RadHash Investor Memo

August 30, 2025

Executive Summary

Startups are shipping faster than ever on AI-assisted, low/no-code scaffolding—then paying a massive “rebuild tax” at scale. Modernization projects routinely cost ~$1.5M and fail at alarming rates, while tech debt diverts 10–20% of new-product budgets and can equal 20–40% of the tech estate’s value. RadHash offers a self-hosted, AI-native foundation that ships ~95% of the repetitive stack and lets builders customize the last 5%, eliminating rebuilds and making scale the default. DevOps.com CIO Dive McKinsey

Thesis: speed without durability is breaking startups

The baseline fragility of new companies is well established: roughly one in five die within a year, and about half don’t make it to year five (BLS). That fragility gets amplified when teams sprint to MVP on tools that can’t carry real workloads, complex integrations, or security/compliance—forcing costly rewrites at the worst possible time. Bureau of Labor Statistics

The market is openly acknowledging this shift: we’re “entering the fast fashion era of SaaS,” as Sam Altman put it—apps created and discarded in rapid cycles. The speed is real; the durability often isn’t. X (formerly Twitter)

Problem: the rebuild tax and compound drag of tech debt

Application modernization projects average ~$1.5M and commonly run 16 months; many fail. That is the bill for cutting corners on foundational architecture. DevOps.com CIO Dive

Meanwhile, tech debt silently diverts 10–20% of the new-product budget and is estimated at 20–40% of total tech-estate value—an anchor on velocity just as teams should be compounding growth. McKinsey & Company

Independent research on low/no-code also documents limits at enterprise scale—customization, debugging, integration, and performance challenges—exactly the failure modes that trigger rebuilds under load. arXiv KPMG Assets

CB Insights’ post-mortems continue to show “no market need,” “ran out of cash,” and “team issues” among top failure reasons; brittle tech magnifies each. CB Insights

Solution: a self-hosted, AI-native foundation you own (not rent)

RadHash ships a composable, self-hosted foundation that eliminates ~95% of repetitive software work and leaves the last 5% for your differentiation. You get: secure infra, identity, data, orchestration, payments, marketplaces, AI/agent tooling, one-click deploy, auto-scaling—without surrendering control or planning a rewrite later. (Internal description.)

Why now? Agentic AI is moving from concept to enterprise practice. Autonomy, orchestration, and governance are becoming core architectural requirements—demanding durable foundations, not disposable demos. McKinsey & Company

(Definition/context) Agents are software entities that perceive, decide, and act toward goals—already recognized by major vendors and analysts as a next competitive frontier. Amazon Web Services, Inc. Forrester

What RadHash unlocks

• Scale from day one: infra-first design avoids the late-stage tear-down.

• Ownership & sovereignty: self-hosted stack; you keep the code, data, and margins.

• Agent-ready orchestration: build automations across systems with security and observability built in.

• Economic compounding: usage + payments + marketplace flows align incentives across your ecosystem.
  (Internal positioning.)

Validation and signals

• Cost reality: modernization averages ~$1.5M; many projects fail—consistent with the “rebuild tax” RadHash eliminates. DevOps.com CIO Dive

• Debt reality: 10–20% of new-product budgets lost to tech debt; tech-estate impact 20–40%. McKinsey & Company

• Method reality: literature highlights low/no-code challenges at scale (customization, debugging, integration, performance). arXiv

• Market timing: agentic systems are a named 2025 frontier; governance and orchestration are decisive capabilities. ForresterMcKinsey & Company

Internal traction (for data room): LOIs, customer count across countries/industries, MoM growth, and pilot deployments on Rad tech. (Internal; share via data-room links.)

Go-to-Market (how this scales efficiently)

• Freemium entry point: frictionless onboarding lowers barriers and accelerates initial adoption.

• Residual economics: transaction-based flywheel creates durable, compounding revenue streams.

• Viral propagation: adoption spreads founder-to-founder, partner-to-partner, and across startup ecosystems globally.

Risks & mitigations (addressed head-on)

• Developer trust in a “new stack.” Mitigation: self-hosted model, compatibility with mainstream clouds/tooling, and live references. Analysts emphasize agent governance—our architecture aligns with that guidance. McKinsey & Company

• Category noise (no/low-code hype). Mitigation: position infra-first, code-owning, agent-ready; point to research on low/no-code limitations at scale. arXiv

• Capital discipline in GTM. Mitigation: partner-led expansion keeps CAC efficient; usage + payments align revenue with adoption (internal).

Phased upside (how value compounds)

1. Early adopters → immediate stickiness: avoid $1.5M modernization cycles; own the stack from day one. DevOps.com

2. Ecosystem flywheel: each startup brings users, partners, transactions, and data; agentic workflows increase operating leverage. McKinsey & Company

3. Network effects: repeatable channel motion across startup ecosystems creates distribution that’s hard to dislodge.

4. Category creation: infra for the agentic startup economy—durable, sovereign, automation-ready (external trend + internal moat). Forrester

Why this team

Operator-led with deep infra and product experience (20+ years, multiple exits, 100+ deliveries). We’ve lived the rebuild problem and designed RadHash so founders never face it again. (Internal summary.)

The ask

Join us to make scale the default for the next wave of AI-native startups. We raising pre-seed on SAFE to bridge scale-out operations with EBITA positive milestones.

Sources (key validations)

• Business survival (BLS): survival by year and five-year trajectories. Bureau of Labor Statistics

• Modernization cost & failure: $1.5M average; long timelines; high failure incidence. DevOps.com CIO Dive

• Tech debt drag (McKinsey): 10–20% budget diversion; 20–40% of tech-estate value. McKinsey & Company

• Low/no-code limitations: customization/debugging/integration/performance challenges at scale. arXiv

• Agentic AI trend: competitive frontier and governance imperatives. ForresterMcKinsey & Company

• “Fast fashion era of SaaS” reference: Sam Altman post. X (formerly Twitter)

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