The AI Economy Paradox: When Cheap Intelligence Costs More

Forbes

14-04-2025

Playful AI bots interact with villagers.
Economic tension is building in the world of AI development, and it's reshaping the relationship between developers, AI providers, and the very tools we use.
When OpenAI's ChatGPT and Microsoft's GitHub Copilot established the $20/month subscription benchmark, they inadvertently created what has become the market's psychological anchor for AI tool pricing. This price point made sense for the early generations of AI assistants—those with limited context windows, occasional utility, and without sophisticated tool use.
These models provided real value, but their capabilities had clear boundaries. They were helpful for simple code completions, basic content generation, and answering straightforward questions. The economics worked: the cost to serve these models aligned reasonably well with what users were willing to pay.
Fast forward to today, and the economic dynamics have fundamentally shifted. The latest generation of models—Claude 3.7, Gemini 2.5 Pro, OpenAI's Deep Research models, and others—have undergone a dramatic evolution. They can use tools intelligently, pull in comprehensive context, and solve complex problems with impressive accuracy. They're exponentially more useful than their predecessors—and exponentially more expensive to run.
A critical part of this evolution has been reliability. Early AI systems had high hallucination rates, which severely limited their practical utility in work-related processes where accuracy is essential. The real productivity gains have come with today's premium systems that incorporate sophisticated error-reduction mechanisms—models like OpenAI's o1-pro which runs parallel processes to self-validate, or their Deep Research model which leverages web search to reduce hallucinations, or my company's use of deep code analysis to improve AI coding agents.
As an industry insider, I can tell you that by paying $200/month for OpenAI's Pro, I'm saving thousands over paying for their $20/month subscription. The economics make perfect sense when you consider that I use it for specialized knowledge where traditional expert advice would cost me at least $500/hour, and I get answers in minutes rather than days.
Advanced AI capabilities deliver tremendous value, far exceeding their sticker price. Now, not everyone is a company's CEO, so there has to be a happy medium, an opportunity to get real, practical value at prices that are comparable to what we are used to paying for software as a service.
We are used to thinking that the cost of intelligence is dropping exponentially (apples to apples), and it's true. Due to better hardware, model distillation, and other techniques, we are at a point where, approximately every six months, the price per token halves, and the user expectations for what $20 should buy have followed this trend.
But what might seem like an incremental increase in intelligence to a bystander sometimes requires a step-function increase in computational price. For example, OpenAI's o1 reasoning model costs $60 per million output tokens, while o1-pro, their most expensive offering, costs $600 per million output tokens.
The biggest trend in AI in 2025 is agentic systems, which have their own cost multipliers built in. Let's break this down:
More context means more information about the problem and higher chances of finding the answer. All of this requires more tokens and more compute. The most advanced models now offer massive context windows—Gemini 2.5 Pro has a 1 million token context window, while Claude models offer up to 200K tokens. This dramatically increases their utility but also their computational costs.
Tool use is one of the first signs of intelligence, as tools are "force multipliers". In the last 6 months, we have seen rapid and continuous progress in AI agents' abilities to utilize tools (like web search, code execution, data analysis, various integrations). This makes the agents significantly more capable, but almost every time a tool finishes, the entire context, plus the tool result, must be reprocessed by the model, multiplying the costs. In coding, for example, it's normal for our AI agents to run multiple tools while working on a single request from you: it could run a tool to find the right files, a tool to get additional context, and a tool to edit files.
The more capable a model becomes, the more users rely on it, creating a feedback loop of increasing demand. For example, as I switched the majority of my web searches from Google to my AI assistants, that has significantly upped my daily use of those tools. As coding agents become more powerful, we see developers using them nonstop for hours instead of occasionally.
So when the aggregate costs jump 10-100x due to tools use, expanded context, and growing usage, even rapid technological improvements can't close the cost-to-price gap immediately. We are observing a true Jevons paradox, where the reduced costs of a certain resource (in this case intelligence) drives a jump in the use of that resource that's superceding the cost reduction. For example, while Chat GPT Pro costs $200/month (10x of the base paid subscription), Sam Altman himself acknowledged they're "losing money on OpenAI Pro subscriptions" because "people use it much more than we expected."
