As artificial intelligence adoption accelerates across industries, a new challenge is emerging behind the scenes: managing the soaring cost of using advanced AI models.

For many companies, AI spending has grown so rapidly that executives are now being forced to prioritize efficiency over access to premium systems. One startup founder says the shift has become less about optimization—and more about survival.

Startup Switched Entirely From Anthropic to DeepSeek

Earlier this month, Flo Crivello, CEO of AI startup Lindy, made a dramatic move to cut operational expenses.

The company migrated all of its AI traffic away from Anthropic’s Claude models and shifted fully to DeepSeek, a Chinese AI developer known for lower-cost open-weight alternatives.

According to Crivello, the financial impact was immediate.

“We did it, and you could see that cost curve go down — crash to the ground,” he said while speaking from Lindy’s headquarters in San Francisco.

The 34-year-old founder expects the decision to save the company millions of dollars over the coming months.

Despite those reductions, Crivello said AI remains one of Lindy’s largest expenses — even exceeding payroll costs for its team of roughly 25 employees.

“It’s a matter of survival for the business,” he said.

AI Costs Are Becoming a Major Corporate Concern

Since the launch of ChatGPT in late 2022 triggered global interest in generative AI, businesses have rapidly integrated AI into areas including:

Customer support
Marketing automation
Financial operations
Software development
Internal productivity tools

But as adoption expanded, operating costs increased just as quickly.

Many organizations now face significant expenses tied to:

Model inference usage
Token consumption
AI infrastructure
Developer experimentation
Large-scale automation workflows

In some cases, annual AI spending has reportedly reached hundreds of millions—or even billions—of dollars across the technology sector.

The Rise of “Tokenmaxxing” and AI Usage Culture

One area where spending accelerated particularly fast was AI-assisted software development.

Developers increasingly began using AI systems to generate code, build products, and automate engineering workflows that traditionally required larger teams.

This gave rise to internal trends sometimes described as:

Tokenmaxxing — maximizing AI usage without strict spending limits
AI leaderboards — measuring and rewarding heavy AI adoption

While these approaches boosted experimentation and speed, they also created concerns around runaway operating costs and unclear return on investment.

Companies Begin Introducing AI Spending Controls

The response is now shifting from aggressive expansion to controlled adoption.

Ride-sharing company Uber recently introduced spending controls for selected AI tools.

Under the updated structure, employees receive access under defined usage tiers beginning at $1,500 per month, while higher limits require additional approval.

The move followed comments earlier this year from Uber leadership indicating that the company exhausted its annual AI budget within only four months.

The change signals a broader transition across the industry: companies are still investing heavily in AI—but increasingly demanding measurable efficiency and tighter cost discipline.

A New Phase of the AI Economy

The early wave of AI adoption rewarded speed and experimentation.

Now, businesses appear to be entering a second phase—where controlling costs, selecting efficient models, and proving business value may become just as important as deploying the latest technology.

For startups especially, choosing between premium AI performance and long-term financial sustainability is quickly becoming one of the defining decisions of the AI era.

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