When OpenAI launched ChatGPT Pro, it positioned the $200-per-month premium subscription as an offering for power users. Yet, less than a year later, CEO Sam Altman revealed a surprising reality in a recent interview: the company is losing money on those subscriptions. “Insane thing: we are currently losing money on OpenAI Pro subscriptions! People use it much more than we expected,” Altman remarked.
This revelation underscores a critical oversight in one of the world’s fastest-growing tech companies. Despite OpenAI’s impact on artificial intelligence, its pricing strategy appears to have been driven by intuition rather than data. In the same interview, Altman admitted that the decision to price the standard Plus plan at $20 per month involved minimal testing. It seems the Pro plan’s pricing followed a similar approach.
Missteps like these are not unique to OpenAI. Pricing remains a complex challenge for many brands, especially in rapidly evolving industries like AI. But, with projected losses of $5 billion for 2024 and revenue of $3.7 billion, according to The New York Times, OpenAI’s case highlights the high stakes of getting it wrong. Effective pricing strategies require more than instinct – they rely on thoughtful market research and cost analysis to align with consumer expectations and business sustainability
OpenAI has seen remarkable growth, with ChatGPT reaching 300 million weekly active users and earning its reputation as the gold standard in AI chatbots. Yet, this success is overshadowed by significant financial strain. Steep operational costs—driven by massive computational demands, data centre investments, and energy consumption—have outpaced revenue, highlighting the perils of unsustainable pricing.
This situation underscores the importance of data-driven pricing strategies, especially for companies managing high-demand, high-cost products. OpenAI’s case shows that even the most innovative offerings can falter without a pricing model that accounts for operational realities and consumer behaviour.
The company’s decision to adopt flat pricing reveals the risks of intuition-led strategies. While the $20 Plus plan and $200 Pro plan aimed to simplify access, they overlooked critical factors like regional affordability and usage intensity. As a result, the Pro subscription, tailored for power users, costs more to maintain than it generates in revenue—a problem amplified by the strain on computational resources.
Other tech giants have also struggled with pricing missteps. Take MoviePass, for example. The company famously offered unlimited movie tickets for $9.95 per month, far below the actual cost of a single ticket in most markets. The model led to a rapid influx of users but proved financially unsustainable, ultimately causing the company’s collapse. Similarly, Uber’s early ride-share pricing strategies ignored the long-term costs of driver incentives, leading to billions in losses as it fought to compete with rivals like Lyft.
Even in retail, companies have stumbled. JCPenney’s decision to eliminate sales and discounts in favour of “everyday low pricing” alienated loyal customers accustomed to frequent promotions. The misstep resulted in a significant revenue decline and a tarnished brand reputation.
For OpenAI, projected losses of $5 billion against $3.7 billion in revenue further emphasise the high stakes of getting pricing wrong. Without adjustments, ChatGPT’s unsustainable operational costs could undermine its long-term viability.
The lesson is clear: groundbreaking products, whether in AI, entertainment, or retail, can become financial liabilities without data-driven pricing strategies. Guesswork might deliver short-term gains but often leads to long-term instability. To thrive, businesses must align pricing with consumer behaviour, regional realities, and operational costs—a task best accomplished through rigorous market research.
Fixed global pricing, such as the $20 ChatGPT Plus subscription, simplifies user acquisition but risks alienating users in lower-income regions where affordability varies. Tailored regional pricing could address these disparities, improving conversion rates and expanding the paying user base.
Additionally, OpenAI’s freemium model achieves a conversion rate of 5-6%, driving most of its revenue from subscriptions. However, sustaining growth in these figures demands deeper insights into user behaviour. For example, which features encourage free users to convert? How do price thresholds differ for professional versus casual users? Robust market research could answer these questions, offering pathways to refine pricing and expand the paying user base.
OpenAI’s pricing challenges stem from a lack of market research. Methods like Gabor-Granger and Van Westendorp’s price sensitivity meter could have revealed the ‘sweet spot’ for balancing affordability and profitability.
By digging deeper into what users value, OpenAI could have tailored its tiers to appeal to different needs—without alienating heavy users or underserving casual ones. By leveraging these insights, OpenAI could have introduced pricing tiers that balanced accessibility and profitability across diverse user groups.
Market Research as the Key to Conversion
For OpenAI, converting free users to paid plans is both an opportunity and a challenge. With 5-6% of users upgrading, market research could uncover which features—affordability, advanced tools, or seamless access—drive these decisions. Techniques like conjoint analysis and A/B testing would provide valuable insights to align pricing and features with user needs, ensuring plans resonate with both casual and professional users.
High operational costs, such as data centre investments and energy consumption, drive OpenAI’s losses. Market research could have forecasted usage patterns to align pricing with demand, mitigating the strain of offering unlimited access to power users.
Testing pricing scenarios before launching the Plus and Pro tiers could have revealed acceptable price points, feature preferences, and perceived value through A/B testing and consumer feedback.
