We are currently in a massive boom and crack cycle. Companies are scaling LLM usage at an unsustainable rate, only to realise that every token has a literal cost that product margins were never designed to absorb.
You cannot simply hike the price of a legacy service because internal development costs have increased. Instead we see the “added service” pivot. Products suddenly gain Intelligent Search or an AI Chat feature to justify rapidly growing Azure bills.
We are effectively paying for a silicon workforce that costs as much as a human one, but without the same long-term value.
The Developer Apocalypse, which was supposed to arrive next year, has been predicted for years. This ploy by various AI founders to fill the market with fear and accelerate adoption is working.
Many companies responded by firing competent engineers to subsidise their LLM spend.
But there is a massive gap between generating code and owning a product.
To replace even a junior developer, an LLM must be consistently at least 80% accurate, 90% of the time. Even then the real barrier is accountability.
An engineer can be fired for negligence. An LLM’s maximum ownership is a prompt fix. A million-dollar product cannot run on a tool that takes zero responsibility for a production outage. This creates a tightrope for leadership.
Do you pay for the brain or pay for the compute?
Senior developers come with higher salaries but also with a software pedigree that allows them to use models surgically. With the right strategies, a senior engineer can reduce token consumption dramatically.
Junior developers cost less in salary but often rely on brute-force prompting and repeated API calls to get things done. Token usage quietly climbs. In many teams it reaches ₹80,000 per month per seat.
At that point the difference between a junior developer’s salary and a senior developer’s salary begins to thin.
The solution is changing how AI is accounted for.
Instead of an unlimited all-you-can-eat API budget, developers receive a baseline compute allowance per sprint. If a developer meets 100% of their delivery goals while saving on cloud tokens through optimized prompts, local models, or old-school code smooshing, they receive a portion of the savings as a performance bonus.
This creates an incentive structure that rewards efficiency.
Consider a simple example. Assume a monthly LLM compute allowance of ₹1,00,000 per developer. The way a developer integrates LLMs into their workflow can dramatically change the cost profile of the team.
| Developer Profile | Strategy | Token Burn | Savings | Bonus |
|---|---|---|---|---|
| Unstructured LLM Usage | Uses LLM for most tasks with little optimisation | ₹95,000 | ₹5,000 | ₹2,500 |
| Optimized Workflow | Uses LLM selectively with caching and prompt optimisation | ₹40,000 | ₹60,000 | ₹30,000 |
| Seasoned Engineer | Manual coding with targeted LLM assistance | ₹10,000 | ₹90,000 | ₹45,000 |
Developers begin thinking like systems engineers again instead of prompt gamblers. Consistent overspending does not automatically mean a developer is bad. It usually means the developer needs better training in cost-efficient tooling.
At the same time, companies need to rethink how vibe-coded contributions enter a product.
Rethink vibe coding does not mean discouraging it. It means structuring it.
Companies can organise vibe coding hackathons and open code sessions where vibe coders are paired with experienced engineers to build interesting experiments. Weekly camps for vibe-coded ideas allow developers and non-developers to collaborate and produce prototypes without directly modifying the production codebase.
Some will call this gate-keeping. But the primary product of a company should be gate-kept. A million-dollar product is not a democracy.
The core codebase is a mission-critical machine. It must be built on a foundation of human accountability and architectural discipline.
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