T19

The reflexivity claim challenges the default assumption that a tactic with high conversion rates today will maintain those rates in the future. This is well-understood conceptually but systematically underweighted in practice because (a) attribution systems measure current-period performance, not trend, making decay invisible until it is significant; (b) organizational incentives reward finding tactics that work, not anticipating when they will stop working; (c) the rate of imitation is unobservable — you cannot directly measure how many competitors are adopting your tactic.

George Soros's reflexivity principle applied to GTM: when a tactic produces above-average results, it attracts imitation. Imitation increases the density of the tactic in the channel, which increases buyer resistance (inbox fatigue, ad banner blindness, playbook detection), which reduces the conversion coefficient for all practitioners of the tactic. The decay is toward the market mean: the tactic was earning excess return (alpha above the baseline conversion rate); imitation competes away that alpha until the tactic earns the market baseline. Examples of the reflexivity cycle: (a) cold email personalization via GPT: early movers (2022) saw response rates of 5-8%; as GPT personalization became standard (2023-2024), response rates fell to 1-2% because recipients recognized and discounted the pattern; (b) LinkedIn video DMs: 10-15% response rates for early adopters (2021); <3% as they became ubiquitous; (c) intent-data targeting: high-alpha when only a few vendors had access; lower alpha as intent data became commoditized. The actionable form: each tactic has a reflexivity half-life — the time for imitation to compress alpha by 50%. Typical half-lives: email tactics 12-18 months, channel tactics 18-36 months, motion-level tactics 3-5 years.

• Salesloft data: median reply rates for cold outbound email fell from 8.1% (2015) to 1.8% (2023) — an 78% decline over 8 years, consistent with systematic reflexivity as the channel became saturated
• LinkedIn response rates to InMail: declined from 20%+ (2016) to 5-8% (2023) as volume increased — the same reflexivity pattern on a different channel
• GPT personalization adoption cycle: BenchmarkONE and Lavender data show the personalization-lift from AI-generated first lines dropped from ~30% additional opens (2022) to ~5% (2024) as adoption spread
• Andrew Chen's 'The Cold Start Problem': the best user acquisition tactics for apps show consistent alpha decay as imitators adopt them — the same mechanism in consumer GTM

• Some channels and tactics are structurally scarce (direct executive referrals, co-selling with a major platform partner) and resist reflexivity because they cannot be imitated at scale
• Tactics with high minimum-viable-quality thresholds (category-level thought leadership, analyst relationships) decay more slowly because most imitators cannot meet the quality bar
• Market expansion can offset reflexivity: if the addressable audience grows faster than imitation rate, alpha can persist even as the tactic is copied

For each active tactic, track the trailing 4-quarter trend in its conversion coefficient, not just the current level. If the trend is declining without a corresponding change in target audience, you are observing reflexivity decay. Maintain a 'next tactic' pipeline: for every current primary tactic, have an experimental alternative running at low volume that you can scale when the primary tactic decays. The competitive advantage in GTM is consistently being 12-18 months ahead of widespread adoption on new tactics — which requires systematic experimentation, not just optimizing current performance.