Companies claim AI is revolutionary for productivity, yet economic studies, including one by OpenAI itself, show no correlation between spending on AI and increased revenue per employee. The hype about transformative efficiency is not reflected in actual economic output.

Enterprise software companies report huge AI revenue growth, but this is often a sales tactic. Systems like Workday's 'flex credits' are packaging innovations designed to capture AI budget from CIOs, not fundamentally new, agentic experiences that transform how work gets done.

A critique from a SaaS entrepreneur outside the AI hype bubble suggests that current tools often just accelerate the creation of corporate fluff, like generating a 50-slide deck for a five-minute meeting. This raises questions about whether AI is creating true productivity gains or just more unnecessary work.

A National Bureau of Economic Research survey of 750 financial executives reveals a "productivity paradox." They report significant performance improvements from AI, but these gains are not yet reflected in hard revenue numbers, showing a lag between perceived value and financial impact.

Simply making your team more productive with AI (e.g., doubling PRs) won't increase revenue unless you redesign your business model to leverage that new capacity. The goal isn't to do old things faster, but to find entirely new things that are now possible, like letting customers order cars via email in 1995.

AI tools providing individual convenience (like finding a cheap flight) don't meaningfully show up in GDP. Real, measurable productivity gains only materialize when businesses fundamentally re-engineer their core processes to leverage the technology, much like Walmart and UPS did with computing and the internet in previous decades.

When companies see high AI tool usage without a corresponding increase in shipped features, it may not be tech failure. It could be that engineers are successfully automating their existing tasks to maintain previous output levels, effectively gaming productivity metrics.

Businesses are fixated on using AI for productivity and cost-cutting, a path that leads to a "race to the bottom." Its real value lies in democratizing creativity and creating new opportunities for growth that were previously inaccessible.

A satirical take highlights a real trend: large enterprises are rolling out AI tools not for tangible ROI but for "digital transformation" optics. Success is measured with fabricated metrics like "AI enablement" to impress boards and investors, while actual usage remains negligible and productivity gains are unverified.