AI’s Hidden Price Tag Threatens Indie Developers and Startups

AI is getting sharper, and it’s getting pricier too. Rising costs are squeezing startups and independent developers building on the latest models.

According to the Wall Street Journal, the price per unit of AI is falling, but the surge in complex tasks means developers are burning through far more tokens. What was billed as the race to build the “smartest AI” is fast becoming a contest over who can shoulder the most expensive systems.

Token drain turns simple tasks into costly workloads

Even basic AI requests can consume hundreds or thousands of tokens, while advanced tasks run far higher. Data from the WSJ shows:

• Basic chatbot Q&A: 50 to 500 tokens.
• Short document summary: 200 to 6,000 tokens.
• Basic code assistance: 500 to 2,000 tokens.
• Writing complex code: 20,000 to 100,000+ tokens.
• Legal document analysis: 75,000 to 250,000+ tokens.
• Multi-step agent workflow: 100,000 to 1 million+ tokens.

Startups face mounting bills

For smaller developers, the costs are escalating quickly. Users of Cursor, a coding tool, have seen their monthly credits vanish within days after the company updated its pricing.

Replit has introduced what it calls “effort-based pricing,” which charges more for complex tasks. The change sparked a wave of complaints on Reddit, with some users threatening to quit, even as the company insists that enterprise plans still yield margins of up to 90%.

Bigger players remain in a stronger position. Notion CEO Ivan Zhao told the WSJ that AI has cut its margins by about 10%, but the company still operates profitably at scale.

Costs run deeper than tokens. Advanced AI also demands vast computing power and energy, a burden smaller developers struggle to absorb.

Big tech’s infrastructure edge leaves smaller rivals at risk

The cost of competing in AI is climbing into the trillions. McKinsey estimates global AI infrastructure spending could hit $6.7 trillion by 2030, a scale that favors enterprises with the capital to build or buy the resources they need.

Companies with control over the full stack, from chips and computers to energy, are positioned to thrive. A Reuters analysis found that firms with their own infrastructure hold a decisive edge over smaller developers, who must rely on third-party providers, leaving them vulnerable to rising costs and supply constraints.

Larger organizations also benefit from tighter oversight of their budgets. A survey by CloudZero found AI spending is growing by more than a third year over year, with many companies already paying over $100,000 a month. Enterprises can track costs and measure returns with dedicated tools, a capability that is often out of reach for most startups.

Will survival in the AI race depend on who can pay?

For many developers, the path forward is uncertain. Some are turning to smaller, open-source models to cut costs, while others experiment with leaner prompts to keep token use in check.

Those rising token demands, the WSJ reported, have already fueled a debate over who the winners and losers in AI will be.

For startups, survival may hinge on adapting to rising costs or risk being priced out of the AI race altogether.

The competition for AI infrastructure isn’t limited to startups. Meta has struck a major deal with Google to bolster its cloud capacity.