So if $200/mo Pro subscription is a bargain, why aren't you hearing about more businesses adopting it? One aspect that complicates this economic tension is the difficulty in evaluating AI capabilities. Unlike traditional software, where features can be clearly identified as present or missing, the differences between AI models are often subtle and situational. To a casual observer, the difference between o1 and o1-pro might not be immediately apparent, yet the performance gap in business tasks can be substantial.
This evaluation challenge creates market inefficiencies where users struggle to determine which price tier actually delivers the value they need. Without clear, reliable ways to measure AI performance for their specific use cases, many default to either the cheapest option or make decisions based on brand rather than actual capability.
This economic reality has led to what I'm seeing across the industry: AI providers artificially capping their models' capabilities to maintain sustainable economics at the $20 price point. I recently experienced this firsthand with Raycast Pro, which offers "advanced AI" access to Claude 3.7, but significantly caps the model compared to Claude's desktop application. Same model, drastically different results.
The difference lies in how these services implement restrictions. Raycast appears to limit web search capabilities to a couple of queries, while Claude Desktop allows more extensive searching to build better contextual understanding. The result is the same underlying model delivering vastly different intelligence.
The economic pressures facing AI providers are leading to difficult decisions that sometimes alienate users. We're seeing this play out in communities like Reddit, where loyal users express frustration when companies change their pricing models or capability tiers.
For example, in a popular Reddit post titled "Cursor's Path from Hero to Zero: Why I'm Canceling," a user detailed how a once-beloved AI coding tool deteriorated in quality while maintaining the same price point. The post resonated with many developers who felt the company had sacrificed quality, choosing to artificially cap capabilities rather than adjusting their pricing model to reflect true costs.
Many users are caught in a catch-22 where they aren't getting a lot of value, so they aren't paying a lot, so they are using underpowered solutions, so they aren't getting a lot of value. The industry stands at a crossroads. One path leads to more realistic pricing that reflects the true cost and value of these advanced systems. Based on my market analysis, $40-$60 is enough to deliver next-generation intelligence that people can use for 1hr+/day for the mass market. It's not going to cover 8hr of continuous AI use, or blasting 100 parallel AI agents to see which one is slightly better, but most people don't need AI at that level.
What's particularly interesting is that in mature enterprise software markets, paying hundreds of dollars per month for productivity tools is standard practice. Consider that Salesforce subscription costs $165-$300 per user per month, and companies routinely "stack" sales productivity solutions, adding tools like Outreach, Gong, Clari, and Dialpad on top of that base investment. Yet when it comes to AI—arguably the most transformative productivity technology of our time, and costlier on the compute side, there's a peculiar hesitation to venture beyond the $20 price point.
This has resulted in artificial capping of capabilities to maintain the now-standard $20 price point. This approach risks frustrating power users while potentially stymying innovation in what these systems can accomplish.
For the individual developer or business, the calculation should ultimately be about value, not price. If an AI tool saves you thousands of dollars and countless hours, even a $200/month price tag represents an incredible ROI. As the industry matures, we'll likely see more realistic pricing models emerge that better reflect both the costs of providing these services and the value they deliver. The most successful companies will be those that can clearly articulate and demonstrate this value proposition.
The $20 benchmark served its purpose in bringing AI to the masses. But as these tools evolve from occasional helpers to indispensable partners in our creative and professional lives, their economic models will necessarily evolve as well. Market makers like OpenAI have the biggest influence on how this economic tension is resolved. If they can successfully introduce moderately priced plans with appropriate capabilities—finding that sweet spot between the current $20 standard and the premium $200+ tier—they could help educate the market on the true value of advanced AI.
Mass adoption requires prices that feel accessible, even if the underlying value far exceeds the cost. The tension between AI capabilities, user expectations, and economic realities will define the next chapter of our industry. As AI tools continue their remarkable evolution, we may need to evolve our expectations about their cost as well.
For now, users should evaluate AI tools based on the outcomes they enable, not merely their price tags. And providers should continue seeking that elusive balance: fair compensation for the incredible value they provide, while making these transformative technologies broadly accessible.
Andrew Filev is the CEO and founder of Zencoder, a company that helps developers automate code testing and creation through AI agents. His previous company, Wrike, was acquired for $2.25 billion.