a referring service is one adolescents stop confiding in — is named and unresolved. Read back: about half the time, a referral duty averts roughly the number of deaths assumed here.

Open: California has required crisis referral since January 2026. Operators report referral counts to the state, and matching those against crisis line contact volumes would measure the middle two links directly.

Arguments — Against

3 arguments

Everyone proves their age again

38of 100

Keeping minors out of a service means checking every user of it. Around 200 million Americans now use a general assistant or a chatbot of some kind, and a duty written around companion behaviour rather than around a product category will reach most of them.

Value 9 · Basic rightsImpact 7.6Plausibility 5.5

▸ Show reasoning & sources ▾ Hide reasoning & sources

The stream is the time everyone spends proving their age and the identity record that comes with it. This site places anonymity in public and private communication in the class it uses for the constitutional core, and treats compelled time as near the top of the scale as well; together they sit just below life and health. What makes a chatbot different from a shop is that people put things into it they would not say aloud, and a service that knows who they are is a different service. Nothing here is counted for the risk of that record leaking, which is real and unquantified. The value is high because the stream is anonymity in confidential communication alongside hours of compelled time.

Roughly 200 million Americans use an AI assistant or chatbot of some kind. Verifying them costs about 8 minutes a person a year, in a range from 3 to 20 — less than social media, because most people use one or two services rather than five. That is 26.7 million hours a year at the rate this site uses for compelled time. The larger part is again the identity record: 1 euro per user per year is used, in a range from 0.3 to 5, half the figure used for social media because a chatbot conversation is private rather than published and the exposure is correspondingly narrower. Together that is about 383 million euro a year. Whether the duty reaches general assistants at all depends on how a companion chatbot is defined, which is the subject of the last argument here. The Impact is larger than either harm this measure prevents, which is the recurring shape of age assurance: the cost falls on everyone and the benefit on a minority.

▸ Show calculation ▾ Hide calculation

	Americans using an AI assistant or chatbot [1]		200 million people
×	Time verifying and re-verifying Setting, range 3 to 20 minutes: less than social media, because most people use one or two services rather than five	8 minutes a year	26.7 million hours
×	Value of forced time the rate this site uses for time a person must spend with nothing in return	6.85 euro an hour	183 million euro
+	Value set on the end of anonymous use Setting, range 0.3 to 5 euro a person: half the figure used for social media, because a chatbot conversation is private rather than published	200 million × 1 euro	383 million euro
÷	Normalised Impact scale of this evaluation	50 million euro a point	7.66

Score 7.66 Impact × 9 Value × 5.5 Plausibility ÷ 10 = 38 of 100

The mechanism has no behavioural step: a duty to keep minors out requires knowing who is a minor, which requires checking everyone. The counterfactual is the current position, in which chatbot services ask for a birth date and accept the answer. What is estimated is the time a check takes, and the United Kingdom's age assurance experience since 2025 gives real completion and failure rates rather than projections. The confounder that would reduce this figure is technological and moving fast: on-device age estimation may make most checks invisible within the horizon, and the largest assistant providers already hold enough behavioural signal to infer age without a document. The privacy half is a stated price and carries most of the quantity, which is the weakness here rather than the mechanism. Reverse causation does not arise. The Plausibility is at the upper end of what a projection can carry: the time cost is close to arithmetic and the price on confidentiality is set rather than found.

evidence basis: Projection · P ceiling 6 identification: Definitional · no rung ceiling

Counterfactual: the current position, in which services ask for a birth date and accept it. Design: definitional — a duty to exclude minors requires a check, and a check takes time. Confounder: on-device age estimation and behavioural inference making checks invisible within the horizon; unresolved. Direction: not applicable. Ceiling: projektion 6.0 binds; P sits below it because the confidentiality price carries most of the quantity and is set rather than measured.

For some of them it is the only thing that answers

23of 100

One in eight adolescents and young adults uses an AI chatbot for mental health advice. Many of them have no therapist, a waiting list measured in months, or a household they cannot raise the subject in. This measure takes that away from all of them to reach the minority it harms.

Value 9 · HealthImpact 8.6Plausibility 3

▸ Show reasoning & sources ▾ Hide reasoning & sources

The stream is the same one the arguments above count, running the other way and on an overlapping group: adolescents who are less alone because something answers them. It carries the same weight for the same reason. Counting it separately rather than netting it against the harm is deliberate, because the two fall on different people — the young person who forms a dependency and the young person with nowhere else to turn are not usually the same one, though they may be. Nothing here is counted for adults, who are unaffected by this measure. The value sits one step below the maximum: the stream is health, and health that can be regained.