Market research could have offered OpenAI critical insights to refine its global pricing strategy, aligning with regional purchasing power and user expectations. Techniques like Van Westendorp’s price sensitivity meter could have revealed pricing thresholds that resonate across diverse markets, striking a balance between accessibility and profitability.
Equally important is understanding the freemium user journey. Data-driven approaches like conjoint analysis would identify the features that drive free users to upgrade. Armed with these insights, OpenAI could have crafted subscription tiers that resonate with specific user segments, boosting conversion rates and ensuring sustainable revenue growth.
The challenges OpenAI faces with ChatGPT Pro’s pricing underscore the critical need for robust market research to guide financial decisions. By leveraging proven research methodologies, the company could have addressed key issues that now contribute to its financial strain.
Market research would have enabled OpenAI to segment its user base and assess each group’s willingness to pay for various subscription tiers. For instance:
By understanding these distinctions, OpenAI could have introduced tailored pricing options that cater to specific needs while ensuring profitability.
One of OpenAI’s greatest challenges is the high computational demand required to run ChatGPT. Market research could have helped forecast usage intensity across different user segments, providing critical data for pricing that aligns with operational costs. By factoring in expected usage patterns, OpenAI might have set higher prices or implemented limits for heavy users to balance the financial impact of intensive computational loads.
Before launching its Plus and Pro subscription models, OpenAI could have employed targeted market research to test pricing tiers and identify optimal price points. Techniques such as A/B testing would have allowed the company to evaluate real-world reactions to various pricing combinations, ensuring that the final structure resonated with users while covering costs.
Sam Altman’s recent suggestion of a potential shift to usage-based pricing reflects an acknowledgement that the current flat-rate subscription model may not be sustainable. Transitioning to a usage-based or hybrid pricing model could offer a path to profitability, but success depends on understanding user behaviour and pricing thresholds – a task ideally suited for market research.
Market research can help pinpoint where users find value in additional features or increased computational power, guiding the creation of scalable pricing. For instance:
Techniques like conjoint analysis could evaluate trade-offs users are willing to make, helping OpenAI determine the features that justify higher pricing.
Usage-based pricing introduces a challenge: ensuring accessibility for casual users while maintaining profitability from heavy users. Market research could map out demand curves, revealing usage patterns and helping establish fair thresholds. For example:
Through techniques like surveys and simulations, OpenAI could test user responses to proposed pricing structures, minimising backlash while maintaining equitable access.
Tiered pricing, informed by market research, could provide flexibility for different user segments without alienating any group. For example:
Each tier could be tested through pilot programs or focus groups to assess demand and fine-tune features and pricing. Techniques like Gabor-Granger or Van Westendorp could ensure each tier aligns with user expectations and willingness to pay.
By integrating market research into its pricing strategy, OpenAI could shift from a one-size-fits-all model to a flexible system that reflects user needs and operational realities. Whether adopting usage-based, tiered, or hybrid pricing, the goal remains the same: aligning value with cost to create a sustainable and scalable model that works for both users and the company.
OpenAI’s pricing missteps provide a powerful case study on the critical importance of data-driven decision-making in today’s complex and competitive markets. Despite its innovations and rapid user growth, OpenAI’s reliance on intuition over data has caused financial strain. The lesson for leaders across industries is clear: structured analysis is essential.
First, pricing is not just about setting numbers—it is a strategic lever that impacts profitability, accessibility, and user satisfaction. Companies must move beyond assumptions or limited testing and instead leverage robust market research to understand consumer behaviour, willingness to pay, and regional dynamics. Techniques such as the Gabor-Granger and Van Westendorp methods offer precise data on pricing thresholds, while conjoint analysis and A/B testing can uncover which features users value most.
Second, market research is not a one-time activity. Regularly revisiting pricing strategies is essential to stay aligned with evolving consumer preferences, market conditions, and operational realities. As OpenAI’s case demonstrates, even the most innovative offerings can become unsustainable if they fail to account for high operational costs or diverse user needs. Tools like usage-based or tiered pricing models, informed by ongoing research, can create equitable solutions for both light and heavy users.
Third, the freemium model is both an opportunity and a challenge. OpenAI’s 5-6% conversion rate is a testament to the potential of free-to-paid upgrades, but sustaining and growing these figures requires deeper insights into user behaviour. Understanding what drives conversions—whether it’s affordability, premium features, or seamless access—is key to designing subscription tiers that resonate with different segments.
Finally, visionary leadership is strengthened by structured decision-making. While intuition and bold moves often define industry leaders, the best outcomes are achieved when those instincts are paired with disciplined analysis. Investing in the right tools, teams, and methodologies for market research ensures that every decision is grounded in actionable insights.
OpenAI’s experience underscores that pricing is not merely a financial consideration—it’s a strategic cornerstone of long-term success. For business leaders navigating similar challenges, the takeaway is clear: in an increasingly complex market, thriving requires more than innovation; it demands a commitment to data-driven strategies that align user expectations with business realities.