As AI generates vast quantities of code, the primary engineering challenge shifts from production to quality assurance. The new bottleneck is the limited human attention available to review, understand, and manage the quality of the codebase, leading to increased fragility and "slop" in production.
AI companies are achieving revenue milestones at an unprecedented rate. Data shows AI labs growing from $1B to $10B in revenue in roughly one year, a feat that took Salesforce 8-9 years. This signals a dramatic acceleration in market adoption and value creation.
The AI market has two opposing trends: a dramatic collapse in token prices for equivalent models (down 150x in 21 months) and unprecedented revenue growth. This indicates that the explosion in utilization and value creation is massively outpacing cost reductions, signaling a healthy, expanding market.
To navigate market volatility, founders should institutionalize exit strategy discussions. By pre-scheduling a board meeting once or twice a year for this topic, it becomes a routine, non-emotional strategic exercise, preventing panic-driven decisions and allowing for clear-headed evaluation of M&A opportunities.
The idea that AI will eliminate SaaS is overblown because it incorrectly projects small startup behavior onto large enterprises. Fortune 100s face immense change management, security, and maintenance challenges, making replacing established vendors with internal AI-coded tools impractical.
The SaaS-era advice to "do one thing well" is outdated and risky in the current AI climate. The best defense against rapid displacement by competitors or platform shifts is to build a multi-product bundle. This strategy creates a wider surface area within a customer's workflow, increasing stickiness and defensibility.
AI coding tools will create a cultural split in engineering teams. Engineers motivated by the utility of shipping products will feel empowered. However, those who identify as "artisanal" craftsmen, valuing the bespoke quality of their code, may struggle and become unhappy as their specific craft becomes less central.
Tech's portion of US GDP has tripled from 4% to 12% since 2005 and is projected to continue growing. This underlying economic shift, accelerated by AI converting services to software, indicates that tech's total market cap has significant room for expansion, supporting more trillion-dollar companies.
The AI era's high velocity of change, where market leaders can be displaced in 1-2 years, resembles the volatile dot-com bubble, not the last decade's predictable SaaS growth. This means founders must consider that even massive scale doesn't guarantee durability, making exit timing a critical strategic question.