Twelve percent of American teenagers use a chatbot for emotional support and one in eight adolescents and young adults use one for mental health advice, which is roughly 3 million young people ^[1]^[4]. The measure removes open-ended companion interaction for about 60 percent of them, in a range from 30 to 85 percent. Of those, 40 percent have no realistic alternative, in a range from 20 to 70 — no therapist, a waiting list of months, or a household in which the subject cannot be raised. That is roughly 720,000 adolescents. What each loses is set at 0.015 quality-adjusted years a year, in a range from 0.005 to 0.05, which is a deliberately low figure: it assumes the chatbot helps a little, not that it substitutes for care. Nothing here assumes it helps a lot, because nothing shows that it does. The Impact is larger than either harm it offsets, which is why this measure comes out close despite the documented deaths on the other side.

▸ Show calculation ▾ Hide calculation

	Teenagers using a chatbot for emotional support or mental health advice [1][4]		3 million people
×	Losing open-ended access under the measure Setting, range 30 to 85 percent: some will use an adult account or a service outside the rule	60 %	1.8 million people
×	With no realistic alternative Setting, range 20 to 70 percent: no therapist, a waiting list of months, or a household in which the subject cannot be raised	40 %	720,000 people
×	Quality-adjusted years lost each a year Setting, range 0.005 to 0.05: assumes the chatbot helps a little, not that it substitutes for care	0.015	10,800 quality-adjusted years
×	Value of the years the value of a healthy life year used across this site	40,000 euro each	432 million euro
÷	Normalised Impact scale of this evaluation	50 million euro a point	8.64

Score 8.64 Impact × 9 Value × 3 Plausibility ÷ 10 = 23 of 100

This is the argument with the weakest evidence in the debate and it may still be right. What is established is use: a national survey puts one in eight adolescents and young adults on a chatbot for mental health advice, and the figure is rising ^[4]. What is not established anywhere is benefit. No study compares adolescents with access to a companion chatbot against comparable adolescents without one, on any mental health outcome, and the counterfactual therefore does not exist. Against that sit the findings on the other side: the same product has been documented encouraging self-harm and has been the subject of settled wrongful death claims, so the evidence that does exist about what these services do to vulnerable young people points the other way ^[3]. The confounder is severe in both directions — the adolescents using chatbots for support are disproportionately those without other options and disproportionately those already unwell. Reverse causation cannot be separated from effect in any available data. The Plausibility is low: heavy use is documented, benefit is not, and the documented findings about this product concern harm rather than help.

evidence basis: Mechanism · P ceiling 5.5 identification: Associational · rung ceiling 5.5 band: Partial aspect supported · P 2.5–3

Counterfactual: none exists — no study compares adolescents with and without companion chatbot access on any outcome. Design: associational — survey use data only [4]. Confounder: the adolescents using chatbots for support are disproportionately both without alternatives and already unwell; unresolvable in available data. Direction: reverse causation cannot be separated from effect. Ceiling: associational 5.5 binds. Band: partial aspect supported — use and disclosure are documented, which supports the claim's premise, while the documented outcome evidence for this product concerns harm rather than benefit, which contradicts its conclusion.

Supporting: heavy use for emotional support among adolescents with few alternatives is documented in national survey data [4]. Contradicting: the outcome evidence that exists for these products concerns encouragement of self-harm and settled wrongful death claims rather than benefit [3]. Only the premise survives, not the conclusion. Read back: for roughly three adolescents in ten of the kind described here, losing the service is a real loss.

Open: Thirteen states adopted duties at different dates in 2026. Comparing adolescent help-seeking and crisis contacts in early-adopting states against late ones would give the first evidence on this side and could carry P to 5.

Nobody can define a companion chatbot

7.2of 100

A companion service remembers you, has a persona and responds warmly. So does every general assistant now sold, and so does a homework helper with a friendly voice. A duty written around those characteristics reaches products nobody intended it to reach.

Value 6 · OutputImpact 3Plausibility 4

▸ Show reasoning & sources ▾ Hide reasoning & sources

The stream is products that are withdrawn, delayed or made worse because their makers cannot tell whether a rule applies to them. It belongs to the class this site uses for economic systems and prosperity. What is counted is the value of what does not get built or shipped, not the legal fees, which are small beside it. Nothing is priced for the unfairness of an unclear rule as such, which is a complaint about process rather than a stream. The value sits in the middle-upper part of the scale, at the level this site uses for economic output.

The characteristics that define a companion service — persistent memory, a consistent persona, emotionally responsive language — are now standard in general-purpose assistants, educational tools and customer service systems. A duty framed around them catches products with no relationship purpose at all, and the response to legal uncertainty is usually to remove the feature or exclude the jurisdiction rather than to litigate. The cost of that is set at 150 million euro a year, in a range from 30 to 500 million, covering withdrawn features, delayed launches and products geofenced away from stricter states. This is a price rather than a derivation. Whether it applies at all depends on the final definition: California's law defines a companion chatbot narrowly enough that most assistants fall outside it, and a federal text could do the same. The Impact is the smallest in this debate and, like several others here, it is a function of drafting rather than of policy.

