Regimes &
Strategy Half-Lives

The Psi macro scalar is the single variable that most changes what you should be optimizing. It encodes the capital market's current weighting of growth versus free cash flow — and it is set entirely outside your control. In ZIRP (Psi ≈ 1), growing ARR at any cost maximizes enterprise value. In a post-ZIRP FCF-focused market (Psi ≈ 0.3–0.5), growth past the Rule of X efficiency threshold is penalized. The regime shift from ZIRP to post-ZIRP repriced the entire SaaS sector in 18 months (2021–2023).

The practical failure mode is running ZIRP playbooks in a post-ZIRP world: over-investing in growth headcount, tolerating high CAC payback periods, and pricing for logo acquisition rather than Net Revenue Retention. Teams that did not update Psi in 2022 discovered the error in 2023 ARR reviews.

A tactic's conversion coefficient is highest before imitation and decays toward the market mean as it is copied — this is thesis T19: reflexivity, not drift. Cold email personalization in 2019 had a 3× reply-rate advantage for early adopters. By 2023, the industry average had converged to near-2019 baseline as every SDR team ran the same playbook. The half-life of a GTM strategy is an endogenous, measurable phenomenon — not exogenous noise.

This has a direct implication for planning: budget for strategy rotation, not just execution optimization. A GTM team that optimizes a single tactic to perfection will find that perfection coincides with the tactic's peak decay rate. The right response to declining coefficient performance is regime diagnosis first — is this drift (recoverable) or topology change (structural)?

A coefficient drift (e.g., cold email reply rates declining, paid CAC rising) is recoverable: shift the channel, change the message, rotate the tactic. A topology change (e.g., AI-assisted search reshaping how buyers discover vendors) rewrites the graph itself — the channels that previously drove category entry may now be structurally bypassed.

Misclassifying a topology change as drift is fatal precisely in the highest-stakes periods. In drift, you optimize harder. In topology change, you rebuild your model of how buyers find you. The correct diagnosis changes the response entirely. The 2024–2026 AI search transition is the live test case: if AI Overviews and LLM-driven research bypass traditional search-click funnels, teams treating the SEO traffic decline as a coefficient problem will exhaust budget on a structurally broken channel.

Thesis T25 is the least glamorous and most violated: name the objective or optimize incoherently. Growth, efficiency, survival, positioning, and optionality are in permanent tension. Without an explicit regime-conditional weight vector, the system silently optimizes whatever the comp plan rewards — which is almost never what the company actually needs at this stage of the cycle.

In practice, the objective function failure mode looks like: a post-ZIRP company still paying SDR comp plans calibrated for pipeline volume (ZIRP objective) while the board measures NRR and payback period (post-ZIRP objective). The field team and the executive team are optimizing different functions. The GTM system loses coherence before anyone diagnoses the cause.

Every channel has a carrying capacity and a convex CAC curve (thesis T4). The S-curve of channel adoption describes it in three phases: (1) pioneer phase — early adopter advantage, below-average CAC, above-average conversion; (2) growth phase — tactic spreads, CAC rises toward the mean, conversion compresses; (3) saturation phase — channel is a commodity, CAC exceeds LTV for the marginal buyer, reflexive decay accelerates.

Reflexivity accelerates the decay in phase 3: as more teams pile into the saturated channel, they collectively degrade the signal-to-noise ratio, which drives response rates down further, which drives more teams to add volume, which compresses rates further. This is the doom loop of saturated outbound. The optimal GTM portfolio holds a mix of channels at different S-curve positions — harvesting mature channels while seeding the next early-advantage channel before the current one tips into saturation.