AI may be powerful.
But the human brain is still the most energy-efficient thinking machine we know.
Also, when I eat peanuts, I know exactly how many I consumed.
With AI, I am still trying to understand whether I used one peanut, one packet, or one truckload.
This is not just a pricing problem. It is a business and user-experience problem.
Across business functions, people are now using multiple AI tools, multiple subscriptions, and multiple “usage limits” — for writing, coding, research, design, sales, marketing, support, analysis, automation, and decision-making.
Every tool promises productivity.
But every tool also introduces a new meter.
A new limit.
A new pricing model.
A new upgrade prompt.
A new point where flow can break.
As business owners, we cannot buy a bottomless pit for every person, every function, and every use case just to make sure their work is not interrupted.
And honestly, AI does not always save time for people in thinking roles.
Often, its bigger value is different.
It improves quality.
It improves options.
It improves review.
It improves consistency.
It improves the depth of thinking.
But that also means AI usage has to be managed carefully — not only as a cost-saving tool, but as a quality and capability multiplier.
That is why AI pricing feels strange from a business point of view.
We are comparing the cost of machine thinking with the cost of human thinking — but only one side is visible.
For humans, the unit economics are surprisingly elegant.
People say, “If you pay peanuts, you get monkeys.”
But evolution had the last laugh.
With the right peanuts, it produced Homo sapiens — possibly the most energy-efficient thinking machine the universe has produced so far.
The human brain runs on food, sleep, curiosity, and context.
AI runs on GPUs, cooling, tokens, premium requests, model multipliers, rate limits, and billing pages that often need another AI to explain them.
So here is my question to software developers, product leaders, founders, business owners, and AI decision makers:
What would an ideal AI pricing and usage experience look like?
Users should be able to see real-time consumption.
Tools should warn before flow is interrupted.
Pricing should be easier to understand than tokens, model multipliers, and hidden usage limits.
Business users may need simpler “effort units” instead of technical billing units.
Teams need clearer dashboards for cost, context, and limits.
Because the problem is not only whether AI is expensive or cheap.
The real question is whether AI usage is understandable, predictable, and aligned with the way humans actually work.
That is the pricing experience businesses will need as AI becomes part of everyday work.
About the Author
Bhagath Singh Karunakaran is an entrepreneur, systems thinker, and deep-tech practitioner with over two decades of experience across software, IoT, Industry 4.0, and AI-led business transformation. He is the founder of i45G, where he works with SMEs, institutions, and leaders on practical technology adoption, systems thinking, workforce readiness, and AI-enabled business transformation.
Through his writing and consulting, he focuses on helping business owners and decision-makers move beyond hype and adopt technology with clarity, ownership, and measurable value.