Left Curve Solutions: Avoid Mid‑Curve Overengineering

The Good Stuff, with Pete and Andy - Episode 23: Left Curve Solutions and AI Implementation Challenges

Hosts: Pete (from Madeira) and Andy (from Perth)

Episode Overview: Pete and Andy explore the challenges with AI tooling reliability, the philosophy of "left curve" solutions over complex engineering, and why most enterprise AI pilots are failing. The discussion covers Nostra protocol development, business implementation strategies, and the ongoing debate between bionic human versus human-at-the-edge AI integration models.

Key Discussion Points:

**Nostra Protocol Development and Decentralized Applications (04:00-25:00)**

Pete shares updates from Madeira on building decentralized applications using Nostra protocol

Discussion of "bring your own database" concept - Nostra KV Connect for private company data

Benefits of distributed data storage versus centralized honeypots vulnerable to hacking

Identity portability and cryptographic authentication as core value propositions

**Craig David Bot Demo Project (22:00-25:00)**

Pete's weekly demo challenge: building a bot that analyzes your week using Nostra events

Integration with payment systems and automated research capabilities

Future plans for video generation with custom music overlays

**Claude Code Performance Issues (25:00-40:00)**

Widespread reports of Claude Code degradation affecting productivity across the development community

Anthropic's explanation of "three interlocking bugs" affecting token routing and limits

Discussion of alternatives like Wingman, Goose, and maintaining control over AI tooling stack

The shift from 100x productivity back to 90x productivity as a "first world problem"

**Left Curve Solutions Philosophy (40:00-46:00)**

Concept borrowed from Sovereign Engineering: avoid mid-curve over-engineering

Examples from Bitcoin e-cash development where simpler solutions (Fedimints, eCash) succeeded

The tendency to over-complicate due to ego and "tall poppy syndrome"

Focus on minimal viable approaches rather than proving technical sophistication

**Enterprise AI Pilot Failure Analysis (46:00-58:00)**

MIT study showing 95%+ failure rate for AI pilots in large organizations

Root causes: poor scoping, misunderstanding of technology capabilities, bureaucratic implementation

Lack of genuine problem identification and systems thinking approach

The consulting industrial complex extracting fees without delivering value

**ROI Measurement Challenges (58:00-1:05:00)**

Traditional additive ROI models don't capture compounding AI benefits

Example: document review automation leading to faster deals, new service offerings, market expansion

Need for longer-term measurement frameworks beyond quarterly reporting cycles

Hidden individual AI usage versus official pilot programs

**Build vs Buy Strategy Discussion (1:05:00-1:12:00)**

Opportunity to capture market share while incumbents struggle with implementation

Time advantage for building AI-native solutions while large companies run ineffective pilots

Path dependency question: bionic human as stepping stone to human-at-the-edge models

Role-dependent optimal human placement in AI-augmented workflows

**Implementation Philosophy**

"Full left curve solutions" - prioritizing simplicity and function over complexity

"We're not here to fuck spiders" - focus on meaningful problems, not technical masturbation

The "dog with two dicks" problem - AI tools that code aggressively without proper planning