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T7

The claim: Fit and switching costs may modify GTM response. Universal multiplication, a sharp switching threshold and additive moat revenue are competing unvalidated models, requiring independent pre-outcome measures and scoped tests.
contested load-bearing Model updated 2026-10-05

Evidence boundary

See the scoped evidence_register and qualification_protocol. Structural plausibility, descriptive surveys and laboratory benchmarks do not alone identify a local commercial policy effect.

Why this claim matters

See the scoped evidence_register and qualification_protocol. Structural plausibility, descriptive surveys and laboratory benchmarks do not alone identify a local commercial policy effect.

The mechanism

Switching costs can affect renewal and rents. A sharp regime threshold, an exponential hazard and additive revenue form are proposed specifications, not identified consequences of lock-in theory. GRR plus low satisfaction does not identify switching costs, and retained enterprise revenue is not an existence proof of an additive causal law. Compare specifications with independent fit/switching-cost measurements and interventions.

Evidence for

  • G2 — The Answer Economy — 15 April 2026. Primary report. Commercial survey; methods/findings inspected. March online sample of 1,076 B2B decision makers and 39 marketer interviews across regions. Reports AI research alongside continued Google use and frequent inaccuracies. Source: https://learn.g2.com/g2-2026-ai-search-insight-report
  • Oded Netzer and Rajan Sambandam — Synthetic Data in Marketing Research How to Evaluate and When to Trust — 12 September, revised 19 September 2026. Record and PDF. Full methods inspected. Real 2025 US sample of 3,063 people, 18 sectors, 108 questions; grounded leave-one-question-out prediction and out-of-bag screening. Rich category histories improve agreement over demographics alone; 46 retained questions perform better than all questions. Source: https://arxiv.org/abs/2609.13995
  • Kuric, Demcak and Krajcovic — Distorted Perspectives of LLM Simulated Preferences — 18 May 2026. Full preprint. Twenty-nine actual UX studies, 2,073 people, 78 tasks. GPT-4.1's top choice matches people in 53% of tasks; GPT-5.2 reaches 65% without an established general distribution-fit gain. Source: https://arxiv.org/html/2605.18311v1
  • Leonard Kinzinger and Jochen Hartmann — Synthetic Personalities — 3 June 2026. Full preprint. Five hundred SOEP individuals, 183 held-out items, three models and 2.1 million generated responses. Grounding and preserving original responses help; reasoning improves ordering more reliably than closeness. Source: https://arxiv.org/html/2606.04592v1
  • Stephen France and Pia Albinsson — Synthetic Consumer Insight Generation with Large Language Models — 7 July 2026. Record and full PDF. Full comparative methods inspected. Word associations and completions about five US cities, 173 students, six LLMs and varied prompting/seeding. Broad topics overlap while synthetic style differs. Source: https://arxiv.org/abs/2607.05761

Evidence against / limitations

  • Classical switching-cost mechanisms motivate the hypothesis; this exact piecewise GTM revenue model has not been fitted or causally tested here.
  • reported frequency and AI-attributed switching are not observed journey shares or causal switching. Sampling/weighting and question bases constrain universal adoption estimates. C1/C2/C3/C8.
  • selected in-domain survey predictions, not novel procurement decisions. Question-level agreement differs from individual fidelity; generated replicas add no independent people. Commercial research affiliation disclosed. C1/C2/C6/C8.
  • specific models/protocols and proprietary UX data, no procurement or transactions. Persona elaboration and reasoning do not consistently repair validity. C1/C2/C8.

So what: the operator implication

GRR plus low satisfaction does not identify switching costs, and retained enterprise revenue is not an existence proof of an additive causal law. Compare specifications with independent fit/switching-cost measurements and interventions.

Related theses

All theses

How to cite this

@misc{shalvi_gtm_thesis_t7_2026,
  author = {Singh, Shalvi},
  title  = {GTM World Model Thesis T7},
  year   = {2026},
  url    = {https://shalvisingh.com/gtm/theses/t7}
}

Singh, Shalvi. "GTM World Model Thesis T7." shalvisingh.com, 2026. https://shalvisingh.com/gtm/theses/t7