A long-held software engineering law, the 'mythical man-month,' stated that adding money or people to a project wouldn't speed it up. AI has changed this fundamental rule. Elon Musk's xAI proved you can now 'throw money at the problem' to rapidly catch up on a technological lead.

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.

For 50 years, adding engineers didn't speed up software development, giving startups a defensible head start. AI changes this. With proprietary data and massive GPU resources, large incumbents can now 'throw money at the problem' to close gaps quickly, eroding a first-mover advantage.

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.

Historically, a developer's primary cost was salary. Now, the constant use of powerful AI coding assistants creates a new, variable infrastructure expense for LLM tokens. This changes the economic model of software development, with costs per engineer potentially rising by dollars per hour.

The focus in AI has evolved from rapid software capability gains to the physical constraints of its adoption. The demand for compute power is expected to significantly outstrip supply, making infrastructure—not algorithms—the defining bottleneck for future growth.

Poolside, an AI coding company, building its own data center is a terrifying signal for the industry. It suggests that competing at the software layer now requires massive, direct investment in fixed assets. This escalates the capital intensity of AI startups from millions to potentially billions, fundamentally changing the investment landscape.

Software companies are using AI tools internally to boost employee productivity. This means future operating expense (OpEx) growth may depend less on the high cost of hiring talent and more on the cost of compute, which is trending downwards. This represents a fundamental shift in the industry's cost structure.

Historically, labor costs dwarfed software spending. As AI automates tasks, software budgets will balloon, turning into a primary corporate expense. This forces CFOs to scrutinize software ROI with the same rigor they once applied only to their workforce.