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▶ 25:50 · Enterprise AI Adoption · Model capabilities far exceed enterprise absorption capacity; context provision is the new bottleneck

episode shorts · 1

How to build with AI in 2026

now playing · Enterprise AI Adoption

Fintechtailwindscore 8/10jd ross

AI-native risk management disrupting $200-400B insurance brokerage fee pool

Traditional brokers extract 10-15% of trillions in premiums via fragmented producer model; AI platform automates policy placement, claims, and compliance, delivering 10-30% savings while ex…

AI Coding Agentstailwindscore 9/10jd ross

Engineers manage fleets of AI agents, software build cost asymptotically approaching zero

Full-time engineers no longer write code; they direct multiple agents (Codex, Cloud Code, Cursor) that oneshot solutions from tickets, collapsing the build-measure-learn loop from months to…

AI Infrastructureheadwindscore 8/10jd ross

Foundational model companies will eat top-end SaaS, middle commoditizes, eventually everyone builds custom software

Anthropic, OpenAI, and Google will capture highest-value SaaS categories by targeting high-token-usage areas; mid-market SaaS commoditizes, and within 5 years non-technical workers will one…

Enterprise AI Adoptionmixedscore 7/10jd ross

Model capabilities far exceed enterprise absorption capacity; context provision is the new bottleneck

Frontier models are already more capable than most enterprises can adopt; the limiting factor is feeding models the right context and building evaluation loops, not model intelligence.

Cybersecurityriskscore 7/10jd ross

AI voice/email agents will flood communication channels, forcing verified identity and double opt-in systems

ElevenLabs-style human-sounding agents enable mass personalized spam/scams; trust infrastructure must shift to cryptographic verification, double opt-in, or paid access to preserve signal.

AI Economics & Business Modelstailwindscore 8/10jd ross

Jevons paradox in engineering: cheaper code increases demand for builders 10x, eliminates routine support roles

As feature build cost drops from $100k to $20k, demand for creative engineers explodes while customer-support email roles collapse; returns accrue to those who can define and direct new cap…

←

▶ 25:50 · Enterprise AI Adoption · Model capabilities far exceed enterprise absorption capacity; context provision is the new bottleneck

episode shorts · 1

How to build with AI in 2026

now playing · Enterprise AI Adoption

Fintechtailwindscore 8/10jd ross

AI-native risk management disrupting $200-400B insurance brokerage fee pool

Traditional brokers extract 10-15% of trillions in premiums via fragmented producer model; AI platform automates policy placement, claims, and compliance, delivering 10-30% savings while ex…

AI Coding Agentstailwindscore 9/10jd ross

Engineers manage fleets of AI agents, software build cost asymptotically approaching zero

Full-time engineers no longer write code; they direct multiple agents (Codex, Cloud Code, Cursor) that oneshot solutions from tickets, collapsing the build-measure-learn loop from months to…

AI Infrastructureheadwindscore 8/10jd ross

Foundational model companies will eat top-end SaaS, middle commoditizes, eventually everyone builds custom software

Anthropic, OpenAI, and Google will capture highest-value SaaS categories by targeting high-token-usage areas; mid-market SaaS commoditizes, and within 5 years non-technical workers will one…

Enterprise AI Adoptionmixedscore 7/10jd ross

Model capabilities far exceed enterprise absorption capacity; context provision is the new bottleneck

Frontier models are already more capable than most enterprises can adopt; the limiting factor is feeding models the right context and building evaluation loops, not model intelligence.

Cybersecurityriskscore 7/10jd ross

AI voice/email agents will flood communication channels, forcing verified identity and double opt-in systems

ElevenLabs-style human-sounding agents enable mass personalized spam/scams; trust infrastructure must shift to cryptographic verification, double opt-in, or paid access to preserve signal.

AI Economics & Business Modelstailwindscore 8/10jd ross

Jevons paradox in engineering: cheaper code increases demand for builders 10x, eliminates routine support roles

As feature build cost drops from $100k to $20k, demand for creative engineers explodes while customer-support email roles collapse; returns accrue to those who can define and direct new cap…