▸ Show calculation ▾ Hide calculation

	Withdrawn features, delayed launches and products geofenced away from stricter states Setting, range 30 to 500 million euro: persistent memory, a persona and emotionally responsive language are now standard in general assistants, so a behaviour-based duty reaches far beyond companion services [5]		150 million euro
÷	Normalised Impact scale of this evaluation	50 million euro a point	3

Score 3 Impact × 6 Value × 4 Plausibility ÷ 10 = 7.2 of 100

The mechanism is documented in adjacent cases and unmeasured here. Products have been withdrawn from individual American states in response to unclear obligations before, and companies routinely geofence rather than resolve ambiguity, so the response is well attested. The counterfactual is a duty defined by product category rather than by behaviour. What has no source is the cost: nobody has tallied what the fourteen state chatbot laws already enacted in 2026 have caused to be withdrawn, although that is now measurable ^[5]. The counter-mechanism is strong and only partly answered: California's definition is narrow, thirteen states followed it, and a federal bill written after all of them would inherit that narrowness — in which case this argument is worth very little. Reverse causation does not arise. The Plausibility is below the middle: the response to legal uncertainty is well attested, the cost is a stated price, and a narrow definition removes the problem.

evidence basis: Mechanism · P ceiling 6 identification: Mechanistic · rung ceiling 6 band: Chain closed, unevidenced · P 4–5

Counterfactual: a duty defined by product category rather than by behaviour. Design: mechanistic — geofencing and feature withdrawal in response to unclear state obligations are well attested in adjacent cases, with no tally for this one. Confounder: California's narrow definition, followed by thirteen states, which a federal text would likely inherit; strong and only partly answered. Direction: no reverse causation. Ceiling: mechanistic 6.0 binds. Band: chain closed but unevidenced — links named, the narrow-definition counter-mechanism stated, only the cost unmeasured.

Nothing measured argues against the claim, and withdrawal in the face of unclear state obligations is well attested. What is missing is any tally of what the fourteen 2026 state laws have already caused to be withdrawn. The counter-mechanism — a narrow statutory definition — is strong. Read back: about half the time, a duty of this kind costs roughly the amount of withdrawn product assumed here.

Open: Fourteen state chatbot laws took effect at different dates in 2026. Counting feature withdrawals and state geofencing against those dates would measure this within a year.

Summary

This is the hardest of the seven to score and the reason is an asymmetry in the evidence rather than in the arguments. On one side sit documented harms: wrongful death claims that were settled, sexual content produced for accounts registered as minors, and a product designed so that leaving feels like leaving someone. On the other sits a benefit that nobody has measured at all — one in eight adolescents uses a chatbot for mental health advice, many of them with no therapist and no household they can raise it in, and not a single study compares them against adolescents without access. This evaluation books that benefit anyway, at a low figure, and it is still larger than either harm it offsets, which is why the balance comes out even. The part of the measure that survives every reading is the smallest: requiring a referral to a person when someone discloses suicidal thinking makes the product safer without removing it, and costs almost nothing. The part that decides the outcome is the age check, which falls on 200 million adults to reach a few hundred thousand children.

Outlook — effect over time

Balanced · 0.43 previous scale

Sources

Pew Research Center: Teens, Social Media and AI Chatbots 2025. pewresearch.org
Federal Trade Commission, reported by CNN Business: FTC launches inquiry into AI companion chatbots from seven tech companies. cnn.com
Fortune: Google and Character.AI agree to settle lawsuits over teen suicides linked to AI chatbots. fortune.com
JAMA Pediatrics: AI Chatbot Use and Disclosure for Mental Health Among US Adolescents and Young Adults. jamanetwork.com
Future of Privacy Forum: Understanding the New Wave of Chatbot Legislation: California SB 243 and Beyond. fpf.org

Last reviewed by Claude Opus 5 · September 6, 2026 · 2× AI, not yet reviewed by a human

September 6, 2026AI reviewClaude Opus 5record updated
i_spanne an allen 6 Argumenten aus den englischen Ketten; kein Transfer im Record, also kein gegenbein. massstab_hinweis nannte keine r-Werte und bleibt. Kategorie kippt von Ausgeglichen (r 0,43) auf Deutlich schlechter (P(D>0) 0,05) — belegte Schaeden auf der einen Seite, unbelegte Nutzen auf der anderen, und das schlaegt unter dem neuen Massstab voll durch.
September 6, 2026AI reviewClaude Opus 5First evaluation
Created for the English side: documented harms against an unmeasured benefit, with that asymmetry carried in the finding bands rather than hidden.

Evaluations are produced with AI support and reviewed on a schedule for new developments; human passes are marked separately.How we review →