The cost for a given level of AI performance halves every 3.5 months—a rate 10 times faster than Moore's Law. This exponential improvement means entrepreneurs should pursue ideas that seem financially or computationally unfeasible today, as they will likely become practical within 12-24 months.

The cost to run an autonomous AI coding agent is surprisingly low, reframing the value of developer time. A single coding iteration can cost as little as $3, meaning a complete feature built over 10 iterations could be completed for around $30, making complex software development radically more accessible.

Eclipse Ventures founder Lior Susan shares a quote from Sam Altman that flips a long-held venture assumption on its head. The massive compute and talent costs for foundational AI models mean that software—specifically AI—has become more capital-intensive than traditional hardware businesses, altering investment theses.

In the AI arms race, a $10 billion investment from a trillion-dollar company is seen as table stakes. This sum is framed as the cost to secure a handful of top engineers, highlighting the massive decoupling of capital from traditional value perception in the tech industry.

Increased developer productivity from AI won't lead to fewer jobs. Instead, it mirrors the Jevons paradox seen with electricity: as building software becomes cheaper and faster, the demand for it will dramatically increase. This boosts investment in new projects and ultimately grows the entire software engineering industry.

Building software traditionally required minimal capital. However, advanced AI development introduces high compute costs, with users reporting spending hundreds on a single project. This trend could re-erect financial barriers to entry in software, making it a capital-intensive endeavor similar to hardware.

Don't view AI through a cost-cutting lens. If AI makes a single software developer 10x more productive—generating $5M in value instead of $500k—the rational business decision is to hire more developers to scale that value creation, not fewer.

Marc Andreessen observes that once a company demonstrates a new AI capability is possible, competitors can catch up rapidly. This suggests that first-mover advantage in AI might be less durable than in previous tech waves, as seen with companies like XAI matching state-of-the-art models in under a year.

Contrary to the idea that technology always gets cheaper, building on AI is less expensive now. The current phase is characterized by abundant venture capital and intense competition among AI tool providers, which subsidizes costs for developers. As the market consolidates, these costs will rise.